NISTIR 8022

Literature Review of Mobile Robots 

for Manufacturing

Michael Shneier

Roger Bostelman

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8022
NISTIR 8022 

Literature Review of Mobile Robots 

for Manufacturing

Michael Shneier

Roger Bostelman

Intelligent Systems Division

Engineering Laboratory

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8022

May 2015

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director 
ii

Abstract

Mobile robots are devices that can move autonomously to accomplish their goals. This paper provides a 

review of such robots oriented towards manufacturing applications. It describes the kinds of mobile 

robots that are used and what criteria are appropriate when deciding to make use of mobile robots. It 

also covers ways of localizing the robots, controlling them, and addresses their safe use in collaborative 

applications with humans. The standards covering mobile robots are described and the paper ends with 

a brief survey of more advanced vehicles and applications.

Disclaimer: Commercial equipment and materials are identified in order to adequately specify certain 

procedures. In no case does such identification imply recommendation or endorsement by the National 

Institute of Standards and Technology, nor does it imply that the materials or equipment identified are 

necessarily the best available for the purpose.
iii

Table of Contents

Abstract......................................................................................................................................................... ii

Introduction ..................................................................................................................................................1

Human-Robot Interaction.............................................................................................................................3

Navigation and Localization..........................................................................................................................4

Planning and Coordination ...........................................................................................................................6

Performance Evaluation................................................................................................................................8

Standards for Industrial Mobile Robots........................................................................................................8

Advanced Applications and AGVs.................................................................................................................9

Summary and Conclusions..........................................................................................................................11

Bibliogaphy..................................................................................................................................................13
1

Introduction

While there is no generally-accepted definition for the term “mobile robot,” it is often taken to mean a 

device that can move autonomously from place to place to achieve a set of goals (see, e.g., Tzafestas 

[1]). Mobile robots are used in a wide range of applications including in factories (e.g., automated 

guided vehicles or AGVs), for military operations (e.g., unmanned ground reconnaissance vehicles), in

healthcare (e.g., pharmaceutical delivery), for search and rescue, as security guards, and in homes (e.g., 

floor cleaning and lawn mowing). Automated guided vehicles or automatic guided vehicles (AGVs) were

invented in 1953 [2]. AGVs are most often used in industrial applications to move materials around a 

manufacturing facility or a warehouse [3]. Typical AGV types are, as shown in Figure 1, tuggers (AGVs 

that pull carts), unit loaders (AGVs with onboard roller tables for parts-tray transfers), and fork trucks 

(robots similar to manual fork trucks). Use of mobile robots, and AGVs in particular, is growing as the 

range of robot applications in factories, hospitals, office buildings, etc. increases. While mobile robots 

can use a range of locomotion techniques such as flying, swimming, crawling, walking, or rolling, this 

paper focuses mainly on rolling or wheeled mobile robots. More advanced mobile robots are briefly 

discussed and referenced in the sections on Localizing the Mobile Robot and Advanced Applications. 

tugger unit load fork lift

Figure 1. Typical AGV types (tugger and unit load AGV photos courtesy of America In Motion)

This paper reviews research and applications on a range of topics of importance for implementing 

mobile robots and AGVs in manufacturing. These include planning, navigation, vehicle localization, and 

interactions between mobile robots and humans and between groups of mobile robots. It also covers a 

sampling of applications in real-world factories and provides a brief discussion of some advanced mobile 

robot concepts.

Mobile robots address the demand for flexible material handling, the desire for robots to be able to 

operate on large structures, and the need for rapid reconfiguration of work areas. Much of the earlier 

work on outdoor vehicles for defense, search and rescue, and bomb disposal is relevant to the 

manufacturing domain, as is work that has been done on personal care robots and robots for household 

and hospital applications. When a robot arm is added to the mobile robot, we term this a “mobile 

manipulator,” discussed briefly in the Advanced Applications section. 

Two roadmaps provide predictions for future mobile robot systems. For material handling, the Material 

Handling Institute [4] expects new capabilities in autonomous control, artificial intelligence, and 

robotics, along with motion- and gesture-sensitive technologies that could lead to systems in which 

humans, machines, and computers interact freely and effectively in completely new ways. By 2025, it is 

expected that economical, high-speed automation for loading and unloading trucks should be available, 

both at the carton and pallet level. For mobile robots used in manufacturing, a recent roadmap for U.S. 
2

robotics [5] predicts that by 2030, autonomous vehicles will be capable of driving in any environment in 

which humans can drive, and furthermore be safer and more predictable than a human driver. Vehicles 

will be able to learn on their own how to drive in previously unseen scenarios.

