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Survey of 

Industrial Manipulation 

Technologies for 

Autonomous Assembly Applications 

NIST-Internal Report #7844 

Roger Bostelman, Joe Falco 

Intelligent Systems Division 

National Institute of Standards and Technology 

Gaithersburg, MD 20899 

roger.bostelman, joe.falco @nist.gov 

February 29, 2012 

Disclaimer: NIST does not endorse products discussed within this report nor manufacturers of these products. Products 

mentioned are for information purposes only and are not expressed as an endorsement for them or their manufacturer.
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Table of Contents 

Executive Summary ............................................................................................................ 3

1. Introduction ................................................................................................................. 4

a. Background .......................................................................................................... 4

b. End Effectors for Assembly ................................................................................. 5

c. Survey Objective .................................................................................................. 7

d. NIST Mobility and Manipulation Project ............................................................ 7

2. Current Robotic Assembly Systems ........................................................................... 8

a. Gripper Types ...................................................................................................... 9

i. Class I – End-effectors with fixed shape fingers .......................................... 9

ii. Class II - Special purpose end-effectors ..................................................... 11

iii. Class III - Multipurpose end-effectors ....................................................... 14

b. End-Effector Sensing ......................................................................................... 16

i. Tactile ......................................................................................................... 16

ii. Proximity .................................................................................................... 18

iii. Force/Torque .............................................................................................. 19

iv. Vision ......................................................................................................... 20

c. Generic Robot System Assembly Tasks ............................................................ 22

i. Insertion ...................................................................................................... 22

ii. Bin Picking ................................................................................................. 24

d. Specific Robot System Application Examples .................................................. 25

3. Advances in Robotic Assembly ................................................................................ 26

a. Systems .............................................................................................................. 26

b. Research ............................................................................................................. 28

i. Touch .......................................................................................................... 28

ii. Force ........................................................................................................... 29

iii. Vision ......................................................................................................... 30

iv. Threaded Insertion ...................................................................................... 30

v. Bin Picking ................................................................................................. 31

vi. Control ........................................................................................................ 31

vii. U.S. Government Research ........................................................................ 33

c. Patents ................................................................................................................ 35

4. Summary ................................................................................................................... 37

5. Recommendations ..................................................................................................... 38

6. References ................................................................................................................. 41

7. Appendix ................................................................................................................... 47

a. Terminology ....................................................................................................... 47

b. Industrial Robot System Standards .................................................................... 57

c. Design for Assembly – Boothroyd-Dewhurst Method ...................................... 59

d. Types of Force Control Functions ..................................................................... 60
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Executive Summary 

Robots are often perceived as fast, precision machines for replacing humans. However, equating the abilities of 

robots and human beings is risky business. A task that appears easy for a human assembler can be difficult or 

even impossible for a robot. In today’s manufacturing environments, to ensure success with robotic assembly, 

engineers must adapt their parts, products, and processes to the unique requirements of the robot. 

This document provides an overview of the current state of manipulation systems, as well as insight into future 

manipulation systems through discussion of research being performed in the field. The objective of this survey is 

to educate the manufacturing community about industrial manipulation capabilities, advancements, and research 

while focusing on autonomous assembly applications. The paper addresses advanced dexterous manipulation by 

surveying the state-of-the-art in end-effector and manipulation capabilities for autonomous assembly tasks. 

Humans can learn complex assembly tasks with relative ease compared to a robotic learning system. While 

today’s robots are precise and capable of achieving repeatable positions to within a few thousandths of an inch 

over thousands of hours of operation, dimensions and surfaces can vary between parts. If these tolerances 

between like parts are significant, a conventional robotic system using a position-based robotic control strategy 

based on nominal part dimensions will be ineffective as an assembly tool in cases where the assembly tolerance is 

less than the positional uncertainty of the robot. In these cases, end-effector sensing and an adaptive control 

scheme must be implemented in order to meet the requirements of the assembly operation. 

Results of this survey show that dexterity, perception, and force control of robot systems are advancing as 

exemplified by the availability of multi-fingered and tactile grippers, random part identification and localizing 

capabilities, and research aimed at advanced tasks such as assembling parts while in motion. Advanced military 

programs are underway to improve the state of science for robot dexterity and capability. Safety and performance 

standards are beginning to consider collaboration among humans and robots within the same workspaces. On the 

manufacturing forefront, advances in highly capable assembly systems are minimal and recent gripper patents are 

mainly special purpose. Following is a set of generic recommendations for industry actions and new technologies 

towards the development of more capable robotic systems: 

Actions: 

• Development of performance measures for assembly 

• Increased use of robots for ‘intelligent’ fixturing 

• Development of methodologies for human/robot interaction 

• Integration of current industrial grippers with sensors 

• Development of autonomous robot systems capabilities that compare to humans 

• Definition of clear, standard interfaces for grippers 

• Use of robot hand guiding through the teaching process 

• Development of path planning for two arm robots 

• Use of external metrology to support robot system applications 

• Verification of perception systems combined with low cost gripper tactile sensors 

• Improvement of force control for assembly processes 

Technologies: 

• Adaptive end-effectors 

• More compact end-effectors and end-of-arm sensing 

• Tactile sensing for low cost pneumatic grippers 

• Robots on vehicles 

• Dynamic robot work volumes 

• Tactile based response for robots 
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• Collaborative robots 

1. Introduction 

a. Background 

“The logical evolution of the assembly line would seem to lead to one that is fully automated. Such an automated 

system would ideally imply the elimination of the human element and its replacement with automatic controls that 

guarantee a level of accuracy and quality that is beyond human skills. In the 1980s, Japanese and Italian 

automobile manufacturers so successfully automated their assembly lines that certain of their factories consisted 

almost entirely of robots regularly doing their jobs. On the other hand, General Motors found that robots could not 

replace human workers and had to retrench from technology and focus on retraining workers.” [Bruno, 2011] 

[Genaldy, 1990] et. al., compared human and robot performances for a simple 

assembly operation through a time and cost study finding that a pair of robots 

performing the same simple task were slower than the human. Despite the 

significant research that has been performed in robotic manipulation and 

autonomous assembly, it is evident that the technology has a long way to go to 

match the assembly capabilities of, for example a 16th century mechanical 

watchmaker including his/her: dexterity, sensitivity, accuracy, and compliance. 

(Watchmaker photo copyright approved: Igor Gratzer/Shutterstock) However, 

great strides towards this capability have been made for at least larger 

automotive part assembly as stated in [Picard, 2002] with watchmaker precision. Their FlexPlace system achieved 

sub-millimeter accuracy and did away with traditional heavy and expensive tooling by using improved robot path 

adjustment through learning, part modeling/matching, and a set of sensors mounted on the robot gripper. 

The goal of this document is to provide an overview of the current state of manipulation systems as well as insight 

into future manipulation systems through discussion of research being performed in the field. This study is not 

exhaustive and instead an example of the current state of the art. Discussion will focus on the end effector 

(mainly robot grippers) and the combined end-effector/manipulator capabilities to perform autonomous assembly 

tasks. The need for agile manufacturing is moving the manufacturing industry towards more flexible and 

complex assembly operations involving, for example: 

 multiple manipulators providing flexible part manipulation and fixturing; 

 assembling parts while the manipulator and/or the parts are in motion; 

 and human/robot collaborative assembly where humans and robots work “hand-in-hand” to assemble a 

common and complex system of parts. 

Manipulators can have varied end-effectors ranging from basic parallel gripping of a particular part to complex 

dexterous hands capable of grasping parts of various geometries. Gripping/grasping and manipulation combined 

with perception of the part, manipulator, and environment further complicates the potential tool set that may be 

required for generic autonomous assembly across the variety of manufacturing applications including for 

example: automotive, aircraft, distribution, and food-processing. 

Selecting an end-effector to perform a manipulation task requires an analysis of the parts to be grasped. In the 

simplest case, as in many industrial robot applications, only a few parts must be handled through a predefined set 

of tasks. In the case of a known part and known grip, an end-effector system must be defined to grasp and 

maintain control of a part through the various manufacturing tasks. Known part – known grip applications use 

fixturing to constrain parts in known locations. In another case, called known part, the parts are known, but there 

is variation in part pickup orientations (such as in random bin picking applications) and a grasping algorithm must 

be applied to determine the best grasp to use in order to access a part and maintain control. The last case, called 
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unknown part, is the least structured environment where the robotic system must both determine the identity of a 

part and its properties (e.g., shape, size, mass, position) and subsequently a valid grasp. The more unstructured 

the environment, the more advanced the supporting perception system must be. Ideally, but currently unrealistic, 

is a robotic manipulation system having the dexterity and perception of a human to work in an unstructured 

manufacturing environment. 

b. End Effectors for Assembly 

End-effector designs are quite diverse. The flexibility of end-effectors to handle a wide range of objects without 

manipulator mechanical reconfiguration leads towards the use of a universal robotic hand. These universal robot 

hands, developed to mimic multiple human grasp techniques, require very complex mechanisms and control 

algorithms. They are an extensive area of research, but to date no practical, commercially-available versions 

exist. More economical end-effectors, designed using a single grasp technique to handle a subset of objects of 

varying weight, shape, and material are often termed grippers. These grippers are typically designed with two 

fingers having single degree-of-freedom (DOF) using various kinematic mechanisms and specialized jaws for the 

prehension of a particular object or family of objects. Sometimes grippers may incorporate three single DOF 

fingers for grasping object geometry without the need for specialized jaws (e.g., objects with cylindrical features). 

Some end-effectors are designed for even more specialized prehension operations. A classification scheme for 

end-effectors [Nof, 1999] is outlined in section 2a Gripper Types with examples of each current end-effector 

type for use in robotic assembly. 

[Monkman, 2007] states that the choice of gripper depends mainly on the work it has to perform and that every 

prehension task is characterized by the following factors and requirements: 

 Technological requirements: prehension time, gripping path, time dependence of the prehension force, and the 

number of the object acquisitions per gripping cycle 

 Effects of the prehended objects: mass, design, dimensions, tolerances, position of the center of gravity, 

stability, surface, material, strength, and temperature 

 Factors related to handling equipment: positional accuracy, axial accelerations, and connection specifications 

(mechanical, electrical, fluidic, etc.) 

 Factors related to environmental parameters: process forces, feeding conditions and clamps, storage 

conditions, contaminations, humidity, and vibration 

[Monkman, 2007] also suggests that gripping procedures consist of part prehension and retention and can be 

divided into four phases: 

 Preparation for contact e.g., by appropriate orientation of objects following a predefined motional pattern. 

 Prehension by establishing contact between object and gripping surfaces. At this stage the work piece is 

subjected to static forces and moments. 

 Retention of the part during its manipulation in space or, in some cases, moving, rotating, or even (in rare 

cases) mounting. Dynamic forces and moments occur in the course of motion or task related procedures. 

 Release of the part at its destination, e.g., by switching-off the vacuum supply and possibly using the 

assistance of an integrated ejection mechanism. 

How well a part is secured in a gripping process depends on the number of degrees of freedom allowable 

following prehension. Figure 1, modeled by the National Institute of Standards and Technology (NIST), shows 

Monkman’s typical two-point prehension on various parts and the corresponding remaining degrees of freedom. 
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Figure 1 – Two point prehension of parts showing the degrees of freedom F = 0 to F = 5. 

(1 gripper jaw, 2 part) 

Monkman says that a part is held by force matching (with frictional or gravitational forces equal to the gripping 

force). Clamping forces from pinching grippers cannot be arbitrarily increased. The upper limit force is dictated 

by the allowable surface pressure, depending in turn on the contact force and the coefficients of elasticity of the 

gripper jaw and part material. Imperfections in gripper finger form can lead to poor surface contact with the part. 

This becomes particularly apparent when long fingered grippers are used. The design of the gripper system is 

influenced significantly by the forces necessary to ensure reliable prehension of the part. However, the required 

gripping force depends on many factors which can be only partially estimated. Some of these factors are: 

 Spatial settings: arrangement of the gripper relative to the industrial robot and its movable axes 

 Resultant force: vector sum of all single acting forces resulting from mass, inertia, Coriolis, and centrifugal 

forces – all of which may change with robot movement 

 Geometry of the part and prehension points 

The ultimate part retention stability is achieved by maximum matching of the gripper and part profiles. Jointed 

grasping mechanisms also make it possible to compensate for irregular part shapes and to correct for position 

deviations. Sensitive/delicate parts should be handled with shape matching having no appreciable impactive 

forces. 

The strategy for gripping a part can be: 

 Predetermined: The operations required to achieve a reliable grip at the corresponding contact points are preprogrammed 

 Variable: operations are only briefly defined and can be adaptively matched to the situation in accordance 

with the information supplied by sensors 

Impactive gripping (impact of jaws against part surfaces) requires the motion of solid jaws in order to produce the 

necessary grasping force. Ingressive gripping results in surface deformation or even penetration (intrusive) of the 

surface down to some predefined depth (force-shape mating). Contigutive prehension implies a direct contact to 

facilitate gripping. Examples include chemical and thermal adhesion. Astrictive methods are based on binding 

forces between surfaces. Magnetic and electrostatic adhesion and vacuum suction can lift most objects even 

without direct initial contact. Adaptive grippers possess either integrated or external monitoring sensors which 

imply the need for specific data processing techniques. Table 1, repeated here from [Iberall, 1997] for simplicity 

to the reader, provides a good representation of gripping methods and their non-penetrating and penetrating 

designs. 
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Table 1 – Gripping Method with non-penetrating and penetrating designs [Iberall, 1997] 

Gripping Method Non-penetrating Penetrating 

Impactive Clamping jaws, chucks, collets Pincers, pinch mechanisms 

Ingressive Brush elements, hooks, hook and 

loop (Velcro) 

Needles, pins, hackles 

Contigutive Chemical adhesion (glues), surface 

tension forces 

Thermal adhesion 

Astrictive Electrostatic adhesion Magnetic grippers, vacuum suction 

c. Survey Objective 

The objective of this survey is to educate the manufacturing community about industrial manipulation 

capabilities, advancements, and research focused on autonomous assembly applications. The paper addresses 

advanced dexterous manipulation by surveying the state-of-the-art in end-effector and manipulation capabilities 

for autonomous assembly tasks. 

d. NIST Mobility and Manipulation Project 

The National Institute of Standards and Technology’s (NIST) has been researching measurement science and 

standards for automated manufacturing since the 1980’s, beginning with the Automated Manufacturing Research 

Facility (AMRF). [Zenzen, 2001] Recently, NIST began the Measurement Science for Intelligent Manufacturing 

Robotics and Automation (MSIMRA) Program which seeks new measurement science to gauge the performance 

of intelligent automation systems in a variety of areas, from perceiving movement in their vicinity to meeting 

production goals to protecting nearby humans. Such flexible systems will improve safety and make it easier and 

quicker to build new products. Within this NIST program is the NIST Mobility and Manipulation Performance 

Measurements and Standards (MMPMS) Project [MMPMS, 2011] which is tasked with researching measurement 

science for advanced, dexterous robot manipulation for collaborative human/robot assembly operations. An 

example of human/robot collaboration is a robot grasping a heavy part for fastening to an assembly, while a 

human performs the more dexterous operation of bolting the part into place. 

Advancing industrial manipulation systems towards autonomous assembly applications has been requested by 

robot manufacturers to improve the capabilities of these systems and by automobile manufacturers to apply these 

improved systems. Initially, the project must understand the state of the science and uncover gaps in research, 

technology implementation, and standards. 

The MMPMS project has designed, developed, and implemented a flexible robot testbed at NIST as shown in 

Figure 2. The testbed includes an industrial robot, currently configured underslung, though the robot could also 

be mounted upright or on a pedestal. A NIST-developed automated guided vehicle (AGV) is also included in the 

testbed, along with conveyers, mannequins, test equipment, sensors, and computer systems. A variety of 

measurement systems are used in the testbed; for example, high accuracy laser tracker with metrology targets, 

ceiling-mounted barcodes and stereo camera, and wall-mounted reflectors with spinning laser positioning. The 

testbed is currently being used, among other areas, to research: 

 automated guided vehicle safety to improve the American National Standards Institute/Industrial Truck 

Standards Development Foundation (ANSI/ITSDF) B56.5 safety standard [ANSI, 2011], 

 robot safety to improve the International Organization for Standardization (ISO) 10218 robot safety 

standards and develop the ISO TS 15066 technical specification of collaborative robots [ISO, 2011], 

 virtual and real pallet assembly to develop standardized test methods for robotic palletizing 

 perception control to develop the ASTM WK 31638 - pose measurement systems standard [ASTM, 2011] 
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Next, and based on the findings of this survey, the testbed will be used for developing new measurement science 

for parts assembly tasks and improving associated standards. 

