NISTIR 8140

Review of Research for Docking 

Automatic Guided Vehicles and 

Mobile Robots

Roger Bostelman

Tsai Hong

This publication is available free of charge from:

https://doi.org/10.6028/NIST.IR.8140
NISTIR 8140

Review of Research for Docking 

Automatic Guided Vehicles and 

Mobile Robots

Roger Bostelman

Tsai Hong

Intelligent Systems Division

Engineering Laboratory

This publication is available free of charge from:

https://doi.org/10.6028/NIST.IR.8140

October 2016

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
1 

Review of Research for Docking Automatic Guided Vehicles and Mobile Robots

Roger Bostelman, Tsai Hong 

Abstract 

This paper provides a literature review of docking research, an area that has relatively few research 

articles as compared to generic automatic guided vehicle (AGV) or mobile robot research. Docking 

refers to the arrival and stopping at a position relative to another object. Docking can include 

positioning the vehicle or the equipment onboard the vehicle relative to another object, for example a 

tray station, trailer, or pallet. The paper is expected to be useful to ASTM Committee F45 for 

development of docking and navigation standards and to industrial vehicle research communities as 

basis to further docking research. The paper covers research on vehicle control for docking and on the 

use of sensors combined with intelligent vehicle control. Docking research on AGV or mobile robots 

with onboard robot arms is also included. Other related research described includes flexible cells and 

docking of other than ground vehicles, e.g., unmanned surface vehicles. Closing the survey is a 

discussion of patents related to docking and an extensive list of references. 

Index Terms: survey, dock, docking, AGV, mobile robot, docking patents, standards. 

Introduction 

Automatic guided vehicles (AGVs)1 are used in a variety of industrial settings, including factories and 

hospitals, to transport materials from one location to another. AGVs move automatically using 

preprogrammed stop points, typically 1) navigating via laser triangulation [1] using offboard path 

planning and vehicle control. Mobile robots provide similar functionality, although they typically use 

simultaneous localization and mapping [2] and/or other fiducial-following methods with onboard path 

planning and vehicle control. 

AGVs and mobile robots are designed to execute pickup/delivery services between pickup/delivery 

stations. To do so, the vehicle must be able to navigate between stations and perform precise docking 

maneuvers. Docking can be referred to as the arrival and act of stopping at a position relative to another 

object prior to performing a load transfer or other object-connection (e.g., assembly) operation between 

an AGV or mobile robot and another vehicle or equipment/object, for example with a roller table, rack, 

trailer, cabinet, or object. Docking requires more precise control than navigation to avoid collision with

the station or the load and to ensure a correct load transfer or connection with the object. Without such 

precise control, docking maneuvers can damage the load, the station, and the vehicle [3]. Mandel and 

Duffie also state that current docking accuracy is limiting the industrial applications of AGVs and 

mobile robots. [4] 

Precise docking control requires generic definitions of terms. Within ASTM Committee F45 Driverless 

Automatic Guided Industrial Vehicles [5], ASTM subcommittee F45.91 has drafted, in working 

document WK48954, definitions for many terms, including ‘dock’ and ‘docking’: 

• dock, n—target location where the vehicle interacts with another object.

• docking, v- the arrival and act of stopping at a position relative to another object. 

Such generic definitions will allow vendors and users to utilize the exact same terminology when 

discussing the various capabilities of AGVs and mobile robots. Docking operations could include not 

only docking of the vehicle (AGV or mobile robot) but also docking of equipment onboard the vehicle 

 

1 Disclaimer: NIST does not endorse products discussed within this paper 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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such as an apparatus or a robot arm, with other vehicles or facility equipment. Safety during both 

navigation and docking is important. 

Industrial vehicle standards exist [6, 7], or are being developed [8], for safety; but only for AGVs and 

people/equipment near AGVs. Within at least [6], there are no specific safety requirements for docking

operations or methods. However, there are specifications for non-contact safety sensors and bumpers 

that are used to detect obstacles, equipment, and people near the vehicle. These vehicle safety sensors, 

however, are not typically designed for use near docking equipment, such as tray stations and racks. 