Criteria for Adoption

Mobile robots can be relatively expensive and may require significant expertise to install and operate. It 

is therefore important to ensure that their use in a particular application is appropriate. This need has 

led to the development of various criteria for evaluating the conditions under which mobile robots 

should be adopted. In their technology roadmap [6], Sabattini et al. first describe the process of 

installing and setting up AGVs in a factory. They then describe some of the barriers to greater adoption 

of the technology. These include cost, the fact that it is difficult to achieve the desired efficiency to make 

the introduction cost-effective and able to operate at the required task cadence, the lack of flexibility of 

current systems which makes changeovers expensive and time-consuming, and safety concerns. Cost 

also includes the need for an accurate localization system and developing routing plans and traffic 

management. Efficiency in using AGVs can be limited by poor routing, by having to reduce the speed of 

the vehicles (e.g., due to sharing the workspace with people), or by inadequate knowledge about the 

environment that causes poor paths to be selected or delays due to the vehicle having to react 

appropriately to changes (e.g., pallets of goods within the intended AGV path). Greater use of sensors 

can help to increase efficiency. Ways to increase the flexibility of the system are to reduce the need for 

infrastructure such as the targets needed for localization (e.g., by adopting alternative localization 

methods) and increasing the ability of the system to adapt to changes in its tasks (e.g., through models). 

Safety is of paramount importance but the means to assure it may impact efficiency and flexibility. 

Increasing the use of sensors and providing better semantic models of the task and environment can 

reduce these impacts.

Criteria for using mobile robots as assistants in industrial applications are provided in Angerer et al. [7]. 

The authors outline the required capabilities of a mobile robot (see Table 1). They also describe the 

characteristics of tasks that are suitable for their use. These include the presence of a frequentlychanging environment, handling loads with a mass higher than 5 kg (which causes ergonomic problems 

for people), the need to move components between a storage area and the work space, a wide variety 

of parts, and the ability to work interactively with people. They propose four main areas where 

improvements would enhance adoption and make AGVs easier to use. The first is safety, for which they 

advocate the use of sensors to detect and track the people in the workspace and changes in the robot’s 

behavior in response to the people’s actions. Another area for improvement is to make it easier and 

faster to react to changes in the process or task. This currently requires substantial reprogramming 

which the authors’ propose to replace with an automated approach described in Angerer et al. [8]. The 

third area they advocate is to provide substantially more information to the robot about its 

environment, both from a priori knowledge and from sensor inputs. The knowledge should include a 

model of the process and the interaction between the human and robot. The final requirement is that 

the human-robot collaboration feel natural and intuitive, which can be achieved with a model of the 

task and the use of sensors to help the robot understand the degree of completion. 

The authors provide a methodology for introducing mobile robots into manufacturing applications and 

an example of a real implementation of multiple part feeding. 
3

Table 1. Required capabilities for a mobile robot assistant in manufacturing environments (from [7]).

Industrial requirements for assistive robotics

Navigation Robustness in unstructured environment

Gripping technology (when used 

as a mobile manipulator) Applicability for different part geometries

Hardware components Economic components in compliance with industrial standards

Workload

20 kg (needed to help offload ergonomically undesirable tasks from 

people and to be able to handle typical loads)

Workspace

1.8 m square floor area (a typical size of a human-reachable work 

area)

Availability 99 %

Energy supply 24 hours

Safety

CE (European Commission) labeled application for man-machine 

interaction

Human-Robot Interaction

It is becoming more common for humans and robots to share a workspace. This has led to the need for 

improved human-robot communication and for awareness by the robot of what can be expected of the 

people around it and, similarly, by the people of what can be expected of the robots. An aspect of 

interaction with robots that is not unique to mobile robots is teaching them the tasks they are expected 

to accomplish. In Argall et al. [9], a method of teaching by demonstration is described, in which primitive 

components of motions are learned by a robot through teleoperation. The method is able to extrapolate 

from a set of basic motions to the development of a complete task without the user having to 

demonstrate all aspects of the task. Another approach is to use gestures to show the mobile 

manipulator what it should pick up or where it should go (Pedersen, et al. [10]). This requires the 