Figure 2 – NIST Measurement Science for Intelligent Manufacturing Robotics and Automation (MSIMRA) 

Program Testbed. 

2. Current Robotic Assembly Systems 

Combinations of sensing and other devices add value to the robot and manufacturing assembly process, but also 

complicate the robotic system. Figure 3 shows a block diagram of several sensors and devices that could be 

added to a robot to provide additional safety and functionality, including: tool changing, compliance, force and 

torque measurement, structured light, and other sensors. 

Figure 3 – Block diagram of a robot end-effector showing several sensors and devices that could be attached to a 

robot to provide additional functionality. 
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Tool changers are used by robot users to allow a single robot to accomplish more tasks by automatically changing 

end-effectors or other peripheral tooling. Recent advancements include internal channeling to allow cabling and 

pneumatics to pass through the unit adapting to new hollow robot wrists. Robotic collision sensors (breakaway 

devices) prevent damage to robotic end-effectors resulting from robot crashes. Their features can include: 

automatic reset, high repeatability, large moment rotation, rugged design, and low cost. Compliance devices 

allow a robot to compensate for positioning errors due to machine inaccuracy, vibration, or tolerance, thereby 

lowering contact forces and avoiding part and tool damage. Examples of these three devices are shown in Figure 

4. 

Figure 4 – Robot tool changer (left), collision sensor (middle), and compliance device (right) (www. ati-ia.com) 

[photo-use permission granted by ATI-IA]

a. Gripper Types 

i. Class I – End-effectors with fixed shape fingers 

Class I end-effectors used for assembly, namely grippers, shown in Table 2, can manipulate and hold a variety of 

parts, including: round, square, and flexible (hoses, cables – conformable parts). The gripper is designed to ensure 

that the part it holds cannot translate or rotate with respect to the gripper (i.e., constrained in all six degrees of 

freedom). Grippers typically use friction, physical constraint, attraction, or support to hold parts. Class I grippers 

are primarily friction or physical constraint types with soft, replaceable material between the gripper jaw and the 

part. 

Class I special purpose grippers, such as grippers with replaceable jaws, are specific to the part being carried and 

manipulated. A variety of special purpose grippers, as exemplified in Table 2, are designed to accurately pick up 

single or multiple parts. 

Table 2 – Class 1 Gripper Examples 

Gripper Class Example Brief Description 

 

Two fingers - Revolute (angular) and 

translational pairs: 

(left) Rotational or angular and (right) 

translational (www.schunk.com) two fingered 

grippers. The translational gripper shows the 

universal adapter (bottom tabs) ready for 

attaching custom fingers as in the extended jaw 

case that follows. 

[photo-use permission granted by Schunk]
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Two Fingers - Extended jaws:

Translational two fingered gripper with 

extended jaws for grasping square or round 

parts. (www.schunk.com) 

[photo-use permission granted by Schunk]

Three fingers - Grasp at three points:

Three fingered concentric, long stroke, beltdrive gripper (www.schunk.com) 

[photo-use permission granted by Schunk]

Spherical objects of differing size maintaining 

center: 

Three fingered gripper (www.robotiq.com) 

[photo-use permission granted by Robotiq]

 

Special purpose grippers: 

(top left) angular gripper with hygienic design 

and (top right) stacking gripper, both for the 

food processing industry (www.schunk.com) ; 

(bottom left) bagged product gripper and 

(bottom right) universal warehousing gripper 

that can handle bags, bundles, cases, etc. 

(www.fanucrobotics.com) 

[photo-use permission granted by Schunk and 

Fanuc Robotics]
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ii. Class II - Special purpose end-effectors 

Table 3 shows examples of Class 2 special purpose end-effectors and provides a brief description of each. There 

is a huge variety of suction cup style vacuum grippers. Shapes include oval and round and sizes can range from 

12.7 mm (0.5 in) up to 101.6 mm (4 in) in diameter. They can be made of Viton fluoroelastomer, silicone, other 

rubbers, or polyurethane. Picking the right cup material, size, and shape requires experience, vendor guidance, 

and sometimes trial and error. Even with all of the variety available in cups, they won't work in every application. 

Sometimes holes in the part preclude use of vacuum cups. Cups also can cause cosmetic problems on Class A 

surfaces - freeform surfaces of high efficiency and quality. Rough surfaces also may not work well with cups. In 

any of these cases, alternative ways to secure parts are needed. 

Vacuum cups typically engage in one of three ways: at the front or side of the part or at an angle. For side 

engagements, short-stroke pneumatic cylinders advance the cups to grip the part after the vacuum cup frame 

moves into position. For quick pickup and placement of parts or for fast cycles, spring arms allow the cups to be 

in place before ejection and then accept the part like a mitt accepts a baseball. Hex-shaped gripper-arm profiles 

will keep grippers from turning when their orientation to the part must remain fixed. [EOAT, 1997] 

Electro and permanent magnet devices are used to pick up and release ferromagnetic parts. Passive magnets are 

typically used as an element with other class II gripper technologies. Electromagnet type grippers gain an 

attractive force only when current is applied to the unit. 

Radiant energy joining processes include electron beam and laser welding which focus an energy beam on the 

work pieces to be joined. We focus on laser welding or joining as it is more advanced and has many advantages 

over electron beam welding. Below is an example list of advantages of laser beam welding where a complete list 

is shown in [Sivam, 2004]: 

• Welds can be made inside transparent glass or plastic housings. 

• A wide variety of materials can be welded, including some formerly considered as unweldable 

combinations. 

• As no electrode is used, electrode contamination or high electric current effects are eliminated. 

• Areas not readily accessible can also be welded. 

• It permits welding of small, closely spaced components with welds as small as a few microns in diameter. 

• Unlike electron beam welding, it operates in air and no vacuum is required. 

• Since the laser beam is highly concentrated and narrowly defined, it produces a narrow size for the heat 

affected zone. 

• Because it is light, it is clean and no vaporized metal or electrodes dirty up the delicate assemblies. 
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Spray adhesive has many temporary and permanent assembly applications including, for example: application of 

die lube, mold release, and glue for metal and plastic parts, spraying release agent on screens and drums in wood 

building panel manufacturing, applying adhesive to packaging materials to prevent slippage on pallets, applying 

adhesive on tire treads. 

Robotic deburring tools are robotic end-effectors for removing burrs, flashing, and other unwanted edge 

properties caused by cutting or machining and are often used as an integral part of the assembly process. There are 

several basic types of deburring end-effectors. Radially-compliant robotic deburring tools have a rigid outer 

housing and internal motor/spindle assembly that mounts on a pivot bearing. Pistons in a chamber near the front 

of the housing supply a constant flow of air pressure to a rotating shaft. A rotary cutting burr or file rides on a 

cushion of air that provides a reliable field of compliance while maintaining a constant force and spinning at high 

speeds. This field of compliance is exerted in the radial direction, providing a high degree of stiffness in the path 

direction and a low degree of stiffness in the contact direction. Often, a floating head is used to compensate for 

variances in robot path or part position. Axially-compliant robotic deburring tools are also available. These 

devices exert a constant axial force on a deburring head mounted to a free flying piston (FFP). The movement of 

these high-torque tools compensates for changes in part tolerances, part misalignment, and robot path variation. 

Table 3 – Class 2 Gripper Examples 

Gripper Class Example Brief Description 

Vacuum gripper: 

(top) supporting and placing a box on a conveyer 

at NIST. This gripper was custom designed from 

a series of pneumatic components and cups 

similar to those shown (bottom) 

(www.anver.com) 

[photo-use permission granted by Anver] 
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Electromagnetic gripper: 

Magnetic gripper handling piston rods (Kawasaki 

Robotics - 

http://www.youtube.com/watch?v=eGPne8_sR4c) 

Welding heads:

Intelligent laser welding head (www.servorobot.com) 

[photo-use permission granted by ServoRobot] 

Machining Spindles: 

A product line of robotic deburring end effectors. 

(http://www.atiia.com/products/deburr/deburring_home.aspx) 

[photo-use permission granted by ATI-IA]
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Sprayers:

Robot spraying adhesive to mount headlights in 

automobiles during manufacturing. 

(www.kuka.com) 

[photo-use permission granted by Kuka]

iii. Class III - Multipurpose end-effectors 

Table 4 shows examples of Class 3 special purpose end-effectors and provides a brief description of each. 

Advanced (or sometimes called ‘universal’) grippers offer flexibility to pick up a wide range of objects. Typical 

advanced grippers are at least three fingered and multi-jointed and are similar in shape and functionality to human 

hands. This design adds complexity, yet promotes flexibility towards prehension of parts with varying properties. 

[Mindtrans, 2010] provides a compilation of figures and descriptions of 22 anthropomorphic robot hands/arms, 

including prosthetic hands/arms that range in price from $6000 USD to more than $100 000 USD. Three of the 

22 described in [Mindtrans, 2010] are exemplified here. One of the hands described in this reference is the DLRHIT Hand II shown in Table 4 and developed by the Harbin Institute of Technology (HIT) and the German 

Aerospace Center (DLR). The DLR-HIT Hand II, has five modular fingers and each finger has four joints and 

three degrees of freedom. Altogether there are 15 motors in the finger body and palm. The hand is actuated by 

commercial flat brushless DC motors commutated by digital hall sensors. There is an absolute angle sensor and a 

strain-gauge based joint torque sensor at each joint. The high-speed, real-time communication bus is implemented 

by field programmable gate array (FPGA). The DLR-HIT Hand II is the further development of the DLR-HIT 

Hand I, also shown in [Mindtrans, 2010], which they claim has been successfully used in some research institutes 

in USA, Spain, Italy, Greece, Germany, and China. [DLR, 2011] 

The Delft hand/arm is shown in Table 4 and is quite different from the HIT or other anthropomorphic hands and 

arms from [Mindtrans, 2010]. This system includes a potentially low-power and safe manipulator similar to a 

classic desk lamp. In contrast with conventional factory robots, the Delft arm has a low mass and uses low-power 

motors to reduce its cost and be low in mass for additional safety. The design is based on the principle of static 

balancing, whereby adjustable springs compensate for the mass of the arm. Claimed specifications include: 

 masses are fully balanced using adjustable 

springs 

 4 degrees of freedom 

 4 low-power motors 

 4 rotary encoders 

 10 kg total mass 

 2 kg maximum payload 

The Delft hand, suggested in the reference, is designed to be a powerful, versatile, lightweight three-fingered, 

under-actuated gripper. The three fingers of the hands each have two degrees of freedom, all actuated by a single 

motor. Special mechanisms assure a powerful and robust grip on widely varying objects with a minimum of 

sensing and control. Claimed hand specifications include: 

 3 x 2 degrees of freedom 

 1 low-power motor 

 1 force sensor 

 0.6 kg total mass 

 8 kg maximum payload 
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The Robotics and Mechanisms Laboratory (AKA the RoMeLa Project) at the College of Engineering at Virginia 

Tech University has designed and built a prototype robotic hand that is controlled and operated by compressed 

air. Called RAPHaEL (Robotic Air Powered Hand with Elastic Ligaments), the robot can hold heavier, solid 

objects, as well as light or delicate ones such as a light bulb or an egg. The hand is powered by a compressor air 

tank at 414 kpa (60 psi) and an accordion-style tube actuator, with microcontroller commands operating and 

coordinating the movements of its fingers. It uses no other motors, and the strength of the grasp is controlled by a 

change in air pressure, making the hand quite dexterous. 

Table 4 also shows two advanced grippers that conform to the part they are grabbing, rather than being designed 

for particular parts. The “ball gripper,” a project supported by the Defense Advanced Research Projects Agency 

(DARPA) and including researchers from Cornell University, the University of Chicago, and iRobot Corporation, 

uses everyday ground coffee and a latex party balloon, bypassing traditional designs based on the human hand 

and fingers. An everyday party balloon filled with ground coffee -- any variety will do -- is attached to a robotic 

arm. The coffee-filled balloon presses down and deforms around the desired object, and then a vacuum sucks the 

air out of the balloon, solidifying its grip. When the vacuum is released, the balloon becomes soft again, and the 

gripper lets go. [Cornell, 2010] 

[Prahlad, 2011] presented a new approach, not shown, to gripping called electro-adhesion which uses electrostatic 

cohesion to lock onto objects and conformance to encompass an object to grasp and then lock the joints in order 

to trap the object. This approach works best when large forces are not necessary to keep the object confined. 

Table 4 – Class 3 Gripper Examples 

Gripper Class Example Brief Description 

Robot hand:

DLR-HIT 5 finger hand 

[photo-use permission granted by 

DLR-Hit-Hand, Robotics and 

Mechatronics Center]

Robot hand:

Delft hand/arm (top) and hand (bottom) 

[photo-use permission granted by TU 

Delft; photo by David Joosten]
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Robot hand:

Pneumatic 5 fingered robot hand 

http://www.romela.org/main/Robots 

[photo-use permission granted by 

RoMeLa: Robotics & Mechanisms 

Laboratory, Virginia Tech] 

Advanced gripper: 

Ball geometry-conforming gripper 

(www.cornell.edu) 

[photo-use permission granted by 

Cornell University]

b. End-Effector Sensing 

[Brumson, 2011] says that robots can now perform tasks better because of advancements in sensors. 

“Applications are moving toward tactile force feedback and non-tactile sensing for positional and robotic 

guidance. Advancements in vision sensors make them less expensive and more powerful,” says Nicholas Hunt, 

Product Support Manager at ABB Inc. (Auburn Hills, Michigan) “Advancements include high accuracy of vision 

sensors such as laser scanners. Sensors are very affordable and robot manufacturers are able to incorporate those 

features and functions inside the robot to accommodate data.” 

Robots are no longer simply grabbing parts and forcefully moving them into fixtures, asserts Hunt. “With tactile 

feedback available and the processing power within robots, integrators now allow the process to control the 

robot's behavior, not the robot’s behavior controlling the process. Tactile feedback and vision systems with 

scanning lasers enable end-effectors to move on a micron level of accuracy.” 

i. Tactile 

Much research has been performed in the area of tactile sensing for robots and is nearing the capability for use in 

assembly. [Howe, 1993] provides references for robot hand tactile sensing and states that “transmission dynamics 

such as friction, backlash, compliance, and inertia make it difficult to accurately sense and control endpoint 

positions and forces based on actuator signals alone.” He also lists the common robot hand sensors as: dynamic 
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tactile sensor, tactile array sensor, finger tip force-torque sensor, and joint angle sensors. [Cutkosky, 1993] et. al. 

describes tactile and force and torque event driven dexterous manipulation with the events primarily determined 

through tactile and force/torque sensing. They breakdown a simple task (e.g., grasping a glass of water, lifting it 

and replacing it) that contains several events and discontinuities. Experiments with human subjects reveal that 

during such tasks people rely on a combination of fast- and slow-acting tactile sensors to detect such events as 

contact, the onset of motion, and the onset of slipping. Preliminary experiments with a simplified robotic hand 

have suggested that a combination of force sensors and dynamic tactile sensors can provide robots with a similar 

ability. 

Although there has been much tactile sensor research, we found that relatively few tactile (including spatial 

measurement and touch) sensors are being used in robotic assembly processes today, perhaps with the exception 

of sensors to track weld seams. [Kuka, 2011] With any deviations in the weld seam from the model, the sensor 

tracking the actual seam informs the robot to make error corrections in real time. Currently no general 

specification of a touch or tactile sensor exists. Crowder suggests that the following can be used as a basis for 

defining the desirable characteristics of a touch or tactile sensor suitable for the majority of industrial applications 

[Crowder, 1998]: 

• A touch sensor should ideally be a single-point contact, although the sensory area can be any size. In 

practice, an area of 1 mm2

 to 2 mm2 is considered a satisfactory compromise between the difficulty of 

fabricating a sub-miniature sensing element and the coarseness of a large sensing element. 