They will detect the presence of the equipment and cause the vehicle to initiate an emergency stop2

. 

The standard requires powered, load-handling devices to have an interlock when used in conjunction 

with powered, load-handling stands or devices external to the vehicle. Furthermore, the standard 

requires a signal confirming proper vehicle alignment prior to activation of any load transfer 

mechanism. 

To complement the above stopping and safety-related standards, ASTM Committee F45 is developing 

performance-related standards for industrial vehicles. The work is organized according to the following 

subcommittees: ASTM F45.91 deals with terminology, F45.01 with environmental effects, F45.02 with 

docking and navigation, and F45.03 with obstacle detection and protection. New working documents

are being developed for ‘Navigation in Defined Spaces’ (WK48955) and ‘Docking’ (WK50379). 

These documents, however, are based on minimal background research on the theory and methods 

behind vehicles, and their onboard equipment, docking with another object. 

This paper includes results of a search of relevant scholarly research and industrial patents using the 

term ‘dock’ and of interviews with industry contacts. The beginning docking research section includes 

prior general literature reviews on navigation and docking. Following are specific research discussions

on control, sensors with intelligent vehicle control, and mobile robots with robot arms. Sensors 

combined with intelligent vehicle control change the control methodology to include environmental 

sensing for planning and actuation. Moreover, the addition of an onboard robot arm provides further 

complexity to vehicle controls. Other related research is then provided covering manual, multi-robot, 

space, and surface vehicle challenges. A section on cross-docking is provided to ensure that there is no 

confusion in vehicle docking and the term cross-docking which typically covers material handling 

directly from receiving to shipping areas in distribution facilities. Lastly, five patents on dockingrelated equipment are discussed: a sensory interactive docking module, a robot arm onboard a vehicle 

for material handling, an automatic charging system, a docking platform, and docking station. 

These results provide a basis for developing new docking-related performance standards for industrial 

vehicles, such as AGVs and mobile robots. The paper also discusses research associated with crossdocking. Cross-docking is a concept associated with material handling and distribution in which goods 

move directly from receiving to shipping. We have included a section on cross-docking because it 

includes unstructured navigation and docking. A list of references is then provided to allow the reader 

to delve further into specific docking research areas of interest. 

Docking Research 

Docking research has been ongoing in several areas since inception of automated guided vehicles in the 

1950’s. This section first divides docking research into general literature reviews that provide other 

compilations of past research, although with different intended focus than this document. Following 

 

2 Note: areas around docking equipment are called hazard or restricted areas. People should not be present in these areas. 

But, if they are, the vehicle safety sensors should detect them and cause an emergency stop.

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are sections on control, sensors and intelligent vehicle control, mobile robots with robot arms, and other 

related research. Since docking cannot occur without navigation to the docking location, navigation is 

also embedded within the surveyed docking research. 

General Literature Review 

Arkin and Murphy, Rooks, and Vis have compiled general literature reviews describing the state-ofresearch on navigation and docking. Arkin and Murphy [9] described some of the earliest research 

methods that were proposed for achieving mobility of autonomous vehicles in the workplace. They 

presented an overview of the autonomous robot architecture (AuRA), which was designed to facilitate

intelligent mobility/navigation. They also described the changes needed to adapt AuRA to new 

domains for flexible manufacturing systems. The changes included the new types of knowledge and the 

new motor behaviors required for this domain. Finally, they discussed the results of both simulations 

of, and real experiments of, navigational planning and reactive/reflexive motor schema-based 

navigation in that domain. 

In 2001, Rooks [10] described different uses of AGVs across a range of industries and applications. For 

example, he described how at one manufacturing facility, AGVs were used as tugs to tow trailers 

delivering components to production lines, while at another, they carry both part kits and finished 

engines to and from assembly dressing lines. At yet another facility, they handled heavy paper rolls and 

served coating machines. These applications use traditional embedded wire navigation technology. 

Other guidance techniques were also described, including laser scanners which are being applied in a 

Belgian fruit and vegetable market to transport pre-packed products to dispatch lines. Specific to 

docking, Rooks details an automatic docking system and a standard pallet truck that is available as a 

complete automated package with laser guidance. 