definition of gestures that are both easily communicated by humans and easily recognized and 

disambiguated by the sensors on the mobile robot. Some researchers have also investigated ways in 

which a robot can ask for help. Rosenthal and Veloso [11] describe a mobile robot that can navigate 

around an office environment but has no manipulator, so, for example, cannot push the elevator 

button. The robot has algorithms to enable it to find people and ask them for help, taking into account 

the imposition on the people it asks (the travel distance to the help location) and the robot’s own need 

for a short task completion time. Another issue to consider when people are in the environment is 

addressed by Sisbot et al. [12]. Here a planner is developed that computes paths that take into account 

the comfort and expectations of people that may be near the robot. The plan assures that the robot 

both keeps a safe distance from all people and tries to keep the robot in the field of view of the people 

to prevent surprise appearances.

Personal care robots have developed into advanced human-robot interactive systems. For example, 

Care-O-bot (Graf et al. [13]) is now in its third generation with characteristics that are potentially very 

useful to the industrial mobile robot community. Navigation is via odometry (measurements of vehicle 

motion) improved by simultaneous localization and mapping (SLAM) based on front and rear laser scan 

data that is compared with a global map. A three-level hierarchical controller includes single wheel 

control, four wheel control, and a trajectory planner to enable path planning around obstacles and 

through narrow passageways. The omni-directional mobile manipulator includes a tray and robot arm 

and can compute collision-free manipulation paths based on data from a color camera and light 
4

detection and ranging (LIDAR) sensors. The system also implements spatial segmentation for obstacle 

learning and interpretation of the three-dimensional cloud of points detected by the LIDAR sensors for 

object recognition. 

Navigation and Localization

Mobile robots often operate in large facilities and many different approaches have been taken for 

localization and navigation. They range from methods in which the entire facility is first mapped and 

routes are planned a priori to those in which sensors provide information about traversable areas and 

the vehicles determine their own current positions and plan their paths dynamically based on features 

recognized in the environment. There is typically a trade-off between a priori plans and dynamicallygenerated one. When there is not expected to be much change in the environment and cycle times are 

critical, a priori planning is usually preferred. When the workspace or the tasks change frequently it is 

often better to plan dynamically. Manufacturing facilities often take a middle road. Markers may be 

placed in the work area that are recognized by sensors on the vehicles and provide accurate localization 

through triangulation and thus simplify navigation. Other sensors on-board the vehicle look for obstacles 

or unexpected objects in the path of the vehicle and may be able to plan a way around them before 

returning to their pre-planned route. . It is also important to know the position and orientation (pose) of 

a mobile robot and many methods have been developed to provide this information. A commonly-used 

approach is to rely on odometry augmented by sensor-based measurements from lasers, radiofrequency identification (RFID) systems, two-dimensional bar codes (e.g., QR codes), cameras, etc. More 

advanced systems make use of algorithms that accomplish the localization and navigation tasks 

simultaneously. These systems are usually referred to as SLAM algorithms, for Simultaneous Localization 

And Mapping. By seeing the same features in multiple views using sensors that move with the vehicle, 

the algorithms can stitch together the sensor information. When this information is combined with the 

vehicle’s estimates of the positions at which the information was gathered, a local map can be 

constructed. Over time, the whole facility can be mapped and the maps can be used to plan the vehicle’s 

paths.

Localization and navigation of commercial AGVs is still commonly accomplished by wire guidance where 

induction is sensed from electrified wires embedded in the floor. It is now more common, however, to 

determine localization by laser triangulation methods, in which a spinning laser senses range and 

azimuth to wall-mounted reflectors. Several other localization methods are available on AGVs today as 

shown in Figure 3, including ceiling mounted bar codes, range or camera-based wall-following, using 

floor markers or magnets, and following magnetic tape. Azizi and Howard [14] describe some of the 

factors that reduce the effectiveness of odometry-based methods and ways of improving their

performance using models of the errors and of the vehicle. Floor spots or magnets are an extension of

wire guidance which use floor-embedded magnets to localize the AGV at the magnet and correct for 

odometry errors that accumulate between magnets. Wire guidance has been expanded to magnetic 

and chemical tape guidance. An example of mobile robots that use tape-based path sensing is discussed 

in Horan et al. [15]. These vehicles use cameras to view the floor tape. Similar research was performed 

at NIST to follow a lane having tape lines as boundary markers instead of a single center line. At the end 

of the lane, unique, temporary markers could be placed on the floor that would indicate to the vehicle

that it should use its perception system to navigate through unstructured environments to a particular 

endpoint. Ceiling-mounted bar codes are available as an alternative to laser triangulation and are used 

in large warehouses where center supports for reflectors may not be available. The unique bar codes 

are two-dimensional patterns read by an onboard camera and the system can determine the position of 
5

the vehicle with an uncertainty of approximately 5 cm. Range-based wall-following is typically used in 

confined spaces, such as during truck loading applications.