• The sensitivity of the touch sensor is dependent on a number of variables determined by the sensor's basic 

physical characteristic. In addition the sensitivity may also be the application, in particular any physical 

barrier between the sensor and the object. Sensitivity within the range 0.4 N to 10 N, together with an 

allowance for accidental mechanical overload, is considered satisfactory for most industrial applications. 

• A minimum sensor bandwidth of 100 Hz. 

• The sensor’s characteristics must be stable and repeatable with low hysteresis. A linear response is not 

absolutely necessary, as information processing techniques can be used to compensate for any moderate 

non-linearities. 

• As the touch sensor will be used in an industrial application, it will need to be robust and protected from 

environmental damage. 

• If a tactile array is being considered, the majority of applications can be undertaken by an array of 10 to 

20 sensors square with a spatial resolution of 1 mm to 2 mm. 

Commercial tactile sensors that are only recently being implemented are from [Pressure Profile, 2010]. They 

have developed the FingerTPS - Tactile Pressure Sensing for the Human Hand (see Figure 5 left) which uses 

capacitive-based pressure sensors to quantify forces applied by the human hand through finger gloves and a 

wireless data transfer system. Also for robot grippers, they have developed the RoboTouch Systems Pressure 

Profile System (see Figure 5 – right) which provides tactile feedback for robot grippers. The technology and 

company were born out of the Robotics Laboratory at Harvard University over a decade ago. Currently, three 

gripper and robot companies have implemented this tactile system. RoboTouch can include multiple sensing 

pads, each with 12 to 24 sensing elements with digital interfaces. These can be placed on the robot fingertips, 

grippers, and/or palm surfaces. 
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Figure 5 – Tactile sensors for the hand (left) or robot figures and/or palm (right) (www.pressureprofile.com) 

[photo-use permission granted by Pressure Profile]

Tactile sensors can complement visual systems by becoming the controlling system at the time contact is made 

between a gripper of the robot and an object or objects being gripped, a point when vision is often obscured. 

[Wikipedia (tactile sensors), 2010] 

ii. Proximity 

There are several non-contact sensing devices (proximity devices) typically used with robots to measure and 

provide the distance to objects to pick up or avoid. The types of sensors are [Wikipedia (proximity sensors), 

2011]: 

 Inductive 

 Capacitive 

 Capacitive displacement sensor 

 Eddy-current 

 Magnetic, including Magnetic 

proximity fuse 

 Photocell (reflective) 

 Light or Laser rangefinder 

 Sonar (active or passive) 

 Radar 

 Passive thermal infrared 

 Passive optical (such as chargecoupled devices) 

 Reflection of ionizing radiation 

Although not for assembly tasks, [Volpe, 1994] surveyed proximity sensors for use in manipulator collision 

avoidance. Five categories of sensors were considered for this use in space operations: intensity of reflection, 

triangulation, time-of-flight, capacitive, and inductive. From these categories, the most promising commercial and 

mature laboratory prototype sensors were triangulation, time-of-flight, and capacitance sensors. 

NIST developed a proximity sensing system embedded in robot gripper fingers to provide part presence and 

centering as well as gripper rotation information to the robot. Figure 6 shows pairs of infra-red proximity emitters 

and detectors embedded into machined plastic fingers. Four pairs of emitters and detectors were embedded in the 

finger pads. The sensors provide part presence and centering capability that can update robot position with 

respect to the part. Also, front and bottom pairs of emitters and detectors provide tool rotation with respect to 

parts trays. Left and right emitter and detector pairs sensing similarly reflective surfaces from the front sensors 

(fingers pointing down) and bottom sensors (fingers parallel to a tray) are input to the robot controller, compared 

to each other, and used to level the gripper with respect to the parts tray prior to picking up parts. A similar 

system used both infrared and ultrasonic sensors embedded in the robot gripper fingers. [Bostelman, 1989] 
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Figure 6 – Gripper fingers with pairs of infra-red proximity emitter and detectors embedded into machined plastic 

fingers (NIST). 

To date, most machine control applications have used inductive proximity switches. Based on the electromagnetic 

induction principle, these sensors are designed to detect metal targets and are generally insensitive to the effects of 

dirt. A major drawback, however, is the cost and quantity of cabling needed to connect each sensor to a factory’s 

control system. Commonly located on moving parts of machines, these types of sensors are prone to malfunction 

due to cable damage caused by wear and tear. ABB developed a wireless proximity switch, the WISA (Wireless 

Interface for Sensors and Actuators) protocol, and a power system that eliminates the need for cabling in 

proximity sensor applications. [ABB, 2005] Data is relayed from the switch to the machine control system using 

ABB’s WISA protocol, specifically designed for industrial applications. 

Most proximity sensors used in manufacturing are not attached to the robot, but rather to conveyers, support 

structures, or other areas. They are typically used to detect the presence of parts. The latest is high speed, colored 

parts detection as shown in Figure 7. Unlike vision sensors that are designed for pattern detection, contour 

verification, or edge location, full color photoelectric sensors are aimed only at a specific spot on the target that 

verifies that the right product or the desired attribute is present. This lets them operate at speeds as fast as 1 ms, 

which is much faster than the typical 20 ms update time required by vision sensors. [Draper, 2008] 

Figure 7 – Color photoelectric sensors detecting parts on a conveyer. 

[photo-use permission granted by Balluff]

iii. Force/Torque 

Force/torque sensors are used in adaptive control schemes for part assembly operations, constant force operations 

such as buffing, polishing, and deburring. They are also used in a passive mode to collect force data for lot 

testing and statistical process control (SPC). [Perry, 2002] Force/torque sensors can be in-line with the robot endeffector as shown in Figure 3, carried by the robot as in servo guns for welding operations, or measured at each 

joint by independent torque sensors [Motoman, 2006] or through AC drives on robot joints [ABB, 2011]. 

Examples of some robot force controlled applications follow. Appendix D provides a list and drawings of current 

force control function types and descriptions. [Fanuc, 2007] 

Kuka Robotics Corporation has delivered systems that perform grinding and milling operations with the use of 

force and torque sensors. [Ogando, 2007] Force controlled robots are starting to become more popular in "pre-
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machining" applications--or the use of robots to perform rough machining operations, leaving only a single pass 

on a computer numerically controlled (CNC) machine tool for finish machining. In this case, force control helps 

the robot close the gap with machine tool feeds and speeds by optimizing the contact forces between the robotborne tool and the workpiece. Figure 8 (left) shows a robot arm fitted with a force/torque sensor and checks the 

actuation forces on an automobile cruise-control switch. 

ABB Robotics has introduced a new system of robot control for assembly applications. The ABB RobotWare 

Assembly Force Controller utilizes force/torque sensors, adds sensor feedback to the robot's positioning, and 

allows the robot to search for the correct assembly position. Forces and torques are measured by the sensor at the 

wrist of the robot giving it a tactile sense of touch. This system makes it possible to automate tasks that earlier 

required skilled personnel. Figure 8 (right) shows a robot performing assembly of automobile engine cylinders. 

Fanuc Robotics has a similar product with force sensing to provide 3D assembly with six degrees-of-freedom 

(6DOF). 

In another application, ABB Robotics utilized a force and torque sensor to unwind, slit, and then re-wind paper to 

new dimensions. The robot, equipped with the sanding head, smooths the edges utilizing the force/torque sensor 

technology to provide force feedback. This enables the robot to feel and have a sense of touch, just as a human 

would. This sense of touch allows the robot to make quick adjustments in real-time to maintain a constant contact 

force. [ATI, 2007] 

 

Figure 8 – (left) A robot arm fitted with a force/torque sensor checks actuation forces on an automobile cruisecontrol switch. (MachineDesign.com), (right) a robot performing assembly of cylinders (www.abb.com) 

[copyright photo-use permission granted by ATI-IA and ABB]

iv. Vision 

For current machine vision to work effectively, engineers must make sure the parts have a consistent visual 

appearance. They can also include features that enable easy recognition. For example, engineers can add a boss or 

other feature to a double-sided part to help a vision system distinguish one side from another. [Assembly, 2005] 

However, [Schofield, 2010] suggests that “the use of vision guided and tactile sensing is enabling robots to 

become more reactive to their environments and less reliant on component characteristics. He suggests that 

product manufacturing flexibility can be increased with ABB Robotics’ automated ‘TrueView’ vision system with 

application to engine assembly. It uses a single camera for 3D vision guidance of robots where they suggest 

benefits to be the elimination of expensive fixtures and it automates operations that previously required human 

intervention. Claims include that it can improve efficiency, that the robot can react to changes in the 
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environment, that there is no pre-arranging or pre-placing required, and that an auto-calibration can be used for 

easy system integration. 

Fanuc [Fanuc, 2011] sells their SYSTEM R-30iATM robot controller which comes standard with the Fanuc 

iRVision hardware. The laser vision system allows the robot to ‘see’ the location of a part and the system is 

integrated directly with the Fanuc R-J3iC controller. By loading the vision software option and connecting a 

camera directly to the main CPU board, the user can add a vision process to the robotic application. See Figure 9. 

One example of perception use is from [Kuka, 2011], where different components are assembled on a function 

carrier in door linings. Car doors now serve many purposes, such as housing window lifters and audio 

components and measuring the lateral acceleration in the event of an impact for airbag deployment. Robots carry 

out all handling and assembly tasks for many different function carriers including removing the function carriers 

from an injection molding machine. An adaptive gripper system enables the removal of all the function carrier 

variants without the need for a change of gripper. Two cameras secured to the gripper establish the external 

contours and check the dimensional accuracy of the openings. 

In another example [Packworld, 2009], a robot was used to glue a packet or sachet of laundry detergent within a 

very tight tolerance to a folded card on a promotional mailing piece. The requirement was to pick a sachet in a 

random location from a moving conveyor and place it onto the card, located on another moving conveyor in a 

specific location, with ±3 mm (1⁄8 in) accuracy, at 50 pieces/min. The system used a two-camera, proprietary 

vision system to pick pieces from random locations and orient them before placing them on a moving target. The 

camera locates the packets and cards on the conveyors and sends the offset information to the robot controller. 

The controller then combines this data with the conveyor speed information to track each packet. It then picks the 

packet with a vacuum cup when it comes within range of the robot arm and places it on the out-feed conveyor. 

Figure 9 – Vision system being used to guide the robot to load a box with parts (www.fanucrobotics.com). 

[copyright photo-use permission granted by Fanuc Robots]

 “Of course, even with today’s user-friendly vision technology, setting up a new system can still be challenging. 

For example, lighting in machine vision applications is still more art than science. Lighting trials done in 

laboratories rarely duplicate production environments. Moving parts, factory lighting, air quality, outside 
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windows, and skylights can adversely affect the performance of many vision applications. Engineers must ensure 

that once the lighting has been resolved, factory conditions will not affect a new system after installation.” 

Varying lighting conditions throughout the day and year, correcting for parallax, and correlating the vision data 

with the robot controller were other issues to resolve. A constant light source, high camera mount, and camera 

calibration, respectively solved these issues. [Assembly, 2005] 

“Today, the 3D sensor is about where machine vision was 20 years ago. You can buy a 2D vision system that’s 

built into the camera with Ethernet and almost anyone can program the thing, and that’s where we need to get 

with 3D vision.” [Hardin, 2005] 3D machine vision’s biggest problem is that it’s not as easy to use as 2D because 

it needs a combination of light sources and calibration built into the sensor more than 2D cameras. LMI is 

tackling the simplicity challenge by offering lines of 3D “smart gauges” that include sensor(s), lighting, 

processing, and calibration in one package. Some smart gauges have more than one camera – stereoscopy with 

structured light. Others use lasers for fast applications because of the light intensity that lasers provide. LMI’s 

newest sensor does full 3D imaging in full color. Each sensor has multiple cameras and multiple light sources at 

different frequencies and the system combines the image automatically to provide a mixture of 3D and color. 

Lasers are suggested as the most robust method since they are not influenced by ambient light. In addition to 

lighting and computing developments, improved auto-calibration routines in 3D machine vision are an enabling 

development for 3D machine vision that is expanding its utility. SICK Inc. says their IVC-3D system helps to 

simplify 3D measurements by placing image filtering circuitry on the same chip with a CMOS optical sensor. The 

IVC-3D also uses laser line triangulation to create 3D data sets, but the processing on the CMOS chip allows the 

sensor head to isolate the laser line within the 512 pixel x 1536 pixel image and only transmit the 3D information 

with sub-pixel accuracy. This allows the system to run at extremely high frame rates up to 5 000 frames per 

second (fps) at full frame. Fast 3D analysis is critical to new vision applications that track moving objects, such as 

furniture on moving chain hooks, for example, or mobile robots on carts. Geometry based recognition systems can 

recognize more variations, e.g., changes in lighting, focus, feature, shape, etc. than laser based systems. They are 

key to handling bin picking, auto-tracking, and products that can change shape. 

c. Generic Robot System Assembly Tasks 

Autonomous assembly typically requires coordinated robot manipulation combined with end-effectors (e.g., 

gripper) and sensing (e.g., force, vision, tactile). A more detailed view of research on several generic robot tasks 

follows, including insertion and bin-picking. Robot bin picking uses vision and a robot to locate and pick parts 

from a bin or moving conveyor and eliminates the need for collating, accumulating, and orienting. The more 

generic ‘material handling’ is also used in the assembly process, although it is not discussed in this paper beyond 

the information presented in Section 2a Gripper Types that allow material handling by robots. 

i. Insertion 

Peg-in-hole insertion or fitting is not only a longstanding problem in robotics, but the most common automated 

mechanical assembly task. [Paulos, 1993] Both force and vision are being used to support peg-in-hole insertion. 

Passive force assembly is supported by chamfers on mating parts or on remote center of compliance devices. The 

purpose of force control is to make the robot sensitive to contact forces. A basic endeavor in robot force control 

is how to determine the interaction forces and efficiently use the feedback signals in order to synthesize the 

appropriate input signals, so that the desired motion and force can be maintained. The basic variables in robot 

force control are position, velocity, acceleration, and force. The differences in the existing fundamental force 

control algorithms stem from the different application of these basic variables and their relationships. The force 

control result is that the robot can “feel” its surroundings. It can apply a constant force on a surface, even if the 

exact position of the surface is not known. [ABB, 2006] Several software components are typically included in 

robot options with force control, including: gravity compensation, sensor offset calibration, activation and 

deactivation of force control, reference values definitions (desired force, torque or movement), end conditions, 
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supervision, force/load monitoring, and data types. Several robot manufacturers offer optional force control. An 

example of force control function types is shown in Appendix d. A basic approach to force control is as follows: 

1. Identify the load and calibrate the system. 

2. Set up desired force and movement pattern. 

3. Set up end condition. 

4. Activate force control. 

5. Activate force and movement pattern. 

6. Wait for end condition to occur. 

7. Deactivate force and movement patterns. 

8. Deactivate force control. 

Paulos explains a method for high precision, self-calibrating, peg-in-hole insertion using several very simple, 

inexpensive, and accurate optical sensors. (see Figure 10 top). The concept was tested on a static part and 

dynamic peg being inserted into the part. The sensors were simple optical beam sensors, which responded to the 

presence or absence of an object along the beam line. The self-calibrating feature allowed successful deadreckoning insertions with tolerances of 25 micrometers without any accurate initial position information for the 

robot, pegs, or holes. The implemented program worked for any cylindrical pegs, and the sensing steps do not 

depend on the peg diameter, which the program does not know. The key to the strategy is the use of a fixed sensor 

to localize both a mobile sensor and the peg, while the mobile sensor localizes the hole. The strategy is said to be 

extremely fast, localizing pegs as they are en route to their insertion location without pausing. The result is that 

insertion times are dominated by the transport time between pick and place operations. 

More recently, [Youngrock, 2008] performed dynamic peg and dynamic part (i.e., moving peg and part) peg-inhole insertion using vision as shown in Figure 10 bottom. Their real-time visual servoing system includes 

tracking the part with a camera and also a stereo camera shown in Figure 8 (bottom, left). The stereo camera is 

used by another visual tracking module that is a part of a multiple-vision-loop architecture. Purdue University 

demonstrated a Kalman-filter-based framework [DeSouza, 2004; Yoon, 2008] that carries out fast and accurate 

rigid object tracking even when the object motions are large and jerky. 