Vis [11] discussed literature related to the design and control of AGV systems in four different 

applications: manufacturing, distribution, trans-shipment, and transportation. The author concluded that 

new analytical and simulation models were needed for large AGV systems to overcome large 

computation times, NP (nondeterministic polynomial)-completeness, congestion, deadlocks, and delays 

in the system and finite planning horizons. The author concluded that more specific research is needed 

in the design and control of large AGV systems in the application areas of distribution, trans-shipment, 

and transportation. 

Control 

Vehicle control is a major research topic for both navigation and docking. The earliest form of AGV

navigation was based on following electrical wires buried in the floor, or reflecting lines painted on it; 

some vehicles still operate using this mode. This made adaptation to changes in production or 

warehousing processes elaborate and costly because changes in vehicle paths required changes in 

factory infrastructure to modify the guidance paths. Some newer AGV systems use beacons, which 

make changes easier, but these systems are typically not yet capable of avoiding obstacles. Very 

recently, computers have become a common navigation component in AGVs. Practical limitations, 

however, with either a central or an on-board computer mean that a certain level of autonomy is needed 

for navigation. For example, a central computer typically stops a vehicle from entering a location 

occupied by another vehicle. An onboard computer with added intelligence may be capable of 

surpassing the stopped vehicle. However, in both cases, for computational simplicity, the basic 

planned paths consist of straight lines or `lanes'. To enable the vehicles to change lanes, without 

stopping to turn, while staying on the planned path, curves with a continuous curvature are needed. Van 

der Molen and Geerts [12] describe the application of clothoids to enable changing between lanes. 

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These clothoids envelope boundaries that encompass the `swipe-out' area occupied by the vehicle 

during motion. 

Ilić [13] examined some of the general principles and analysis methods that are used to design AGVs 

for automated material handling tasks. Specifically, their research focused on an AGV system (AGVS) 

that is especially suitable for handling and transporting materials in discrete-product manufacturing. 

These systems are most applicable for the automation of low-and medium-volume handling situations, 

where the routing of materials to docking locations is more individualized. Ilić developed a new 

quantitative method for analyzing these systems giving examples to demonstrate the method. 

The application of interpolation-based, approximate fuzzy reasoning methods in direct, fuzzy logic, 

control systems gives a simplified way to construct the fuzzy rule base for controlling both vehicle 

speed and steering. In this control method, rules don’t risk the chance of not having a conclusion for 

some of the observations. Kovàcs and Kóczy [14] introduced an approximate, fuzzy-reasoning method 

based on Kóczy and Hirota [15] interpolation which could be implemented to be simple enough for 

practical direct, fuzzy-logic, control applications designed to acquire the shortest docking routes. They 

introduced a steering and collision-avoidance strategy that demonstrated, in simulation, the efficiency 

of the proposed approximate fuzzy reasoning method in direct fuzzy control. An example simulation 

output of the speed and steering for the shortest docking distance on the trial guide path is shown in

Figure 1. 

a b 

Figure 1 – Simulation of a fuzzy controller used to demonstrate the shortest docking distance on a trial 

guide path (a) without and (b) with an obstacle to avoid. (courtesy [14] Kovàcs and Kóczy, 1998)

Efficient and effective control of AGVs is vital in relatively expensive semiconductor and liquid crystal 

display plants because AGV systems often limit the total production capacity. Jang, et al. [16], 

presented an efficient control methodology for AGV routing in semiconductor and LCD production 

bays, where AGVs play a central role in material handling. The methodology uses information on the 

future state of systems to make routing decisions. These highly knowledge-based systems maintain a 

great deal of information on current and near-future states, such as the arrival and operation completion 

times of parts - thereby enabling a new approach for production shop control. With the proposed 

methodology, the cell controller could record future events chronologically and use this information to 

determine the navigation docking between any part’s operation machine and temporary storage. They 

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showed by simulation that the new control methodology reduced both AGV requirements and flow 

times of parts. 