a b c

d e f

Figure 2. Various AGV localization methods in use today: a) laser triangulation, b) ceiling mounted bar codes, c) range or 

wall-following, d) floor spots/magnets, e) magnetic tape, and f) inductive wire (from [16]).

Biswas and Veloso [17] describe a fast algorithm for localizing a mobile robot using planes extracted 

from depth information that is projected into a 2D map of the environment. They make use of a 

Microsoft Kinect sensor that produces color images registered with range information. Since they are 

working indoors, they make use of the fact that there are typically many flat surfaces in the 

environment. Planes are extracted from the range data and matched with boundaries in the 2D map.

The boundaries are typically places where walls meet or where the floor meets a wall. It is thus relatively 

easy to locate traversable regions including doors, corridors, etc. and to map the work area. Navigation 

is done by planning paths through the resulting map, with the three-dimensional information being used 

to check for and avoid obstacles. Creed and Lakaemper [18] also take advantage of the prevalence of 

planar regions in man-made environment. They project linear features into a two-dimensional map and 

use consistency to build a representation of the environment based on line segments. Moving objects 

are detected as obstacles and are not placed into the map. Their system is able to modify the map to 

deal with slow changes to the environment, such as when a door is opened and remains open for a 

significant time.

Another approach uses vision to localize the robot and, frequently to map the environment at the same 

time (SLAM). This allows navigation without needing an a priori map or known beacons in the 

environment (e.g., Davison and Murray [19], Liu and Thrun [20]). The Ford Motor Company

implemented a SLAM application in which a camera was used for mapping floor patterns beneath the 
6

AGV (Kelly, [21]). A related approach uses either bar-codes on the ceiling (TotalTrax [22]) or a mosaic 

image of the ceiling (Lucas et al. [23]) to determine the location of the vehicle and map the facility.

In yet another approach, Luimula et al. [24] use RFID tags in the environment for localization. Röhrig et 

al. [25]use a combination of laser and wireless ranging (using IEEE 802.15.4a network signals) to localize

an omnidirectional vehicle using a particle filter (a type of sequential Monte-Carlo method). Bostelman 

et al. [26] applied GPS-like waypoint navigation to an indoor facility by using active RFID tags as 

waypoints placed at corners or where events were to occur. For example, at robot turns and door 

openings, the robot senses the tag upon approach. Once at the tag, the robot can, for example, turn the 

assigned amount to move along its new direction toward another tag while using stereo vision and 2D 

laser scanning for obstacle detection and avoidance. Since the vehicle can also place the sensed 

obstacles into a map, the same method could be used for mapping unstructured facilities when moving 

from one tag to another.

Röwekämper et al. [27] developed a method for localization using particle filters and two laser scanners. 

They also developed metrics for pose evaluation and evaluated the performance of their algorithm, 

showing that it is accurate to within an average of 5 mm and 0.15°. The performance metrics are useful 

for evaluating a wide range of algorithms. A different high-resolution localization approach is 

exemplified by Bartlett and Hvass [28] and Wang et al. [29]. These researchers independently developed 

the capability to localize a mobile manipulator by using a large-volume metrology instrument (Nikon 

iGPS). The authors of the first study used a manipulator mounted on an omnidirectional base to follow a 

path on an airplane wing with high accuracy. The second paper used a small robot to scan a turbine 

blade to inspect it or construct a 3D model. In each case, the external iGPS measurements were used to 

control the onboard robot motion with low errors (about 5 mm) in a large area (about 3 m2).

Planning and Coordination

In a typical manufacturing environment there may be a large number of industrial vehicles moving 

material or in-process parts between workstations or, in more advanced operations, positioning tools or 

robots that operate directly on the parts. There are many aspects of the operation of such vehicles that 

must be planned and coordinated. They include ensuring that the paths of the vehicles do not intersect, 

that traffic does not become congested, that material is delivered to the right places at the right times 

and throughput is compatible with the work cadence of the factory, and that vehicles are given time to 

recharge. Planning includes determining more than just the paths that the vehicles will follow. It also 

may include ensuring that the vehicle avoids other equipment or people while enabling high precision 

docking with conveyors or other equipment.