 

Figure 10 – Robot assembly showing (top) peg-in-hole insertion into a static part (bottom) and in a dynamic 

swinging part (www.purdue.edu) [photo-use permission granted by Purdue University]
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A type of peg-in-hole insertion utilizes threaded screws. Approximately a quarter of assembly operations in the 

manufacture of commercial products are screw insertions. [Lara, 1998] However, we found very little 

information about robot manufacturers and users programming and implementing robots to thread screws for 

assembly of parts. There has been some research in this area that will be presented later in Section 3b(iv) on 

threaded insertion. A single reference from [Masi, 2006] briefly discusses the coordinated action of two robots 

assembling a gear case. He describes that positioning errors between the grippers can and will lead to stripped 

screw threads and broken components. For coordination, Fanuc developed a multi-robot controller along with a 

robot simulator where one controller has access to all sensor information from all of the load cells and encoders 

on both robots simultaneously. One robot picked up one half of the gear case housing and presents it to the other 

robot. The second carefully inserts and meshes gears on a bearing shaft already in the housing. The second robot 

then picks up the housing’s second half and locks it in place over the assembly. The two robots then reorient the 

housing to present it to a third robot which inserts and drives screws that ultimately hold the gearcase together. 

All three robots used machine vision to accomplish the assembly task. 

ii. Bin Picking 

In many industrial processes, component to be assembled are delivered scrambled in boxes. Usually these parts 

must be picked out of the box manually to feed them into an automated process. Using an industrial robot for this 

task is very difficult. Robot vendors including Fanuc, Motoman, and Staubli have recently demonstrated binpicking systems at trade shows. At least one North American systems integrator planned in 2006 to offer “semirandom” and random bin-picking as “standard product” technology for certain types of parts. A number of 

automotive industry end-users are also experimenting with bin-picking technology, with some early applications 

already in production. [Iversen, 2006] [Shafi, 2007] explains how to implement bin-picking in a manufacturing 

operation. 

[Yaskawa, 2011], developer of Motoman robots, claims to have a cost-effective, modular packaging solution that 

is ideal for small cartons, bottles, and pouches. Multiple configurations are offered to meet specific throughput 

requirements. Figure 11 shows Motoman's high-speed, six-axis robots placing products into cases. Systems are 

available for partitioned cases. Reconfigurable grippers can accommodate a range of consumer products and can 

make multiple picks at a time. 

Figure 11 – Motoman robot placing parts into a box (www.motoman.com) 

[photo-use permission granted by Motoman]

[Delden, 2006] discussed an approach to grasping and recognizing parts in an industrial robotic work cell. Figure 

12 shows examples of (left) a bin of parts and (right) a robot picking parts from a bin. The centroid, orientation, 

and length of elongated parts lying on a flat work area are estimated by a sequence of simple algorithms. Off-theshelf components and freely downloadable software application programming interfaces (API) attempt to make 
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the system inexpensive and easily implemented. The approach has been implemented and tested with an industrial 

robot. However, retrieving randomly placed parts from a bin is much less structured and more process intensive. 

 Figure 12 – (left) bin of parts (www.tec-automation.com/.../random-bin-picking/), (right) robot picking parts from 

a bin (www.adaptivesystems-usa.com/integ_assembly.asp) 

[copyright photo-use permission granted by Fanuc Robots]

d. Specific Robot System Application Examples 

Many robot assembly tasks are currently being performed, most of which utilize fixtures, clamps, and/or jigs. 

Few tasks include robots that can adapt to flexible processes. The following is an example list of assembly tasks 

that robots are currently performing [Kuka, 2011; Motoman, 2011; ABB, 2011]. The examples are shown here 

for the reader to realize current and available assembly solutions. Many more examples are available on these and 

other robot and end-effector manufacturer websites. 

 screws stoppers into barrels 

 drills and installs fasteners in aircraft components 

 cuts increasingly complex holes in cars requiring a high degree of repeatability 

 picks up a part from a specially designed transfer system, places it in and retrieves it from a mold 

 places the required bearings and then the finished plastic part into the press unit 

 assembles fuse switch disconnects from three different pieces, places it in a press, and then mounts a 

spring and screws in it 

 handles parts through trim, bend, assembly, and spot welding operations 

 assembles LED lamps for semi-trucks, trailers, and off-road equipment 

 two arms are used to insert a sub-assembly into an office chair; one arm holds the chair while the other 

attaches a bracket 

 unloads a set of left- and right-hand parts from a four-station positioner, manipulates parts for bolt 

insertion, places parts into separate dunnage baskets, and adds slip sheets 

 holds a part in its gripper, picks up the next part with the other side of the end-effector, and places them 

 feeds the cylinders of the unit with the thread inserts, which it had previously taken out of sorting pots 

with its suction gripper 

 picks up eight thread inserts individually in one cycle and then passes them on as a group to the cylinder 

 sews leather covers for seat squabs and seat backs for both basic and multi-function seats 

 screws brackets onto the front end of a car 

 positions parts in a press unit and presses in two types of quick-action fasteners 

 removes the parts from an injection molding machine with an adaptive gripper system for a variety of 

parts 

 checks the thickness of material with measuring tongs 
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 inserts hubs into the die with 0.02 mm tolerance 

 inserts clips into flexible cable ducts 

Automobile manufacturers, and perhaps others, are requesting specific dexterous manipulation tasks solved by 

robots that can be reconfigurable for a variety of vehicles or other equipment, parts, and assemblies being 

manufactured. One manufacturer suggested the following assembly areas for robot manufacturers to consider 

automating: 

 Wiper module assembly installation 

 Headlights fastening including cable connection to the light 

 Trimline installation 

 Gas, brake, and other fluids tanks filling 

 Weather strip installation 

 Carpet installation 

 Seat installation 

 Wheel and tire fastening 

 Hoses, cables, and wiring harnesses installation (see Figure 13) 

 Figure 13 – Examples of automobile harnesses and cables and their relatively difficult-to-access locations 

Robotic assembly systems offer tremendous promise for flexible assembly automation, but present a variety of 

complex research issues due to the positioning inaccuracy of the manipulator, dimensional variation of mating 

parts, and their physical interactions. [Cho, 1987] 

3. Advances in Robotic Assembly 

a. Systems 

The nature of manufacturing robotics is changing to be more collaborative with humans. Rodney Brooks, 

Heartland Robotics Founder and Massachusetts Institute of Technology (MIT) Professor, says of the future for 

robotics: 

“Today's manufacturing robots are big and stiff, unsafe for people to be around, engineered to be precise and 

repeatable, not adaptable. Normal workers can't touch them... What if ordinary people could touch robots? What 

if ordinary people got to interact with them and use them?”

[Motoman, 2010] developed the unique dual-arm SDA-series (slim, dual-arm) robot that combines ‘human-like’ 

movement with robotic speed, dexterity, and repeatability. The robot is used for assembly, pick and place, 

Wiring harnesses in jack 

compartment 

Cables further within the 

same compartment 

shown in the left photo 
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machine tending, and other applications. These unique robots are designed with a central "torso" and two 

articulated arms. The Motoman SDA series has three robot models with graduating payload capacities: the 

SDA5D, SDA10D, and SDA20D. SDA robots have a total of 15 axes of movement, seven in each arm, one in the 

base. The two arms can be programmed to work independently or together. Motoman suggests that with a two 

arm robot, the need for costly positioners and tooling is minimized. One arm can act as the positioner, holding the 

part in place, while the other arm performs the rest of the application (i.e., welding, assembly) (see Figure 12 left). 

SDA robots also provide more payload and work envelope options than with single robot arms. When both arms 

work together, their payload capacity compounds. In the same manner, the two arms of SDA robots can work 

together to offer exceptional horizontal reach from side-to-side of the torso. Similarly, ABB (see Figure 14 right) 

has just unveiled their concept robot called FRIDA. They claim FRIDA’s key technology features are: 

 Harmless robotic coworker for industrial assembly 

 Human-like arms and body with integrated controller 

 Complements human labor with scalable automation 

 Padded dual arms ensure safe productivity and flexibility 

 Lightweight and easy to mount for fast deployment 

 Agile motion 

Figure 14 –Dual arm robots (left) [Motoman, 2010] and (right) [ABB, 2011] 

[copyright photo-use permission granted by Motoman and ABB]

[Willow Garage, 2011] has developed the mobile PR2 robot with dual arms, torso, and Class 1 gripper as shown 

in Figure 15. PR2 is programmed using the ROS (robot operating system) open source robotic software 

framework and has a variety of features that provide potential for robotic assembly, including: back-driveable, 

current controlled, spring counterbalance arms; continuous two degrees of freedom wrists and enough torque to 

manipulate everyday objects from opening doors to handling frying pans; two grippers that can “grasp everything 

from towels to tea cups, and brooms to brews;” modular open interfaces to exchange different grippers, forearms, 

whole arms, or sensors; and low level and high level real time controllers with a software architecture for grasping 

and manipulation. Mobility includes a telescoping spine and an omni-directional base. Perception includes object 

recognition using lasers and cameras, open computer vision libraries, and 3D point cloud processing libraries. 

Manufacturer claimed PR2 Manipulator Specifications: 

 Arm Payload: 1.8 kg (4 lbs) 

Wrist Torque: 4 Nm (3 ft lbf) 

Grip Force: 80 N (18 lbf) 
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Figure 15 – PR2 Robot [Willow Garage, 2011, shown is a publicly available image] 

b. Research 

There is much research in advancing robotic assembly. This section divides the surveyed research into several 

areas including: touch, force, vision, threaded insertion, bin-picking, control, and U.S. Government research. 

i. Touch 

Surveying robot tactile or touch sensing uncovers most efforts from the 1980’s and 1990’s as shown in Section 

2b(i) Tactile. Current examples of this research lie in tactile ‘robot skin’ systems. 

Early research by [Inaba, 1996] presents the design and implementation of a tactile sensor suit that covers the 

entire body of a robot. The sensor suit, demonstrated on a full-body humanoid, was designed to be soft and 

flexible and to have a large number of sensing regions. They built the sensor suit using electrically conductive 

fabric and string. The current version of the sensor suit has 192 sensing regions. Each sensing region works as a 

binary switch in the current version. All of the signals from the sensor suit are gathered and superimposed on a 

visual image of the robot. A video multiplexer for the sensor signals is built on a field programmable gate array 

set. 

[Kageyama, 1999] described two types of tactile sensor elements: a multi-valued touch sensor and a conductive 

gel sensor. The multi-valued touch sensor has multi-level pressure thresholds and is capable of covering wide 

areas of robot surfaces. The driving circuit is made as a module and scans the 128 switch elements in 1 ms. The 

switch elements have two pressure thresholds, with the first set to 25 gf/cm2

. The other sensor is made of 

conductive gel which has remarkable softness. This conductive gel has the advantage of its softness compared 

with other sheet type tactile sensors. The impedance of the gel changes by approximately 20 % from 0 gf/cm2 to 

400 gf/cm2

. The tactile sensor consists of 8×8 sensing points on the intersection of the electrodes, and the 

processing module scans all the points in 30 ms. Both sensors were again applied to a wheeled humanoid robot. 

[Kageyama, 1999] 

More recently, [Hoshi, 2006] and [Barnard, 2010] developed a new tactile sensor skin ("skin by touch area 

receptor" or STAR). The skin consists of two components. One is a sensor element that detects a contact area in 

addition to a contact force. The element is inspired by the fact that humans can discriminate the sharpness of 

objects sensitively on any part of their bodies in spite of their several-centimeter two-point discrimination 

thresholds. The researchers developed the sensor element that has such characteristics in a very simple structure; 

two layers of compressible insulators (urethane foam) which are sandwiched between three pieces of stretchable 

conductive sheets (conductive fabric). The other component is a sensor/communication chip. The chips are 

arranged at the boundaries of the elements, and the chips measure the capacitances between the conductive layers 
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and send signals through the same conductive layers. The chips enable connection of the elements to compose a 

soft robot skin including no long wires. 

 [Abhinav, 2009] describes a concept and presents experimental results for robotic end-effector operation when 

attached to an industrial robot for handling product items that are flexible or variable in physical properties such 

as size, shape, or firmness. This work also describes a scheme by which a manipulator can use dynamic tactile 

sensing to detect when it is about to lose hold of a grasped object and take preventive measures before gross 

sliding occurs. [Wikipedia (robotics), 2011] briefly discussed that scientists developed a prosthetic hand in 2009, 

called SmartHand, that functions like a real one - allowing patients to write, type on a keyboard, play piano, and 

perform other fine movements. The prosthesis has sensors that enable the patient to sense real feeling through the 

fingertips. 

ii. Force 

Assembly tasks involving large positional uncertainties are unsuitable for use of position-controlled robots. To 

automate such tasks, the assembly system must be responsive to contact forces. Issues in addressing force 

responsive automated assembly include contact stability, the degree of force responsiveness required for success, 

the speed of a successful implementation, and the means to program a force-responsive system to perform a given 

assembly task. Force control, or interaction control with the environment, can be performed with either indirect 

force control or direct force control. Indirect force control is motion control, without explicit closure of a force 

feedback loop. Direct force control offers the possibility of controlling the contact force to a desired value, thanks 

to the closure of a force feedback loop. [Siciliano, 1999] 

 

[Newman, 1999] provides useful background references and information over several decades for force sensing 

used in assembly. The representative example of inserting a peg in a hole has been studied extensively. [Whitney, 

1977] analyzes how to use a force feedback strategy to guide a simple cylindrical “peg-in-hole” assembly. 

[Whitney, 1982] discusses development of the quasi-static conditions to avoid wedging and jamming for the pegin-hole. [Caine, 1989] provides analysis of non-chamfered peg-in-hole. [Raibert, 1981] developed controllers that 

used both position and force control for the manipulation of constrained objects. [Peshkin, 1990] then studied 

error correction and recovery during assembly. [McCarragher, 1994, 1995 and Austin, 1997] introduced synthesis 

methodologies involving a discrete event controller for a force-controlled system for assembly tasks. [Whitney, 

1979] provided analysis of peg-in-hole assemblies at Draper Lab which led to the development of the remotecenter compliance (RCC) wrist. 

A remote center of compliance can be implemented as a passive mechanism in the wrist, instead of as an active 

control loop involving a force sensor, computer, and actuators. Using a passive RCC, a force-based strategy is 

encoded in the hardware in terms of the force vs. deflection characteristics. If the peg (and/or hole) is chamfered, 

the forces that arise due to small position errors cause the peg to self-align with the hole. Since the RCC is 

passive, there are no stability problems. This technique has demonstrated impressive assembly speeds. However, 

if the parts are not chamfered, or if the position uncertainties exceed the chamfer width, the RCC is ineffective. In 

general, if the force-guided strategy to be invoked cannot be realized in terms of a passive mechanism, then the 

RCC approach is inadequate. Caine, et. al., examined strategies where RCC for both non-chamfered, cylindrical 

and rectangular peg-in-hole insertion is not sufficient. They then developed strategies for constraining the 

allowable contact configurations between the parts in order to avoid configurations that will cause the assembly to 

fail. 

Active algorithms for responding appropriately to sensed contact forces can accommodate more complex 

assembly cases and larger positioning uncertainties. In practice, these systems are too slow to be competitive with 

manual assembly. Attempts to speed up robots controlled with these algorithms have resulted in contact 

instability. [Zeng, 1997] reports on the existing robot force control algorithms and their composition based on the 

review of 75 papers on this subject. The objective was to provide a pragmatic exposition with specialty on their 
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differences and different application conditions, and to give a guide to existing robot force control algorithms. 

The previous work can be categorized into discussion, design, and / or application of fundamental force control 

techniques, stability analysis of the various control algorithms, and advanced methods. Advanced methods 

combine the fundamental force control techniques with advanced control algorithms such as adaptive, robust and 

learning control strategies. While the majority of algorithms attempt reactive force control, [Gullapalli, 1994] 

presents a practical method for autonomous synthesis of appropriate admittance behavior for robust highprecision robotic assembly. They applied an on-line learning approach that relies on the appropriate admittance 

through repeated attempts at the assembly operation. They are able to circumvent the problems that alternative 

approaches have in trying to model the interactions between the robot and its environment. Their test results for 

the peg-in-hole insertion task show that the performance compares favorably with that of other proposed methods 

for high-precision, chamferless peg-in-hole insertion. 