The design of AGVSs must incorporate not only the vehicle’s and guide-path routing, but also some 

control problems such as collisions and deadlock (i.e., when two or more AGVs are programmed to 

occupy the same zone or location). Fanti [17] presented a control strategy to avoid deadlock and 

collisions in zone-controlled AGVSs. In particular, the control scheme managed the assignments of 

new paths and next zone acquisition. Moreover, he proposed colored, timed Petri nets to model the 

AGVS structure and dynamics. The model allowed an easy implementation of the control strategy 

based on the knowledge of the current system state. Later, Dotoli and Fanti [18] presented a novel 

control strategy that manages vehicle paths, both current and following, to avoid deadlock and 

collisions of vehicles and their acquisition of the next zone. They also tested colored petri nets for 

dynamic vehicle modeling and to monitor vehicle performance. They implemented several simulations 

of AGVs with varying fleet sizes to test the effectiveness of the proposed control strategy and compare 

it to their previous work. 

Beyond path routing, Henson, et al. [19] considered steering as a challenge to docking. They developed

a novel, partially heuristic technique that allowed relatively accurate docking for Ackerman-steered 

vehicles. Multiple experiments were performed to better understand and analyze the heuristic element 

of the technique and validated via simulation. The results demonstrated that the vehicle's steering 

characteristics impact docking precision which may prove valuable in attempting to remove the 

heuristic element. 

Berman and Edan [20] developed decentralized control of an autonomous AGV used for material 

handling. Their goal was to improve overall system flexibility and robustness. Their approach

addressed all aspects of AGVS functionality, including: system management, navigation, and docking 

for load transfer. Hierarchical, fuzzy, behavior-based control, which is a reactive navigation scheme 

was expanded to multi-robot control in semi-structured environments by incorporating an a priori path 

optimization and right-of-way determination.

Similar to Berman, Herrero-Perez and Martinez-Barbera [21] proposed a methodology for modeling 

and controlling a flexible material handling system (MHS) which included multiple AGVs that was

suitable for Flexible Manufacturing Systems. “The AGVs incorporated artificial intelligence techniques 

to: 1) facilitate the configuration and adaptation of the vehicles when there are layout modifications and 

2) simplify the interaction between them using simple coordination models. In order to achieve higher 

flexibility, the MHS made use of decentralized navigation control, which increases autonomy and 

scalability, and a distributed Petri net for solving task allocation and traffic control problems.” In order 

to facilitate the integration with the manufacturing processes, tasks dispatched by manufacturing cells 

were allocated by the MHS which took into account pending transportation tasks and the system’s 

current performance. [22] The system was tested in a real factory and in 2010 was in operational use. 

Buyurgan et al. [23] presented the development of an architecture for real‐time routing of AGVs to 

dock locations in a “random flexible manufacturing system”. The AGV routing problem was modeled 

using an evolutionary, algorithm‐based, intelligent, path-planning model. The model made vehicle 

routing decisions for material handling requests to maximize system throughput. A three‐layer software 

architecture was implemented that used hypothetical production data and traditional dispatching rules. 

The analysis showed that, in many cases, the proposed routing model outperformed the traditional 

dispatching rules for real‐time routing of AGVs. 

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Villagra et al. [24] presented a control strategy to ensure low jerk variations in AGV path-tracking 

operations in industrial environments. The system focused on robustness, performance, and ease-ofconfiguration of the vehicle maneuvers. The authors state that robustness was improved by combining 

algebraic techniques for robust, intelligent, feedback control with differential flatness. The system was 

easily configured by defining a velocity profile that was decoupled from the geometric path. Some 

simulated results showed dramatic improvement with respect to more standard, flatness-based 

controllers. 

Lucas et al. [25, 26] looked to improve duration, accuracy, and stability features of precise docking 

tasks for an AGV fork-lift truck that interacted with objects. A fork-lift truck typically performs 

docking maneuvers to load pallets on conveyor belts. The authors proposed a soft-computing technique 

based on a “multi-objective, evolutionary algorithm using multiple fuzzy logic controllers” which 

satisfied imposed constraints for docking tasks. 