The current practice in industry is to handle coordination as an offline problem to be solved when the 

vehicles are programmed. There are a number of reasons for this approach. They include safety 

concerns and the desire to maintain a constant rate of production. The offline approach breaks down in 

situations where there is the need for quick changeovers between tasks, and when people may share 

the workspace with the vehicles. As a result, there is a greater need for methods that try to maintain 

productivity and safety while still enabling the vehicles to modify their trajectories to enable 

coordinated motions and ensure obstacle avoidance. Such implementations rely on increasingly 

adaptive and intelligent control architectures with improved sensor feedback and situational awareness

[36].

Much work has been done to try to plan and coordinate the actions of multiple mobile vehicles. For 

industrial AGVs, coordination is usually done centrally and the optimal routes can be computed offline 
7

because they do not change for long periods. Because AGVs and mobile robots are starting to be used in 

less structured environments and in situations where their tasks are not fully known in advance, the 

planning and coordination has become more difficult. While there are still methods that are based on 

centralized computation, distributed approaches are becoming more common because of the 

computational intractability of centralized methods. Decentralized methods tend to be agent based and 

may make use of a set of spatial and temporal zones in each of which plans are computed separately. An 

example of a zone-based approach is Digani et al. [37]. The authors define a two-layer architecture. The 

first layer breaks the work area into sectors and uses a topological search algorithm to find paths from 

sector to sector. The second layer is responsible for planning paths within each sector and computes the 

actual trajectories that each vehicle will follow, taking into account conflicts that may occur at 

intersections. Another approach that has some similarities is taken by Herrero-Perez and MartinezBarbera [38]. Here, there are again two levels, but one is for decentralized navigation planning and the 

other for centralized task allocation and traffic control. The traffic control makes use of a zone-based 

decomposition of the work area, while the task allocation uses an auction mechanism to allow AGVs to 

bid on tasks. The AGV behaviors are modeled as Petri net plans. The system has been implemented and 

is operational in a real factory environment. While the paths that are planned in this system are not 

fixed, special care must be taken for high-accuracy docking maneuvers, which are planned and executed 

using a special procedure (Herrero et al. [39]). Another approach to task allocation and conflict 

resolution is to use time windows to find candidate paths and check their feasibility (Smolic-Rocak et al.

[40]). This method uses a centralized algorithm to develop a dynamic routing plan for a facility that uses 

multiple AGVs and takes into account the number of active missions and their priorities. The algorithm 

resolves time window conflicts iteratively by inserting new time windows until there are no overlaps or 

the overlap is on the first segment of the path, which means that the candidate path is not feasible. The

method has been implemented in several factories.

Predicting the movements of people and vehicles in the vicinity of mobile robots is important for safety 

and efficiency. Acuña et al. [42] developed a path-planning method called dynamic artificial potential 

fields, in which the planner allows the robot to navigate safely in highly dynamic environments even

when obstacles move at higher velocities than the mobile robot. The method has been tested only in 

simulation and claims 100 % better results for the same scenarios than systems that do not incorporate

prediction. Also, for dynamic environments, artificial potential field algorithms have been used to 

enable mobile robots to “repel” static and dynamic obstacles as if they were oppositely charged 

magnets and to dynamically adjust robot speed. Shehata and Schlattmann [43] researched a dynamic 

virtual obstacle representation that adjusts robot speed and steering angle. Again, this algorithm was 

only proven in simulation.

Angerer, Pooley, and Aylett [8] discuss the use of a hierarchical multi-agent system for dynamically 

reconfiguring mobile robots to accomplish a range of variations of tasks in an automobile factory that 

arise due to customizable feature of individual vehicles. Their system consists of a behavior-based 

backbone that operates at a fixed rate. It includes an ontology describing the objects and where they are 

located in the facility and a set of tasks that the system is able to carry out. The system can dynamically 

generate new agents to execute tasks that may arise when the environment changes. These agents are 

based on known capabilities of the system. Actions have both preconditions and post-conditions and 

this makes it possible to validate a new (planned) action before it is executed and during execution. 