[Newman, 1999] also examined robotic assembly issues in the context of automotive transmission components 

and reported on an impedance-based, low-level algorithm and its interface to higher-level strategies that exhibited 

gentle, fast, and reliable assembly of example transmission components. 

iii. Vision 

Past research on industrial inspection and assembly suggests that problems in these areas are well suited to modelbased analysis. The objects are man-made and manufactured from well-defined geometric descriptions. [Kruger, 

1981] Visual feedback has traditionally been used in the assembly process to a very limited extent. With the 

advent of effective visual servoing techniques, visual feedback can become an integral part of the assembly 

process by complementing the use of force feedback to accomplish precision assemblies in imprecisely calibrated 

robotic assembly workcells. [Nelson, 1993] 

Recent research from [Agrawal, 2009] presents a complete vision-guided robot system for model-based threedimensional (3D) pose estimation and picking of singulated 3D objects. The system uses a novel vision sensor 

consisting of a video camera surrounded by eight flashes (light emitting diodes). By capturing images under 

different flashes and observing the shadows, depth edges or silhouettes in the scene are obtained. The silhouettes 

are segmented into different objects and each silhouette is matched across a database of object silhouettes in 

different poses to find the coarse 3D pose. The database is pre-computed using a computer-aided design (CAD) 

model of the object. The pose is refined using a fully projective formulation of Lowe's model-based pose 

estimation algorithm. The estimated pose is transferred to a robot coordinate system utilizing the hand–eye and 

camera calibration parameters, which allows the robot to pick the object. The authors suggest that the system 

outperforms conventional systems using two-dimensional sensors with intensity-based features as well as 3D 

sensors. They handle complex ambient illumination conditions, challenging specular backgrounds, diffuse as well 

as specular objects, and texture-less objects, on which traditional systems usually fail. The vision sensor is 

capable of computing depth edges in real-time and is low cost. 

iv. Threaded Insertion 

A step beyond peg-in-hole assembly is using self-tapping screws. Lara, et. al. introduced an automated robotbased system for the insertion of self-tapping screws into unthreaded holes. The system consists of three main 

components: a manipulator-guided screwdriver, a camera, and a system to control and monitor the overall 

process. They performed experiments in order to identify the requirements needed for a fully automated insertion 

system. 

In robotics literature threaded insertion of screws in tapped holes is often referred to as a typical task; yet, unlike 

the smooth peg-in-hole problem, a robust control solution for inserting threaded fasteners has not been presented. 

[Nicolson, 1993] Relative screw versus nut orientation is a critical threading element. When errors are allowed 

only for the position along the axis of the bolt, the control problem for this restricted case is one dimensional. 

Therefore, there are two obstacles to the full automation of screw threading: 1) feeding and holding the parts, and 
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2) controlling the parts to ensure proper mating and to detect part failures in the presence of positional 

uncertainty. The main screw threading error is the inability to begin the thread when the bolt rotates too fast for a 

given axial spring constant and position for the spring equilibrium point. Nicolson, et. al. consider controlled 

compliance and accommodation matrix techniques claiming robust insertion of threaded fasteners. Errors in 

translational positioning are shown to be easily corrected. Errors in the angle of tilt between the threaded parts are 

shown to be much more difficult to correct and constrain the region of convergence for simple linear techniques. 

[Diftler, 1999] discussed kinematic models that describe the relationship between threaded parts during backspinning and determining angular alignment of bolts to nuts. They used a technique based on back-spinning a nut 

with respect to a bolt and measuring the force change that occurs when the bolt ‘falls’ into the nut. 

v. Bin Picking 

One robot manufacturer suggests that the future in autonomous robotic capability may be in random bin picking. 

Further, they suggest that there will be no need for unscrambling, collating, or datum positioning, and therefore, 

systems would become smaller, cheaper, and have far fewer elements. [ABB, 2010] 

Research on machine vision and other sensor and software algorithm combinations have focused on bin picking 

due to its complexity. An internet search on ‘bin picking’ scholarly papers returned nearly 12,000 hits. 

[Hujazi,1990] suggests that prior research was based on intensity images and instead presents a segmentation 

algorithm using range images of industrial parts in a bin. A robot is then used for bin picking with a vacuum 

gripper making use of the algorithm to detect part edges and then perform region growing to build a final 

segmented image. [Ghita, 2003] says that generally, accurate 3-D information is required to develop versatile 

bin-picking systems capable of grasping and manipulation operations. After edge detection, as in the previous 

paper, Ghita suggests to recognize the object placed on the top of the object pile using a model-driven approach in 

which the segmented surfaces are compared with those stored in the model database. Finally, the attitude of the 

recognized object is evaluated using an eigen-image approach augmented with range data analysis. [Boehnke, 

2007] used knowledge about the form of the objects to find them in range data, comparing the 2.5 dimension 

appearance of simulated object poses with the real range data. This approach takes features of range sensors into 

consideration to improve the accuracy and robustness of the object localization. [Boughorbel, 2003] suggested 

two types of sensors: range mapping scanners and video cameras for bin picking. The geometry of bin contents 

was reconstructed from range maps and modeled using superquadric representations, providing location and parts 

surface information that can be used to guide the robotic arm. Texture was also provided by the video streams and 

applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and 

represents a step toward full automation. 

vi. Control 

Robot control for assembly is inherent within most sections of this paper, where appropriate part positioning and 

movement are required. This section surveys specific research being performed on algorithms for grasp, fuzzy 

control, and learning. 

Grasp Algorithms

The complexity of the human hand, having 27 degrees-of-freedom, which on a conservative estimation can result 

in more than seven billion different hand poses, requires the derivation of simplified hand motions and hand 

postures. [Iberall, 1997] Basic types of hand prehension are shown in Figure 16 for different objects. [Shimoga, 

1996] provides a survey of existing computational algorithms meant for achieving four important properties in 

autonomous, multi-fingered robotic hands, including: dexterity, equilibrium, stability, and dynamic behavior. 

Further, Shimoga suggests that multi-fingered robotic hands must be controlled so as to possess these properties 

and hence be able to autonomously perform complex tasks in a way similar to human hands. 
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 1 2 3 

4 5 6 

Figure 16 – Hand prehension of different objects: 

1. Cylindrical hollow grip, 2. Tip grip, 3, Hook grip, 4. Three finger grip, 5. Hand palm grip, 6. Tong grip. 

[The photos shown were approved for use by the following: ©Pete Saloutos/Shutterstock, 

©discpicture/Shutterstock, ©Volodymyr Krasyuk/Shutterstock, ©April Cat/Shutterstock, 

©GLYPHstock/Shutterstock, ©ilker canikligil/Shutterstock] 

[Aiyama, 1998] designed compliance and planning motion of a 3-fingered gripper and manipulator control to 

handle a box in a compact array of six boxes as shown in Figure 17 (left). First one of the fingers tumbles one 

box to make two side faces free from the obstacles. Then the other two grasp both the sides and pick it up. To 

fulfill such a sequence of dexterous manipulations onto various objects, the design of compliance of the fingers is 

essential. Figure 17 (right) depicts a similar dexterous part manipulation with no grasping. 

Figure 17 (left) dexterous manipulation and (right) part insertion by a non-grasping manipulation 

[The graphics shown were redrawn by NIST based on images in Aiyama, 1998] 

Fuzzy Control

[Soliman, 2009] explains their gripper designed to grasp unknown objects with different masses, shapes, and 

coefficients of frictions considering simplicity, durability, and economy. The grasping process during object 

lifting is considered mainly based on the slip reflex principle, as applying insufficient force leads to object 

slipping, and dropping may occur. A new fuzzy control algorithm based on empirical investigation of the human 

hand skills was proposed to adjust the applied force on the object without the risk of the object crushing or 

dropping. A simple rule base was used. Also, the controller was designed to maintain the object slip in reasonable 
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limits. The gripper design and developed force control algorithms resulted in the fast response of the task 

achievement. Input/output system variables are measured and analyzed. Experimental results obtained for 

different object masses and system disturbance show fast response in stopping the slippage and an enhancement 

in the grasping of different objects. 

Control through Learning

[Gullapalli, 1994] describes their research in on-line learning for force control of peg-in-hole assembly, 

previously mentioned in Section 3b(ii) Advances in Robotic Assembly, Research, Force. The learning algorithm 

relies on the appropriate admittance through repeated attempts at the assembly operation. 

[Kleinmanna, 2009] suggests that learning control systems are expected to have several advantages over 

conventional approaches when dealing with complex, high-dimensional processes. One example is the task of 

controlling grasp operations of a multi-fingered, multi-jointed robot gripper (the Darmstadt-Hand). The Advanced 

Gripper Control with Learning Algorithms (AGRICOLA) presented in this reference is able to maintain a stable 

grasp even if disturbances are applied. The algorithms also work for objects of different sizes for which the 

grasping has not been learned. Compared to the conventional stiffness approach, the performance of the learning 

system is equal, but the design is much easier since less knowledge about the gripper-hardware must be taken into 

account. The main part of the learning control loop is an associative memory that stores the grasping behavior as 

determined by the choice of an objective function. 

[Bidaud, 1993] presents an advanced control system developed for an articulated gripper. This articulated gripper 

was previously designed to achieve stable grasp of objects with various shapes and to impart compliant fine 

motions to the grasped object. The researchers introduced autonomous reasoning capabilities in the control system 

of this device. Fine motion strategies, needed for mating or grasping, use inductive learning from experiments to 

achieve uncertainty and error recovery (on sensing, control, and modeling). The reference provides an overview 

of the articulated gripper's capabilities for a better understanding of the programming environment proposed. 

Declarative programming facilities in the controller were implemented for solving the problem of synthesis for 

fine motion planning through a time-sensitive expert system. Also, a heuristic procedure was used to obtain an 

implicit local model of contacts in complex assembly tasks. 

vii. U.S. Government Research 

[FRAPA, 1997] discusses a former NIST Advanced Technology Program (now Technology Innovation Program 

(TIP) [TIP, 2011]) joint project including industry, academia, and government for flexible robotic assembly of 

vehicle powertrain components. The project was approved because of the potentially large economic and 

technological gains, including: 

 The total cost to U.S. industries of ergonomically related problems is in the range of $13 billion to $20 billion 

annually. Repetitive stress injuries (RSIs) are more likely to occur in assembly operations with heavy parts 

that require large mating forces. 

 Powertrain and vehicular assembly have a high concentration of heavy parts that need to be manipulated 

repetitively without injury to the worker and without damage to the parts. 

 Once autonomous powertrain component assembly is demonstrated in a flexible assembly work cell, the 

assembly technologies can be applied to numerous other component assemblies and applications, throughout 

various industries. 

Figure 18 shows a graph of robotic application technology requirements and was the focus of the FRAPA flexible 

robotic assembly project. The result included a flexible system including a robot and vision system with 

requirements to be programmable and emulate human characteristics of force-controlled assembly using position 

control for large movements and changing over to force control before contacting parts. Also, the system 

included adaptive learning using artificial intelligence or genetic routines. A parallel axis robot resulted with 
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natural admittance control and relatively high dexterity, including 15 μm precision, 200 Lb-f and 75 Lb nominal 

payload with a 1 m reach, and force sensing of 1 N (1/4 Lb). 

Figure 18 – Graph showing how the FRAPA project compared robot versus visual sensing requirements. 

FRAPA included development of a 3D vision sensor with pose estimation and processing software that calculates 

the 6 DOF pose of parts in random orientations from true 3D vision data, and translates this data into robot 

coordinates, reducing part presentation and precision tooling requirements. The vision system trains itself on new 

parts, requiring no sophisticated programming or CAD inputs. The project team states that the FRAPA-developed 

vision system is the current state-of-the-art in direct 3-D image acquisition. All others are based on camera 

technology that do not return a true 3-D dataset and that rely on the inference of 3-D from 2-D data.

The Defense Advanced Research Projects Agency (DARPA) has been at the forefront of manipulation research. 

Three programs are currently underway: 1) Revolutionizing Prosthetics Program under the Biology Directive and 

Restorative Biomedical Technologies Thrust and managed by COL Geoffrey Ling, M.D., Ph.D., 2) Autonomous 

Robotic Manipulation (ARM) Program, and 3) Maximum Mobility and Manipulation (M3) Program under the 

Materials Directive and Multifunctional Materials and Material Systems Thrust managed by Gill A. Pratt, Ph.D. 

[DARPA, 2010] 

The Revolutionizing Prosthetics Program will create, within this decade, a fully functional (motor and sensory) 

upper limb that responds to direct neural control. This revolution will occur by capitalizing on previous DARPA 

investments in neuroscience, robotics, sensors, power systems, and actuation. The program states that it will 

deliver a prosthetic for clinical trials that has function almost identical to a natural limb in terms of motor control 

and dexterity, sensory feedback (including proprioception), weight, and environmental resilience. The four-year 

device will be directly controlled by neural signals. The results of this program will allow upper limb amputees to 

have as normal a life as possible despite their severe injuries. Currently, prototypes from the two-year and fouryear efforts are undergoing human testing. 

The ARM program thrust is to enable autonomous manipulation systems to surpass the performance level of 

remote manipulation systems that are controlled directly by a human operator. ARM will create manipulators with 

a high degree of autonomy capable of serving multiple military purposes across a wide variety of application 

domains, including but not limited to counter-IED (improvised explosive device), countermine, search and rescue, 

weapons support, checkpoint and access control, explosive ordnance disposal, and combat casualty care 

(including battlefield extraction and treatment). The driver for this program is that current robotic manipulation 

systems save lives and reduce casualties, but still have many limitations. As examples, while manipulatation 
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systems perform well in certain mission environments, they have yet to demonstrate proficiency and flexibility 

across multiple mission environments; they require burdensome human interaction and the full attention of the 

operator; and the time required to complete tasks generally exceeds military users' desires. 

The DARPA Maximum Mobility and Manipulation (M3) program is striving to create and demonstrate 

significant scientific and engineering advances in robotics that will: 

 Create a significantly improved scientific framework for the rapid design and fabrication of robot systems 

and greatly enhance robot mobility and manipulation in natural environments. 

 Significantly improve robot capabilities through fundamentally new approaches to the engineering of 

better design tools, fabrication methods, and control algorithms. The M3 program covers scientific 

advancement across four tracks: design tools, fabrication methodologies, control methods, and technology 

demonstration prototypes. 

c. Patents 

Most patents shown below were found from an internet search on “robot gripper” and have U.S. Patent numbers 

of 7,xxx,xxx numbers or higher or with the new date/patent number designation from 2006 to present. As shown, 

the most recent inventions include special purpose gripping systems that build on previously demonstrated 

technology and are, in most cases, already in use. For example, multiple vacuum grippers as opposed to a single 

vacuum gripper, and grippers that are designed specific to part shapes and sizes, such as syringe and large jaw 

separation grippers. 

 

U.S. Patent No. 7,690,706 B2, called: “Gripper Device,” issued April 6, 2010: 

Abstract: An apparatus for transporting objects may include a plurality of grippers having a first spacing at a first 

position and second spacing at a second position. A drive mechanism may be provided for selectively displacing 

the grippers from the first position to the second position to adjust the spacing between the grippers. See Figure 

19. 

Figure 19 - “Gripper Device,” from U.S. Patent No. 7,690,706 B2 

U.S. Patent No. 7,134,833 B2, called: “Servo Adjustable Gripper Device,” issued to Johannes J. M. de Koning, 

November 6, 2006: 

Abstract: A servo adjustable gripper device for gripping and transporting at least two objects such as boxes 

includes a frame and at least two gripper assemblies connected with carriages slidably connected with the frame. 

The gripper device further includes a robotic arm which controls further movement and placement of the boxes on 

a pallet. The position of the gripper assemblies on the frame is adjustable to provide equal lifting force for a 

variety of differently sized boxes or for different numbers of boxes. See Figure 20. 
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 Figure 20 – (left) Side view and (right) isometric view of the “Servo Adjustable Gripper Device” shown in U.S. 