While the system is in principle applicable to both stationary and mobile robot applications, it was 

developed and tested in an automotive application in which mobile robots move components between 

workstations and storage.
8

Performance Evaluation

Given the expense and risk involved in implementing mobile robots, it is important to evaluate how well 

they perform and to be able to determine ways in which their productivity can be improved. According 

to Berman et al. [31], evaluating the performance of AGVs should include both the capabilities of the 

vehicle itself and the role it plays in the manufacturing environment. The authors advocate a three-part 

evaluation strategy that covers the individual subcomponents of the AGV task (control, navigation, and 

load handling), a quantitative evaluation of the AGV system and its role, and a qualitative evaluation of 

the system. They provide a number of metrics and demonstrate their use in a two-vehicle system.

NIST has addressed performance evaluation of mobile robots in manufacturing by fostering challenges 

to promote academic research on AGV intelligence for factory environments. These challenges 

attempted to raise the AGV’s level of intelligent performance on tasks that occur in real situations. Two 

such competitions were:

 Virtual Manufacturing Automation Competition (VMAC), 2007 - 2009 (Balakirsky and Madhavan 

[32]). This competition consisted of workshops and national and international AGV competitions 

based on real-world factory scenarios that demonstrated accurate path following and docking tasks. 

A feature of the competitions was that the software used enabled code to be moved without any 

changes from the simulation system in which it was developed to a real AGV for demonstration.

 Mobility and Task Completion Challenge, International Conference on Robotics and Automation 

(ICRA), 2012 [33]. This virtual challenge was designed to address the need for one or more factory 

AGVs to operate in unstructured environments amongst dynamic obstacles. Teams used the Unified 

System for Automation and Robot Simulation (USARSim) [34] framework to control simulated AGVs 

that delivered completed pallets by driving through a simulated warehouse environment, including 

loading and unloading of vehicles with a robotic arm.

Temple University and the University of Maryland, College Park, conducted research to test and 

evaluate the navigation capabilities of industrial mobile robots in industrial environments, such as 

modern dynamic warehouses. Their goal was to create and experimentally validate a framework by 

which AGVs and forklifts can automatically generate a sufficiently accurate internal map (world model) 

of their surroundings (Lakaemper and Madhavan [35]). Vendors who participated in the research

received a quantitative, unbiased, third party assessment of their systems' capabilities.

Standards for Industrial Mobile Robots

Currently, there no performance standards for automatic industrial vehicles anywhere in the world. 

There are, however, safety standards both national and international. A key US standard for AGVs is the 

American National Standards Institute/ Industrial Truck Standards Development Foundation 

(ANSI/ITSDF) B56.5 for AGVs and manned vehicles with automated functions [44]. The scope of B56.5 is 

to define the safety requirements relating to the elements of design, operation, and maintenance of 

powered, not mechanically restrained, unmanned automatic guided industrial vehicles and the systems

of which the vehicles are a part. It also applies to vehicles originally designed to operate exclusively in a 

manned mode but which are subsequently modified to operate in an unmanned, automatic mode, or in 

semiautomatic, manual, or maintenance modes. A list of other relevant mobile robot safety standards 

for both US and Europe is shown in Table 2. 
9

A new ASTM International AGV performance standards-development task was formally approved by the 

ASTM main committee on May1, 2014. The effort forms a new committee, entitled: “Driverless 

Automatic Guided Industrial Vehicles,” with the scope being to develop “standardized nomenclature 

and definitions of terms, recommended practices, guides, test methods, specifications, and performance 

standards for Driverless Automatic Guided Industrial Vehicles” while encouraging research and 

sponsoring symposia, workshops, and publications to facilitate the standards development in

coordination with other ASTM technical committees. Five associated sub-committees will be structured 

to address Environmental Effects, Docking & Navigation, Object Detection & Protection, 

Communications & Integration, and Terminology.

Beyond industrial automatic guided vehicles, yet relevant to humans working close to mobile robots, ISO 

13482 [45] crosses over from personal care mobile robots to industrial mobile robot risk assessment and 

mitigation. This standard includes safety of personal care robots designed to improve quality of life for 

people. Most of these robots are mobile and intended to directly interact with humans and obstacles in 

their environments. 

Advanced Applications and AGVs

The range of applications that lend themselves to the use of mobile robots is growing as the capabilities 

of the robots and related sensors improve. This growth is also spurred by the overall demand by 

industry for greater automation and the development of safety standards that let humans and robots 

share a work area. As a result, a number of prototype mobile robots have been built for manufacturing 

applications. These range from vehicles that track materials, to mobile manipulators, to aerial drones

used for material handling.