Patent No. 7,134,833 B2. 

U.S. Patent No. 7,837,247 B2, called: “Gripper with Central Support,” issued to Waldorf, Jenkins, and Kalb, 

November 23, 2010: 

Abstract: A gripper assembly includes at least one gripper jaw and an actuator head linked with at least one 

gripper jaw. An actuator selectively operates to move the actuator head between a plurality of positions. A 

support is fixed relative to the actuator and includes a guide slot that guides the actuator head. One of the actuator 

heads or the guide slot includes a channel and the other of the actuator head or the guide slot includes a guide 

member extending at least partially into the channel. See Figure 21. A dual rod gripper version was also invented 

by Waldorf, et. al. under U.S. patent number 7854456. 

Figure 21 - “Gripper with Central Support” from U.S. Patent No. 7,837,247 B2. 

U.S. Patent No. US 2009/0067973 A1, called: “Gripper Device,” issued to Eliuk, Rob, Jones, and Deck, March 

12, 2009: 

Abstract: Gripper devices for handling syringes and automated pharmacy and mixture systems that utilize such 

gripper devices. The gripper devices may include various gripper finger profiles, substantially tapered or angled 

gripping surfaces, and/or gripper fingers interleaving to reduce radial distortion of the syringes to be grasped 

while opposing axial motion of the syringes. See Figure 22. 
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Figure 22 - “Gripper Device” from U.S. Patent No. US 2009/0067973 A1. 

Other recent gripper patents found using the same search criteria as above include: 

“Slide Gripper Assembly” having a slide assembly coupled to a gripper assembly. U.S. Patent No. 7,188,879 B2, 

by McIntosh, Steele, Givens, and Davenport on March 13, 2007 

 “Expandable Finger Gripper” for gripping the inside of containers or parts with cavities. U.S. patent no. 

7,452,017 B2 by Maffeis on November 18, 2008. 

“Quick Change Finger” that releasably connects a gripper finger to a robotic arm. U.S. patent no. 2010/0314895 

A1 by Rizk and Delouis on December 16, 2010. 

“Long Travel Gripper” for a rectilinear, large jaw separation gripper. U.S. Patent No. 7,490,881,B2 by Null and 

Williams on February 7, 2009. 

“Automated Storage Library Gripper Apparatus and Method” for transporting and handling storage devices 

(cartridges). U.S. Patent No. 7,212,375 B2 by Dickey and Standt on May 1, 2007. 

 “Gripper System” having a pair of jaws and operates in one plane having a central closure axis. U.S. Patent No. 

US 2010/0164243 A1 by Albin on July 1, 2010. 

 “Stack Gripper” for gripping unbound printed products. U.S. Patent No. US 2007/0154292 A1 by Gammerler, 

Gunter, Meisel, Muller, Schubart on July 5, 2007. 

4. Summary 

This survey gave an overview of the current state of manipulation systems, as well as insight into future 

manipulation systems through the discussion of research being performed in the field. It included a methodical 

discussion of industrial manipulation capabilities, advancements, and research with a focus on autonomous 

assembly applications. The Appendices include a glossary of terms, a listing of industrial robot system 

standards, and a method of design for assembly. 
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In summary and with regard to end-of-arm tooling (EOAT) purchase, [EOAT, 1997] suggests the following tips 

that a user should follow: 

 No one gripper style will secure every part. 

 No matter how good the EOAT and no matter who built it, the user will always need to adjust it. 

 Before buying any tooling, wise application-specific users compare the proposed EOAT with part drawings to 

ensure a good fit. 

 The EOAT and the part weight together must not exceed the robot capacity. Also, choose tooling that's as 

light as possible to make the robot last longer. 

This survey determined that the end-effector patents found are generally special purpose and that breakthroughs in 

highly capable assembly systems are minimal. As stated by [DARPA, 2011] with regards to defense robotics, 

“Robots hold great promise for amplifying human effectiveness in Defense operations. Compared to human 

beings and animals, however, the mobility and manipulation capability of present day robots is poor. In addition, 

design and manufacturing of current robotic systems are time consuming, and fabrication costs remain high. If 

these limitations were overcome, robots could assist in the execution of military operations far more effectively 

across a far greater range of missions.” This statement can also be related to the capabilities of current robots 

needed for assembly operations. Dexterity, perception, and tactile capabilities of robot systems are advancing as 

exemplified by the multi-fingered grippers, the bin-picking capabilities, and the vision research. Also, safety and 

performance standards are beginning to consider collaboration of humans and robots within the same workspaces. 

However, several recommendations are listed in section 5 that suggest further advancements for robotic assembly 

systems. 

5. Recommendations 

A Smart Assembly Workshop [Smart, 2006] was held at NIST in 2006 to develop a vision and to define the stateof-the-art and industry needs in Smart Assembly. Smart Assembly refers to a next-generation capability in 

assembly systems and technologies which integrate “virtual” and “real time” methods in order to achieve dramatic 

improvements in productivity, lead-time, quality, and agility. The purpose of the workshop was to develop a 

broad industry/academic vision and to define the state-of-the-art and needs in “Smart Assembly.” 

Topics included: 

 Defining and measuring aspects of smart assembly. 

 Identifying key characteristics and attributes of smart assembly systems. 

 Identifying critical scientific reserach challenges to enable smart assembly. 

 Identifying critical implementation and infrastructure/standards challenges. 

 Identifying models and opportunities for leveraging and collaboration to accelerate the development and 

implementation of smart assembly capability. 

The workshop provided a substantial first step towards the formulation and launching of a Smart Assembly 

initiative. The characteristics and attributes for the future vision state were clearly defined, which fed the 

definition of priority recommendations, and suggested next steps were outlined towards a unified program. One 

specific recommendation was that NIST should consider the creation of a National Smart Assembly Testbed in 

cooperation with industry sponsors to validate the interoperability and performance of smart assembly modules 

and systems. 

The following are recommendations from literature and from interviews with manufacturers. These 

recommendations may be useful for product development, research planning, and standards development. 

 Develop performance metrics for autonomous assembly - current specifications from vendor products are 

limited to accuracy and resolution. Performance measurements should include both robot system 

performance measurements (i.e., overall system performance) and system component performance 

measurements (e.g., force sensor, gripper, robot). 
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o An example performance measure testbed is shown in figure 23. The figure depicts an 

independent test setup that incorporates a 6-axis load cell to measure applied forces of an 

assembly operation, as well as definable measurements of success. The four step assembly shows 

a set of spur gears to be assembled using various standard force control capabilities and the 

resultant forces are monitored throughout the process using an independent load cell or 

force/torque sensor. 

o Other examples are: standard peg-in-hole tests (smooth peg or screws), slides, etc. 

o Vision type assembly tasks 

o Associated with these tests are, for example, force and other sensors with stock vendor algorithms 

having potentially many tuning variables 

Figure 23 - NIST Performance Measures for Assembly concept drawing 

[Schofield, 2010] stated that: 

 Increased use of robots as ‘intelligent’ fixturing will make systems more flexible and give faster product 

change over times 

 External metrology will open up many applications where robots have previously not been sufficiently 

accurate 

 More human/robot interaction in a safe environment will maximize productivity 

 There is a need for reduced sizes of end-effector devices – compliance, force/torque sensors, quick changes, 

sensors (proximity, tactile) 

 There is a need to match autonomous robot systems capabilities to humans, i.e., as compared to precision 

tactility and dexterity by a surgeon, watchmaker, or jewelry maker. 

o tactility – there are limited/no sight applications where the robot needs to ‘feel‘ to perform high 

precision assembly (e.g., start an M3 screw or meshing small gears, parts) 

o dexterity – manipulating tools (e.g., tweezer sized grippers) and parts (e.g., pins, rivots) for precision 

assembly 

Recommendations from interviews with end-effector and robot manufacturers at the ProMat and Automate 2011 

events in Chicago, IL, March 2011 who preferred to remain anonymous are as follows: 

End-Effectors: 

 Clear, standard interfaces for grippers 

 Integration of current industrial grippers with sensors to sense that a part being gripped has been acquired. 

 Verification of perception systems with low-cost gripper tactile sensors 

 Adaptive end-effectors so that robot control includes gripper control - for example, unlike typical human 

arm/hand control, current robot and gripper systems allow the robot to move to a part, then the gripper grasps 
Survey of Industrial Manipulation 

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the part, and the robot moves the part. Alternatively, this motion would be done simultaneously with gripper 

feedback to the robot controller to adjust the arm and grip during the entire part acquisition process. Low cost 

tactile sensing for off-the-shelf pneumatic grippers is needed. 

Robots: 

 “Feel your way through the assembly process” including robots supported by low mass/quick response, high 

update rates, direct impedance control per joint. 

 Hand guiding robots through the teaching process. 

 Path planning of two-arm robots so they don’t interfere or collide with each other. 

 Robots on vehicles - The issue is that the automated guided vehicle (AGV) is addressed by standards 

maintained by the industrial truck interest group. This situation was great for the initial applications of the 

load-carrying AGV (based on identifiable tracks in an industrial setting), but has become problematic as the 

AGV is adapted to become more of a co-worker to the human. Since the redefinition of the term ‘robot’ 

within the International Organization for Standardization (ISO), the AGV now fits within its purview. The 

inclusion of roving robot arms and similar applications in a holistic structure is not currently being considered 

by industrial standards organizations. 

 Dynamic work volumes, for example robot arms on vehicles, that are intelligently controlled to avoid 

obstacles as the vehicle moves the robot and its payload. Included here is intelligent environment sensing and 

control. 

 Tactile response is critical in assembly operations, and has been most difficult to develop with robots. A 

robot does an assigned task very well and repetitively, but has problems with variable situations. 

 Collaborative robots, since they will play a huge part in future assembly applications. More work is needed in 

this area. The ISO/TS 15066 technical specification for how to implement robot collaboration with humans 

and other equipment (e.g., AGV’s) will address some of this need. The ISO 10218-1 standard provides the 

manufacturer with information for construction of collaborative robots. The ISO 10218-2 standard provides 

the integrator and user guidance on how to use collaborative robots. 

 Measurement methods are needed for the component inaccuracies stemming from the robot, end-effector, 

sensors, fixturing, dynamics, joint flex, or other inputs to the assembly process, so that robots can be designed 

to meet high accuracy, assembly applications. 

An example scenario suggested by robot manufacturers was to assemble a kit of parts, such as a disposable 

camera, having small parts that mesh and require robot control, including force sensing, perception, and high 

dexterity. During an industry workshop on Dynamic Perception [Eastman, 2009], attendees discussed the 

scenario of assembling a kit for the purpose of measuring robot assembly system performance: 

“Kit loading and unloading has the advantage of offering well-defined, transportable test artifacts. A 

manufacturing assembly challenge might consist of a suitcase-style case that folds open with hinges that 

allow the two sides to be disconnected. In one half would be a set of parts of specified sizes and shapes in 

holders designed for each. The other half would be a bin. There could also be a mat with outlined spots 

for each part. The challenge kit would support a number of tasks. All the parts could be dumped in the bin 

side and then picked and placed onto the kit side in the designated locations. Alternatively, the kit side 

could be unloaded part by part into the bin. For evaluating robot precision in grasping, the kit side could 

be unloaded onto the mat to match the outlines (there are no supporting sides to guide a part.) The parts 

could be varied to provide both a recognition task and an orientation and gripping task, and the parts 

could vary in difficulty of recognition and gripping. For mobile manipulators, the bin and kit could be 

separated. The parts could be designed with different geometries, such as prismatic, cylindrical, or ovoid, 

so that some are easier for pose calculations and some are harder. For single-arm robot systems, the 

assembly should either have, or come with, a stable base to build on. Beyond simple parts handling, the 

kit could be assembled with or without human help. The parts could be designed for various assembly 

operations, such as peg-in-hole with gravity to hold them in place, screw threads, etc., and with different 

levels of difficulty. Increased dimensional tolerance could intentionally be added to the base parts to make 
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assembly easier, but also to prevent the robot from using dead reckoning for assembly. Over time, the kits 

could be made more complex.” 

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7. Appendix 

a. Terminology 

This terminology was extracted from [Monkman, 2007] and [Nof, 1999]. The source for each term below is 

designated as either [1] or [2] respectively. The terminology extracted was chosen based on how well the 

definition fit the context of this document. In some cases, there are duplicate definitions for the same term. 

Accuracy[2]: The ability of a robot to position its end-effector at a programmed location in space. Accuracy is 

characterized by the difference between the position to which the robot tool-point automatically goes and the 

originally taught position, particularly at nominal load and normal operating temperature. 

Actuator[2]: A motor or transducer that converts electrical, hydraulic, or pneumatic energy into power for motion 

or reaction. 

Adaptive Control[2]: A control method used to achieve near-optimum performance by continuously and 

automatically adjusting control parameters in response to measured process variables. Its operation is in the 

conventional manner of a machine tool or robot with two additional components: 

1. At least one sensor which is able to measure working conditions; and 

2. A computer algorithm which processed the sensor information and sends suitable signals to correct the 

operation of the conventional system 

Artificial Intelligence (AI) [2]: The ability of a machine system to perceive anticipated or unanticipated new 

conditions, decide what actions must be performed under the conditions, and plan the actions accordingly. 

Assembly (Robotic) [2]: Robot manipulation of components resulting in a finished assembled product. 

Assembly Constraints[2]: Logical conditions that determine the set of all feasible assembly sequences for a given 

product. Assembly constraints can be of two types: geometric precedence constraints (those arising from the part 

geometry) and process constraints (those arising from assembly process issues). 

Astrictive gripper[1]: A binding force produced by a field is astrictive. This field may take the form of air 

movement (vacuum suction), magnetism or electrostatic charge displacement. 

Automation[2]: Automatically controlled operation of an apparatus, process, or system by mechanical or 

electronic devices that replace human observation, effort, and decision. 

Basic jaw (universal jaw) [1]: The part of an impactive gripper subjected to movement. An integral part of the 

gripper mechanics, the basic jaw is not usually replaceable. However, the basic jaws may be fitted with additional 

fingers in accordance with specific requirements. 

Basic unit[1]: Basic module containing all gripper components which is equipped for connecting (flange, hole 

pattern) the gripper to the manipulator. The connecting capability implies a mechanical, power, and information 

interface. 

Chemoadhesion[1]: Contigutive prehension force by means of chemical effects (usually in the form of an 

adhesive). 
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Closed –Loop Control[2]: The use of a feedback loop to measure and compare actual system performance with 

desired performance. This strategy allows the robot control to make any necessary adjustments. 

Compliance[2]: A feature of a robot which allows for mechanical float in the tooling in relation to the robot tool 

mounting plate. This feature enables the correction of misalignment errors encountered when parts are mated 

during assembly operations or loaded into tight-fitting fixture or periphery equipment. 

Compliant Assembly[2]: The deliberate placement of a known, engineered, and relatively large compliance into 

tooling in order to avoid wedging and jamming during rigid part assembly. 

Contigutive Gripper[1]: Contigutive means touching. Grippers whose surface must make direct contact with the 

objects surface in order to produce prehension are termed contigutive. Examples include chemical and thermal 

adhesion. 

Compliant Support[2]: In rigid part assembly, compliant support provides both lateral and angular compliance 

for at least one of the mating parts. 

Contact Sensor[2]: A grouping of sensors consisting of tactile, touch, and force/torque sensors. A contact sensor 

is used to detect contact of the robot hand with external objects. 

Control System (Gripper) [1]: In most of the cases a relatively simple control component for analyzing of preprocessing sensor information for regulation and/or automatic adjustment of prehension forces. 

Conveyor Tracking Robot[2]: A robot synchronized with the movement of a conveyor. Frequent updating of the 

input signal of the desired position on the conveyor is required. 

Degrees of Freedom[2]: The number of independent ways the end-effector can move. It is defined by the number 

of rotational or translational axes through which motion can be obtained. Every variable representing a degree of 

freedom must be specified if the physical state of the manipulator is to be completely defined. 

Dextrous hand[1]: Anthropoidal artificial hand (rarely for industrial use), which is equipped with three or more 

jointed fingers and may be capable of sophisticated, programmed or remote controlled operations. 

Disassembly[2]: the inverse of the assembly process, in which products are decomposed into parts and 

subassemblies. In product remanufacturing the disassembly path and the termination goal are not necessarily 

fixed, but rather are adapted according to the actual product condition. 

Double grippers[1]: Two grippers mounted on the same substrate, intended for the temporal and functional 

prehension of two objects independently. 

Drive system[1]: A component assembly which transforms the applied (electrical, pneumatic, hydraulic) energy 

into rotary or translational motion in a given kinematic system. 

Dual Grippers[1]: Tow grippers mounted on the same substrate, intended for the simultaneous prehension of two 

objects. 

Electroadhesion[1]: prehension force by means of an electrostatic field. 

End-Effector[1] [2]: Also known as end-of-arm tooling or, more simply, hand. 
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Generic term for all functional units involved in direct interaction of the robot system with the 

environment or with a given object. These include grippers, robot tools, inspection equipment and other parts at 

the end of a kinematic chain. 

The subsystem of an industrial robot system that links the mechanical portion of the robot (manipulator) 

to the part being handled or worked on and gives the robot the ability to pick up and transfer parts and/or handle a 

multitude of differing tools to perform work on parts. It is commonly made up of four distinct elements: a method 

of attachment of the hand or tool to the robot tool mounting plate, power for actuation of tooling machines, 

mechanical linkages, and sensors integrated into the tooling. Examples include grippers, paint spraying nozzles, 

welding guns, and laser gauging devices. 

End-Effector, Turret[2]: A number of end-effectors, usually small, that are mounted on a turret for quick 

automatic change of end-effectors during operation. 

Endpoint Control[2]: Control wherein the motions of the axes are such that the endpoint moves along a prespecified type of path line (straight line, circle, etc.) 

Endpoint Rigidity[2]: The resistance of the hand, tool, or endpoint of a manipulator arm to motion under applied 

force. 

Error-Absorbing Tool[2]: A type of robot end-effector able to compensate for small variations in position and 

orientation. Especially suitable for assembly tasks, where the insertion of components demands tight tolerance 

positioning and orientation of the parts. (See also Remote Center Compliance device). 

Extended jaw[1]: An (optional) additional jaw situated at the end of an impactive gripper finger. It may, in 

preference to the finger itself, be modified to fit the profile of the object and it may be replaceable. 

External Sensor[2]: A feedback device that is outside the inherent makeup of a robot system, or a device used to 

effect the actions of a robot system that are used to source a signal independent of the robot’s internal design. 

Fixture[2]: A device used for holding and positioning a workpiece without guiding the tool. 

Flexibility (Gripper)[2]: The ability of a gripper to conform to parts that have irregular shapes and adapt to parts 

that are inaccurately oriented with respect to the gripper. 

Flexible Fixturing[2]: Fixture systems with the ability of accommodating several part types for the same type of 

operation. The fixture can be robotic and change automatically according to sensor input detecting the part 

change. 

Flexible Fixturing Robots[2]: Robots working in parallel, designed to hold and position parts on which other 

robots or people or automation can work. 

Force Control[2]: A method of error detection in which the force exerted on the end-effector is sensed and fed 

back to the controller, usually by mechanical, hydraulic or electric transducers. 

Force-Torque Sensors[2]: The sensors that measure the amount of force and torque exerted b y the mechanical 

hand along three hand-referenced orthogonal directions and applied around a point ahead and away from the 

sensors. 

Geometric Dexterity[2]: The ability of the robot to achieve a wide range of orientations of the hand with the tool 

center point in a specified position. 
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Grasp Planning[2]: A capability of a robot programming language to determine where to grasp object in order to 

avoid collisions during grasping or moving. The grasp configuration is chosen so that objects are stable in the 

gripper. 

Gripper[1] [2]: The generic term for all prehension devices whether robotic or otherwise. Loosely defined in four 

categories: Impactive, Astrictive, Ingressive and Contigutive. 

The grasping hand of the robot which manipulated objects and tools to fulfill a given task. 

Gripper axis[1]: A frame with its origin in the TCP(Tool Center Point). This coordinate system is used to specify 

the gripper orientation. 

Gripper Changing System[1]: A module for rapid manual, but in most cases automatic, exchange of an endeffector using a standard mechanical interface. In doing so, all power and control cables must be disconnected 

and reconnected. 

Gripper Design Factors[2]: Factors considered during the design of a gripper in order to prevent serious damage 

to the tool or facilitate quick repair and alignment. The factors include: parts’ or tools’ shape, dimension, weight, 

and material; adjustment for realignment in the x and y direction; easy-to-remove fingers; mechanical fusing 

(shear pins, etc.); locating surface at the gripper-arm interface; spring loading in the z(vertical) direction; and 

specification of spare gripper fingers. 

Gripper External[2]: a type of mechanical gripper used to grasp the exterior surface of an object with closed 

fingers. 

Gripper, Internal[2]: A type of mechanical gripper used to grip the internal surface of an object with open 

fingers. 

Gripper Finger[1]: Rigid, elastic, or multi-link grasping organ to enclose or clasp the object to be handled. 

Gripper Hand (Hand Unit) [1]: Grippers with multiple jointed fingers, each of them representing an open 

kinematic chain and possessing a high degree of freedom with f joints. 

Gripper jaw[1]: The part of the gripper to which the fingers are normally attached. The jaw does not necessarily 

come into contact with the object to be gripped. Note: in some cases gripper fingers may be fitted with an 

additional small (extended) jaw at their ends. 

Gripper, Soft[2]: A type of mechanical gripper which provides the capability of conforming to part of the 

periphery of an object of any shape. 

Gripper, Swing Type[2]: A type of mechanical gripper which can move its fingers in a swinging motion. 

Gripper, Translational[2]: a type of mechanical gripper which can move its own fingers, keeping them parallel. 

Gripper, Universal[2]: A gripper capable of handling and manipulating many different object of varying weights, 

shapes, and materials. 

Gripping Area[1]: The area of the prehension (gripper jaw) across which force is transmitted to the object 

surface. The larger the contact surface area of an impactive gripper, the smaller the pressure on the object surface. 

Gripping Surface(s) [1] [2]: The passive contact surface between object and gripper, i.e., the surface which is 

subjected to prehension forces. 
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The surfaces, such as the inside of the fingers, on the robot gripper or hand that are used for grasping. 

Hand (Robot’s) [2]: A fingered gripper sometimes distinguished from a regular gripper by having more than three 

fingers and more dexterous finger motions resembling those of the human hand. 

Hand Coordinate System[2]: A robot coordinate system based on the last axis of the robot manipulator. 

Handchanger[2]: A mechanism analogous to a tool changer on a machining center or other machine tool. It 

permits a single robot arm to equip itself with a series of task-specific hands or grippers. 

Hard Tooling[2]: Traditional tooling where every part to be processed in the robotic cell has its own fixtures and 

tools. It results in increased changeover time and processing delays. 

Holding system[1]: A term often used for an active prehension system including a gripper, jaws and fingers. It 

may also be apply to a passive temporary retaining device. 

Impactive gripper[1]: A mechanical gripper whereby prehension is achieved by impactive forces, i.e. forces 

which impact against the surface of the object to be acquired. 

Ingressive gripper[1]: Ingression revers to the permeation of an objects surface by the prehension means. 

Ingression can be intrusive (pins) or non intrusive (hook and loop) 

Inspection (Robotic) [2]: Robot manipulation and sensory feedback to check the compliance of a part or 

assembly with specifications. In such applications robots are used in conjunction with sensors, such as a video 

camera, laser, or ultrasonic detector, to check part locations, identify defects, or recognize parts for sorting. 

Jamming[2]: In part assembly, jamming is a condition where forces applied to the part for part mating point in the 

wrong direction. As a result, the part to be inserted will not move. 

Joint Geometry Information[2]: Geometry data for the mating of the parts to be joined, assembled, or welded. 

Kinematic Chain[2]: Pertaining to manipulator. The combination of rotary and/or translational joints, or axes of 

motion. 

Kinematic System[1]: Pertaining to end-effector. Mechanical unit (gear) converting drive motion of the prime 

mover into prehension action (jaw motion) with characteristic transmission rates for velocities and forces. The 

most often used kinematic components are lever, screw, and toggle lever gears. The gear determines the final 

velocity of the jaw movement, and the gripping force characteristics. Grippers without moving elements require 

no kinematics. 

Magnetoadhesion[1]: Prehension force by means of a magnetic field (permanent or electrically generated). 

Main Reference[2]: A geometric reference which must be maintained throughout a production process. The 

compliance with the references of the component elements of a subassembly guarantees the geometry of the 

complete assembly. 

Manipulation (Robotic) [2]: The handling of objects, by moving, inserting, orienting, twisting, and so on, to be in 

the proper position for machining, assembling, or some other operation. In many cases it is the tool that is being 

manipulated rather than the object being processed. 
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Material Handling (Robotic) [2]: the use of the robot’s basic capability to transport objects. It is common to find 

robots performing material-handling tasks and interfacing with other material-handling equipment such as 

containers, conveyors, guided vehicles, monorails, automated storage/retrieval systems, and carousels. 

Mechanical Grip Devices[2]: The most widely used type of end-of-arm tooling in parts-handling applications. 

Pneumatic, hydraulic, or electrical actuators are used to generate a holding force which is transferred to the part 

via linkages and fingers. Some devices are able to sense and vary the grip force and grip opening. 

Minimal Precedence Constraint (MPC) Method[2]: A method for the generation of assembly sequences based 

on the identification of geometric precedence constraints that implicitly represent all geometrically feasible 

assembly sequences. The minimal precedence constraint for an assembly component is defined as the alternative 

assembly states that will prevent the assembly of this component. 

Mounting Plate[2]: The means of attaching end-of-arm tooling to an industrial robot. It is located at the end of 

the last axis of motion on the robot. The mounting plate is sometimes used with an adapter plate to enable the use 

of a wide range of tools and tool power sources. 

Multi-gripper System[2]: A robot system with several grippers mounted on a turret-like wrist, or capable of 

automatically exchanging its gripper with alternative grippers, or having a gripper for multiple parts. A type of 

mechanical gripper enabling effective simultaneous execution of two or more different jobs effectively. 

Multiple grippers[1]: Several grippers mounted on the same substrate, intended for the simultaneous prehension 

of more than two objects. 

Multi-hand Robot Systems[2]: A class of robotic manipulators with more than one end-effector, enabling 

effective simultaneous execution of two or more different jobs. Design methods for each individual hand in a 

multi-hand system are similar to those of single hands, but must also consider the other hands 

Noncontact Sensor[2]: A type of sensor, including proximity and vision sensors, that functions without any direct 

contact with objects. 

Orientation[2]: Also known as positioning. The consistent movement or manipulation of an object into a 

controlled position and attitude in space. 

Orientation Finding[2]: The use of a vision system to locate objects so they can be grasped by the manipulator or 

mated with other parts. 

Palletizing/Depalletizing[2]: A term used for loading/unloading a carton, container, or pallet with parts in 

organized rows and possibly in multiple layers. 

Part Mating[2]: The action of assembling two parts together according to the assembly design specifications. It 

occurs in four stages: approach, chamfer crossing, one-point contact, and two-point contact. 

Part Mating Theory[2]: Predicts the success or failure mode of the assembly of common geometry parts, such as 

round pegs and holes, screw threads, gears, and some simple prismatic part shapes. Two common failure modes 

during two-point contact are wedging and jamming. 

Payload[2]: The maximum weight that a robot can handle satisfactorily during its normal operations and 

extensions. 
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Peripheral Equipment[2]: The equipment used in conjunction with the robot for a complete robotic system. This 

equipment includes grippers, conveyors, part positioners, and part or material feeders that are needed with the 

robot. 

Photoelectric Sensors[2]: A register control using a light source, one or more phototubes, a suitable optical 

system, and amplifier, and a relay to actuate control equipment when a change occurs in the amount of light 

reflected from a moving surface due to register marks, dark areas of a design, or surface defects. 

Pick-and-Place[2]: A grasp-and-release task, usually involving a positioning task. 

Pneumatic Pickup Device[2]: The end-of-arm tooling such as vacuum cups, pressurized bladders, and 

pressurized fingers. 

Pose[2]: The robot’s joints position for a particular end-effector position and orientation within the robot’s 

workspace. Specific positions are named according to the tasks the robot is performing; for example, the home 

pose, which indicates the resting position of the robot’s arm. 

Position Control[2]: a control by a system in which the input command is the desired position of a body. 

Position Finding[2]: The use of a vision system to locate objects so they can be grasped by a manipulator or 

mated with other parts. 

Positioners[2]: Also known as positioning table, positioners are fixture devices for locating the parts to be 

processed n the required position and orientation. Positioners can be implemented as hard tooling devices or 

reprogrammable robotic devices which reduce the setup time and part changeover times. For instance, 

positioners are used in robotic arc welding to hold and position pieces to be welded. The movable axes of the 

positioner are sometimes considered additional robot axes. The robot controller controls all axes in order to 

present the seam to be welded by the robot’s torch in the location and orientation taught or modified by adaptive 

feedback, or changes inserted by the operator, dynamically during execution. 

Precision (Robot) [2]: A general concept reflecting the robot’s accuracy, repeatability, and resolution. 

Prehendability[1]: The suitability of an object to be automatically gripped. Dependant on the surface properties, 

weight and strength when exposed to prehension forces. This property can sometimes be enhanced by applying 

such surfaces or elements (handling adapters) which are required only for a particular purpose. 

Prehension[1]: The act of acquiring and object in or onto the gripper. (we must modify some of these terms 

based on our chosen vocabulary (gripper/end-effector and object/workpiece) 

Prehension planning[1]: Deals with the problem of how to ensure stable mating between robot gripper and 

workpiece. A prehension strategy must be chosen in such a way that it can be accomplished in a stable manner 

and collision free. Post prehension misalignment of the object is undesirable. In many circumstances, special 

constraints must be observed in order to avoid contact with certain parts of the object (forbidden zones) 

Prehension systems[1]: Complete systems including grippers supplemented with additional units (subsystems), 

e.g., rotation, pivot and short-travel units, changing systems, joining (adjustment) tools, collision and overload 

protection mechanisms, measuring devices and other sensors. 

Pressurized Bladder[2]: A pneumatic pickup device which is generally designed especially to conform to the 

shape of the part. The deflated bladder is placed in or around the part. Pressurized air causes the bladder to 
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expand, contact the part, and conform to the surface of the part, applying equal pressure to all points of the 

contacted surface. 

Pressurized Fingers[2]: A pneumatic pickup device that has one straight half, which contacts the part to be 

handled, one ribbed half, and a cavity for pressurized air between the two halves. Air pressure filling the cavity 

causes the ribbed half to expand and “wrap” the straight side around a part. 

Prosthetic Robot[2]: A programmable manipulator or device that substitutes for lost functions of human limbs. 

Protection system[1]: These are elements attached to the inner or outer part of the gripper which are activated in 

case of overload or collision in order to protect the robot and gripper from damage (warning signal, emergency 

stop activation, passive or active evasive movement). 

Proximity Sensor[2]: A device which senses that an object is only a short distance away and /or measures how 

far away it is. Proximity sensors typically work on the principles of triangulation of reflected light, elapsed time 

for the reflected sound, intensity-induced eddy currents, magnetic fields, back pressure from air jets, and others. 

Repeatability[2]: The envelope of variance of the robot tool point position for repeated cycles under the same 

conditions. It is obtained from the deviation between the positions and orientations reached at the end of several 

similar cycles. 

Resolution[2]: The smallest incremental motion which can be produced by the manipulator. It serves as one 

indication of the manipulator accuracy. Three factors determining the resolution: mechanical resolution, control 

resolution, and programming resolution. 

Retention[1]: Pertains to the post prehension status of an object already held in the gripper. Note: prehension and 

retention forces are not always equal. 

Retro-reflective Sensing[2]: A photoelectric source consolidation method based on the aiming of the light beam 

into a white retro target feeding a photoelectric sensor. 