A novel application of AGVs to keep track of and optimize the locations of items in a warehouse is 

provided in Hildebrandt et al. [46]. The authors assume that stock items are equipped with radio 

frequency identification (RFID) tags and that a set of mobile robots can both localize their own positions 

in the facility and determine the locations of stock items using the RFID tags. The robots move about in 

the facility and, by tracking the movement of items, robots can identify preferred paths, find 

opportunities for optimizing storage locations and vehicle trajectories, and keep track of inventory.

The Kiva Mobile Fulfillment System [47] is one of several examples where a beneficial side-effect of the 

way that items are delivered from and returned to storage is the optimization of the placement of items 

in a warehouse. For example, items that are required frequently will, over time, be stored closer to the 

delivery area because the robots find convenient places to store them rather than relying on fixed 

locations.

Throughout this document, discussion of mobile manipulator research has been interspersed with 

mobile robot research as an extension of mobility. Mobile manipulators are being discussed in 

standards committees due to the gaps between AGV and robot arm safety standards and the possibility 

of opening new areas of research (Marvel and Bostelman [48]). Figure 3 shows a timeline from Bøgh, et 

al. [49] providing an example of the many mobile manipulator systems that have been or are being 

researched. Sophisticated vehicles that include robot arms for manipulation and sensors for navigation 

and handling components are discussed in Hvilshøj et al. [50]. 
10

Figure 3. Timeline of mobile manipulator development (Photo courtesy of MTECH, Aalborg University, Denmark [49]).

Small drone multi-rotor copters are beginning to be explored for use in material handling with the 

recent concept of drone delivery of small packages weighing up to approximately 2.2 kg (5 lb) [51]. This 

concept (see Figure 4) from the Netherlands requires minimal infrastructure to install, enables rapid 

deployment, and is expected to maintain a relatively high sustained throughput. Interest from 

companies like Amazon will continue to drive battery and control development [52]. 

Figure 4. Snapshot of simulation video showing drones being used for palletizing (from [46]).
11

An example of a crawling mobile robot is described by Menegaldo et al. [53]. The robot is designed to 

inspect the outer surfaces of large oil ship hulls and floating production storage and offloading 

platforms. Locomotion over the hull is provided through magnetic tracks, and the system is controlled 

by two networked PCs and a set of custom hardware devices to drive motors, video cameras, 

ultrasound, inertial platform, and other devices. The navigation algorithm uses an extended-Kalmanfilter (EKF) sensor-fusion formulation, integrating odometry and inertial sensors. 

When the work area is cluttered or the floor is not level, combinations of mobility methods may be 

needed. For example, Michaud et al. [54] discuss a robot with legs for climbing over obstacles or 

changing robot height combined with tracks for mobility on hard or soft surfaces. Autonomous control

for this type of tracked mobile robot is discussed by Mihankhah et al. [55] for navigating and traversing 

obstacles (e.g., stairs). These types of robots could provide material handling or mobile manipulation in 

highly unstructured environments, such as shipyard dry docks, aircraft manufacturing, or other large,

small-batch manufacturing projects. 

An alternative to a traditional mobile robot with onboard manipulator is described by Yang et al. in [30]. 

It consists of a four-legged, parallel robot with clamping devices at the end of each leg. A set of 

supporting pins is placed in the work environment at known locations. The robot moves by detaching a 

leg from one pin and attaching it to another, thus always accurately knowing its position. It is able to 

climb, so does not have to remain on a flat surface. The legs do not all have to be clamped and the 

platform mounted on the legs can carry and manipulate tools to perform work when reaching its 

destination.

Figure 5. CAD model of a four-legged, parallel, walking robot with locking mechanisms (lockers) as needed for walking or 

load manipulation on some passive joints and clamping devices at the end of eachleg [29].