Rigidity[2]: The property of a robot to retain its stiffness under loading and movement. Rigidity can be improved 

by features such as a cast-iron base, precision ball screws on all axial drives, ground and hardened spiral bevel 

gears in the wrist, brakes on the least stiff axes, and end-effector design that permits a workpiece or tool to be held 

snugly. 

Robot Task[2]: Specification of the goals for the positioning of the object being manipulated by the robot, 

ignoring the motions required by the robot to achieve these tasks. 

Robotic Assembly[2]: the combination of robots, people, and other technologies for the purpose of assembly in a 

technologically and economically feasible manner. Robotic assembly offers an alternative with some of the 

flexibility of people and the uniform performance of the fixed automation. 

Robotic Fixturing[2]: a programmable fixture system that can accommodate a set of parts for processing in the 

same workcell. 

Search Routine[2]: A robot function that searches for a precise location when it is not known exactly. An axis or 

axes move slowly in one direction until terminated by an external signal. It is used in stacking and unstacking of 

parts, locating workpieces, or inserting parts in holes. 
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Secondary References[2]: Geometric references used in assembling a main assembly component. The 

compliance with the references of the components guarantees the correct geometry of the completed assembly. 

Selective Compliance Assembly Robotic Arm (SCARA) [2]: A horizontal-revolute configuration robot designed 

at Japan’s Yamanachi University. The tabletop-size arm with permanently tilted, high-stiffness links sweeps 

across a fixtured area and is especially suited for small-parts insertion tasks in the vertical (z) direction. 

Sensing[2]: The feedback from the environment of the robot which enables the robot to react to its environment. 

Sensory inputs may come from a variety of sensor types, including proximity switches, force sensors, and 

machine vision systems. 

Sensor Coordinate System[2]: A coordinate system mounted over the workspace of the robot and assigned to a 

sensor. 

Sensor Fusion (Sensor Integration) [2]: The coordination and integration of data from diverse sources to 

produce a usable perspective for a robotics system. A large number of sensors can be applied, and the 

information they gather from the work environment or workpiece is analyzed and integrated in a unique 

meaningful stream of feedback date to the robotic manipulator. 

Sensor System[1] [2]: Sensors pertinent to the task or prehension. This may include sensors built into the endeffector, possibly with integrated data pre-processing, for position detection, registration of object approach, 

determination of gripping force, path and angle measurements, slippage detection etc. 

The components of a robot system which monitor and interpret events in the environment. Internal 

measurement devices, also considered sensors, are part of closed axis-control loops and monitor joint position, 

velocity, acceleration, wrist force, and gripper force. External sensors update the robot model and are used for 

approximation, touch, geometry, vision, and safety. A data acquisition system uses data from sensors t o generate 

patterns. A data processing system then identifies the patterns and generates frames for the dynamic world-model 

processor. 

Sensor Glove[2]: A robotics sensor capable of precision measurement of human gestures, with applications in 

surgery and telerobotics. 

Sensory-Controlled Robot[2]: Also known as intelligent robot. A robot whose program sequence can be 

modified as a function of information sensed from its environment. The robot can be served or non-servoed. 

Slip Sensors[2]: Sensors that measure the distribution and amount of contact area pressure between hand and 

objects positioned tangentially to the hand. They may be single-point, multiple-point (array), simple binary (yesno), or proportional sensors. 

Sorting (Robotic) [2]: The integrated operation of a sensor system and a robot for the discrimination of two or 

more types of workpieces. 

Sucker[1]: Normally refers to a passive suction element (disk, cap or cup) which does not require active vacuum 

suction but relies on the evaluation of air by distortion of the element against the object surface. 

Suction head[1]: A form of astrictive gripper which may consist of one or more vacuum suction elements (discs, 

caps or cups) from which air is actively evacuated by means of externally generated negative pressure. 

Synchronization[1]: very specific, not sure if we need it. 
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Tactile Sensing[2]: The detection by a robot through contact by touch, force, pattern slip, and movement. Tactile 

sensing allows for the determination of local shape, orientation and feedback forces of a grasped workpiece. 

Thermoadhesion[1]: Contigutive prehension force by means of thermal effects (usually in the form of freezing or 

melting). 

Tool Center Point (TCP) [1][2]: Working pint at the end of a kinematic chain. The TCP serves also as a 

programmed reference point for an end effector and as a rule determines the origin of the tool frame. A 

coordinate system whose origin coincides with the TCP is called the tool frame. Multiple gripper heads may 

possess several TCPs or one main TCP with the rest being defined relative to the main TCP by tool offsets. 

A tool-related reference point that lies along the last wrist axis at a user-specified distance from the wrist. 

Tool Coordinate System[2]: A coordinate system assigned to the end-effector. 

Tool-Coordinate programming[2]: Programming the motion of each robot axis so that the tool held by the root 

gripper is always geld normal to the work surface. 

Torque Control[2]: A method to control the motions of a robot driven by electric motors. The torque produced 

by the motor is treated as an input to the robot joint. The torque value is controlled by the motor current. 

Torque/Force Controller[2]: A control system capable of sensing forces and torques encountered during 

assembly or movement of objects, and /or generating forces on joint torques by the manipulator which are 

controlled to reach desired levels. 

Touch Sensors[2]: Sensors that measure the distribution and amount of contact area pressure between hand and 

objects perpendicular to the hand. Touch sensors may be single point, multiple-point (array), simple binary (yesno), or proportional sensors, or may appear in the form of artificial skin. 

Tracking[2]: A continuous position-control response to continuously changing input requirements. 

Tracking (Line) [2]: The ability of a robot to work with continuously moving production lines and conveyors. 

Moving-base line tracking and stationary-base line tracking are the two methods of line tracking. 

Tracking Sensor[2]: Sensors used by the robot to continuously adjust the robot path in real time while it is 

moving. 

Vacuum Cups[2]: A type of pneumatic pickup device which attaches to parts being transferred via a suction of 

vacuum pressure created by a venturi or a vacuum pump. 

Vision System[2]: A camera (or cameras) system interfaced to guide a robot to locate a part, identify it, direct the 

gripper to a suitable grasping position, pickup the part, and bring it to the work area. A coordinate transformation 

between the cameras and the robot must be carried out to enable proper operation of the system. 

Wedging[2]: In rigid part assembly, a condition where two-point contact occurs too early in part mating, leading 

to the part that is supposed to be inserted appearing to be stuck in the hole. Unlike jamming, wedging is caused 

by geometric rather than ill-proportioned applied forces. 

Workpiece or object[1]: A general term which refers to the component of object to be prehended or which is 

already under prehension by the gripper. 
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Wrist[2]: A set joints, usually rotational, between the arm and the hand or end-effector, which allow the hand or 

end-effector to be oriented relative to the workpiece. 

Wrist force Sensor[2]: A structure with some compliant sections and transducers that serve as force sensors by 

measuring the deflections of the compliant sections. The types of transducers used are strain-gauge, piezoelectric, 

magnetostrictive, and magnetic. 

b. Industrial Robot System Standards 

This section includes a list of standards from the normative and bibliography references listed in ISO 10218; 

ANSI/RIA R15.06 and [Moon, 2009]. 

ISO 4413, Hydraulic fluid power — General rules relating to systems 

ISO 4414, Pneumatic fluid power — General rules relating to systems 

ISO/IEC Guide 51, Safety aspects — Guidelines for their inclusion in standards 

ISO 7000, Graphical symbols for use on equipment — Index and synopsis 

ISO 8373:1994, Manipulating industrial robots — Vocabulary 

ISO 9283:1998, Manipulating industrial robots — Performance criteria and related test methods 

ISO 9409 (all parts), Manipulating industrial robots — Mechanical interfaces 

ISO 9946, Manipulating industrial robots — Presentation of characteristics 

ISO 10218-1, Robots and robotic devices — Safety requirements — Part 1: Industrial robot 

ISO 10218-2, Robots and robotic devices — Safety requirements — Part 2: Industrial robot system and 

integration 

ISO 11161, Safety of machinery — Industrial automation systems — Safety of integrated manufacturing 

systems — Basic requirements 

ISO 11593:1996 Manipulating industrial robots --Automatic end effector exchange systems -- 

Vocabulary/presentation of characteristics 

ISO 12100:2010 Safety of machinery -- General principles for design -- Risk assessment and risk reduction 

ISO/TR 13309:1995 Manipulating industrial robots -- Informative guide on test equipment and metrology 

methods of operation for robot performance evaluation in accordance with ISO 9283 

ISO 13849-1:2006, Safety of machinery — Safety-related parts of control systems — Part 1: General 

principles for design 

ISO 13850, Safety of machinery — Emergency stop — Principle for design 

ISO 13851, Safety of machinery — Two-hand control devices — Functional aspects and design principles 

ISO 13854, Safety of machinery — Minimum gaps to avoid crushing of parts of the human body 

ISO 13855, Safety of machinery — Position of protective equipment with respect to the approach speeds of parts 

of the human body 

ISO 13856-1, Safety of machinery — Pressure-sensing protective devices — Part 1: General principles for design 

and testing of pressure-sensitive mats and pressure-sensitive floors 

ISO 13857, Safety of machinery — Safety distances to prevent danger zones being reached by the upper 

limbs and lower limbs 

ISO 14118, Safety of machinery — Prevention of unexpected start-up 

ISO 14119, Safety of machinery — Interlocking devices associated with guards — Principles for design and 

selection 

ISO 14120, Safety of machinery — Guards — General requirements for the design and construction of fixed and 

movable guards 

ISO/TR 11688-1:1995 Acoustics — Recommended practice for the design of low-noise machinery and 

equipment — Part 1: Planning 

ISO 14123, Safety of machinery - Reduction of risks to health from hazardous substances emitted by 
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machinery 

ISO 14159 Safety of machinery - Hygiene requirements for the design of machinery 

ISO 14539:2000 Manipulating industrial robots --Object handling with grasp-type grippers -- Vocabulary and 

presentation of characteristics 

ISO/TS 15066 - Technical specification on collaborative workspace (under elaboration) 

ISO 19353, Safety of machinery - Fire prevention and protection 

IEC 60204-1:2005, Safety of machinery — Electrical equipment of machines — Part 1: General requirements 

IEC 60364-7-729, Low-voltage electrical installations — Part 7-729: Requirements for special installations or 

locations — Operating or maintenance gangways 

IEC 61000‐6‐2, Electromagnetic compatibility (EMC) — Part 6‐2: Generic standards — Immunity for industrial 

environments 

IEC 61000‐6‐4, Electromagnetic compatibility (EMC) — Part 6: Generic standards — Section 4: Emission 

standard for industrial environments 

IEC 61496-1, Safety of machinery — Electro-sensitive protective equipment 

IEC 61496‐2, Safety of machinery — Electro‐sensitive protective equipment — Part 2: Particular requirements 

for equipment using active opto‐electronic protective devices (AOPDs) 

IEC 61800-5-2 Adjustable Speed Electrical Power Drive Systems 

IEC 62061:2005, Safety of machinery — Functional safety of safety-related electrical, electronic and 

programmable control systems 

ISO/CIE 8995-1, Lighting of work places — Part 1: Indoor 

EN 563, Safety of machinery - Temperatures of touchable surfaces - Ergonomics data to establish 

temperature limit values for hot surfaces 

EN 1093, Safety of machinery - Evaluation of the emission of airborne hazardous substances 

EN 1127, Explosive atmospheres - Explosion prevention and protection 

EN 12198, Safety of machinery - Assessment and reduction of risks arising from radiation emitted by 

machinery 

CEN/TR 14715, Safety of machinery - Ionizing radiation emitted by machinery - Guidance for the 

application of technical standards in the design of machinery in order to comply with legislative 

requirements 

BGIA/DGUV study - Procedural Guideline for the Arrangement of Workplaces with Collaborative 

Robots 

NFPA 70E - 2009 - Standard for Electrical Safety in the Workplace – revised to address safety gaps and increase 

electrical worker protection from Shock, electrocution, arc flash, and arc blast. 

ANSI/NFPA 79-1997 - Electrical Standard for Industrial machinery 

ANSI/UL 1740-1998 - Safety Standard for robots and robotic equipment 

OSHA 1904 - General requirement for recording and reporting occupational injuries and illnesses 

OSHA 1910.147-Control of hazardous energy (Lockout/Tagout) 

OSHA 1910.212 - General requirements for all machines (Machine guarding) 

OSHA 1910.219 - Mechanical power transmission apparatus 

ANSI B11.19-1990 (R1996), Safeguarding performance criteria 

ANSI B11.20-1991 (R1996), Safety requirements for flexible manufacturing systems/cells 

ANSI Z49.1 - 1994, Safety in welding, cutting and allied processes 

ANSI Z136.1-1993, Safe use of lasers 

ANSI Z244.1-1982 (R1993), Safety Requirements for the Lock Out/Tag Out of Energy Sources 

ANSI Z535.1 - 1998, Safety Color Code 

ANSI Z535.2-1998, Environmental and Facility Safety Signs 

ANSI Z535.3-1998, Criteria for Safety Symbols and Labels 

ANSI Z535.4-1998, Product Safety Signs and Labels 

ANSI Z535.5-1998, Accident Prevention Tags (for Temporary Hazards) 

ANSI/ASME B15.1-1992, Safety Standards for Mechanical Power Transmission Apparatus 
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ANSI/AWS D16.2-1994, Components of Robotic and Automatic Welding 

ANSI/UL508-1988, Industrial Control Equipment 

ANSI/UL969-1991, Standard for safety-marking and labeling systems 

UL 991 - Tests for safety-related controls employing solid-state devices 

UL 1998-Safety-related software 

c. Design for Assembly – Boothroyd-Dewhurst Method 

The Boothroyd-Dewhurst Design for Assembly (DfA) evaluation [AMI, 2011] centers on establishing the cost of 

handling and inserting component parts. The process (see Figure 24) can be applied to manual or automated 

assembly, which is further subdivided into high-speed dedicated or robotic. An aid to the selection of the 

assembly system is also provided by a simple analysis of the expected production volume, payback period 

required, number of parts in the assembly, and number of product styles. 

Regardless of the assembly system, parts in the assembly are evaluated in terms of ease of handling and ease of 

insertion, and a decision is made as to the necessity of the part in question. The findings are then compared to 

synthetic data, and from this a time and cost are generated for the assembly of that part. The opportunity for 

reducing this is found by examining each part in turn and identifying whether each exists as a separate part for 

fundamental reasons. These fundamental reasons are: 

 During operation of the product, does the part move relative to all other parts already assembled? 

 Must the part be of a different material from all other parts already assembled? Or isolated from them? 

 Must the part be separate from all those already assembled because otherwise necessary assembly or 

disassembly of other separate parts would be impossible? 

Figure 24 - Boothroyd-Dewhurst Design for Assembly Method 

The second stage of the analysis is to examine the handling and insertion of each component part. For manual 

assembly, a two-digit handling code and a two-digit insertion code are identified from synthetic data tables. The 

tables categorize components with respect to their features for handling such as size, weight, and required amount 
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of orientation. For insertion, there are categories for part alignment, the type of securing method, and whether the 

part is secured on insertion or as a separate process. These codes are then cross-referenced to identify the time for 

that operation from the table. 

The codes and subsequent times are used to determine a number of metrics: 

 Assembly time (TM) is determined by summing the handling and insertion times 

 Assembly cost (CM) is proportional to TM by a factor that accounts for wage rate and overheads 

 Theoretical minimum number of parts (NM) is the summation of all those essential parts determined 

during the first stage 

 Design efficiency is defined as the ideal assembly time divided by the estimated assembly time 

 The ideal assembly time is given by 3NM, where the 3 represents a handling time of 1.5 seconds and 

insertion time of 1.5 seconds, for an ideal component. 

 The estimated assembly time is TM. 

Though costs and times are determined, care must be taken in the use of these values in an absolute sense. As with 

other techniques, values are best used for comparing redesigns. 

d. Types of Force Control Functions 

Table 5 lists types of force control functions and descriptions from the [Fanuc, 2007] force control sensor 

operator’s manual. Also, Figure 25 shows drawings of example assembly operations to better acquaint the reader 

with the types of assembly operations that can be accomplished using robotic force control. 
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Table 5 - Types of Force Control Functions 

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Figure 25 – Types of assembly functions currently accomplished using robotic force control. [Fanuc, 2007] 

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