Summary and Conclusions

The field of mobile robotics is much larger than what has been described in this document. It covers

autonomous driving on roads and across country, flying and water-based mobile robots, and a range of 

indoor applications that are not related to manufacturing. Historically, research in the United States has 

focused largely on areas of interest to the military and emergency services because that is where 

funding for research has been available. More recently, interest has been growing in mobile robots to 

assist people or provide services because there is a perception that robotic solutions might be 

commercially viable. Research in Europe has been more varied and has addressed more of the 

manufacturing needs, while Japan has focused, until recently, on humanoid robots and Australia has 

conducted substantial work in mining and agriculture. All of these strands of research are starting to be 

combined into systems with greater capabilities both for movement and autonomous action. As a result, 
12

it can be expected that the number of mobile robots in manufacturing will increase and the tasks that 

they will be expected to accomplish will become more complex. With a parallel increase in sensor 

processing capabilities and hardware robustness, it will become more common for people and robots to 

interact in a common workspace.

A range of manufacturing applications will be made possible that are currently very difficult or expensive 

to achieve. For example, instead of requiring large, custom machine tools to fabricate large components, 

it will be possible to move smaller, general-purpose tools around the component and fabricate it in a 

new way. This will require highly-accurate position measurements, but such tools already exist and have 

started to be applied in robotics applications. Another advantage of not requiring large “monument” 

machine tools is more flexibility in arranging the assembly line and, ultimately, enabling dynamic 

reconfiguration as the product mix changes. Other advantages of using mobile robots include the ability 

to offload dangerous or ergonomically-challenging tasks from people and to automate tedious tasks 

such as kitting and palletizing. 

Before these capabilities can reach the marketplace, however, vendors will have to be able to guarantee 

the specifications and range of application areas of their products, and purchasers will want ways of 

comparing products and determining which are most suited to their needs. This will require 

performance metrics and procedures that are currently in their infancy. There will also be the need to 

program the tasks the robots will carry out in an easy and flexible manner, to be able to change tasks 

rapidly as the product mix changes, and to deal with the much less constrained work environments that 

inevitably accompany people working alongside robots. Standards will also have to be enhanced and 

harmonized, especially when mobile robots incorporate manipulators and dexterous end-effectors.

While progress is being made on all fronts, it is likely that introduction of new capabilities for 

manufacturing will be slow. There is a need for more focused research on manufacturing robotics and 

especially on mobile robots that can plan their own paths, localize themselves precisely, and have 

sufficient sensors and generic-enough manipulators to carry out human-like tasks in unstructured 

factories.
13

Table 2. Other AGV-Related Standards

ANSI B11.1-2010 

Safety of Machinery – General Requirements and Risk 

Assessment

BS EN 1525: 1998 

Safety of industrial trucks – Driverless trucks and their 

systems

BS EN ISO 3691-6:2013; BS 

EN ISO 3691-1:2012 

(originally BS EN 1726-

1:1999)

Safety of industrial trucks – Self-propelled trucks up to and 

including 10 000 kg capacity and industrial trucks with a 

drawbar pull up to and including 20 000 N

IEC/EN 60204-1: 2006 Safety of machinery – Electrical equipment of machines -- 

Part 1: General requirements (IEC 60204 -1:2005 (MOD))

BS EN 61496-1: 2010 

Safety of machinery - Electro-sensitive protective 

equipment -- Part 1: General requirements and tests (IEC 

61496 -1:2004 (MOD))

ISO EN 13849-1: 2008 

Safety of machinery - Safety-related parts of control 

systems - Part 1: General principles for design (ISO 13849-

1:2006)

IEC 61508-1: 2010 

Functional safety of electrical/electronic/programmable 

electronic safety-related systems - Part 1: General 

Requirements

IEC 61508-2: 2010 

Functional safety of electrical/electronic/programmable 

electronic safety-related systems - Part 2: Requirements for 

electrical/electronic/programmable electronic safetyrelated systems

IEC 61508-3: 2010 

Functional safety of electrical/electronic/programmable 

electronic safety-related systems - Part 3: Software 

requirements

IEC 61508-6: 2010 

Functional safety of electrical/electronic/programmable 

electronic safety-related systems - Part 6: Guidelines on 

the application of IEC61508-2 and IEC61508-3

IEC 62061: 2005

Safety of machinery - Functional safety of safety-related 

electrical, electronic and programmable electronic control 

systems

IEC/TR 62061-1 Guidance on the application of ISO 13849-1 and IEC 62061 in 

the design of safety-related control systems for machinery

IEEE Std 1175: 1992

IEEE Trial-Use Standard Reference Model for Computing 

System Tool Interconnections

VDI 4451 Part 2:2000

AGVS Power Supply and Charging Technology (Lead acid 

and NiCd batteries), our batteries is LiFePO4 type

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