Balakirsky et al. provided an overview of the National Institute of Standards and Technology/ Institute 

of Electrical and Electronics Engineers (NIST/IEEE) Virtual Manufacturing and Automation 

Competition (VMAC). The goals were for competitors to implement a hierarchical-level control 

framework, to integrate team algorithms, and to port code directly from simulation to a real mobile 

robot. The overview provided the competition's objectives, history, operation, and the supporting 

software infrastructure. [27] A picture of the NIST AGV used for the competition is shown in Figure 

2. 

Figure 2 - NIST AGV used for the NIST/IEEE Virtual Manufacturing and Automation Competition 

[27] Balakirsky, 2008] 

Kirsch et al. [28] suggest that positioning sensors used for accurate localization of AGVs for docking 

applications are relatively expensive. Also, they suggested that there are drawbacks to the 

requirements concerning the omnidirectional AGV design, construction, and onboard safety sensors. 

Instead, they compared three frequently-used algorithms for position tracking using the safety laser 

range sensors: the Extended Kalman Filter, the Unscented Kalman Filter, and the Monte Carlo Particle 

Filter. The authors present analysis of AGV positioning accuracy, the time behavior of the algorithms, 

the AGV movement function, and the measurement function. 

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As in [28], Villagra and Herrero-Perez [3] provided an in-depth comparison between different control 

approaches seeking robust AGV guidance in path-tracking load transfer operations. They assumed that 

tracking errors cannot exceed 10 cm during the docking maneuver to avoid collisions. They compared 

control strategies seeking to ensure low jerk variations in docking operations of AGVs in industrial 

environments. The proposed control system focused both on “robustness and performance (slight errors 

could damage the load or the station)” and on “ease of controller configuration (a reduced set of 

parameters should be enough to easily adapt to new layouts or production rates)”. Fuzzy and vector 

pursuit non-model-based control techniques were applied to a new nonlinear model-based control 

strategy. Simulations were used to demonstrate that the data-driven model for robust feedback control 

was combined with differential flatness to obtain the AGV control strategy. The results showed AGV 

path-tracking in industrial environments did not require prior knowledge of physical parameters. 

Herrero [29], similar to [20], followed with research on automatic configuration of the initial vehicle 

position, or waypoint. This waypoint is used to initiate a robust docking maneuver using nonholonomic (Ackerman steered) robotic forklifts used in automated manufacturing. The proper selection 

of these waypoints is of paramount importance to operate with high industrial accuracy, repeatability, 

and reliability that is required for industrial load transfer operations. An unconstrained optimization 

method coupled with probabilistic techniques was the proposed solution. The authors suggest that their 

proposed method increased the flexibility and adaptability of autonomous robotic forklifts. 

Sensors and Intelligent Vehicle Control 

Beyond advancing control strategies for AGVs and mobile robots, sensors have also been combined 

with intelligent vehicle control to solve docking challenges. The various approaches to sensoryinteractive, intelligent vehicle control briefly described in this section include: 

• Absolute vehicle positioning using bar-codes 

• Vision and optical switches to detect lines on floors 

• Infra-red sensing 

• Fusion of a charge coupled device-camera, odometry sensors, and ultrasonic sensors 

• Sensors combined with fuzzy logic and control 

• Fixed-mounted video, pan-tilt stereo cameras, and combined range sensors with video with 

image-based control

• Ultrasonic transducers on the vehicle and receivers on the facility

• Dead-reckoning combined with vision 

• Radio frequency identification and magnetic sensors with fuzzy control 

• Laser range-finding sensors 

Petriu et al. [30] presents two techniques that enhance the applicability of pseudorandom absolute 

position measurements as a cost-effective AGV upgrade. “An onboard synchronization technique for 

tracking binary code on the floor led to minimum complexity requirements for the tracks physically 

laid on the floor where a one-bit-wide pseudorandom code track was added to the AGV guide path.” 

They suggested that an optimized serial/parallel implementation of the pseudorandom/natural code 

conversion allowed the best design tradeoff between the cost of the equipment and the time for 

implementation. 

Andersen et al. [31] described the benefits and drawbacks of different strategies for docking and 

positioning of autonomous vehicles based on visual feedback. They compared three different sensor 

techniques: magnetic tape with magnetic field sensors, vision used to detect a line on the floor, and an 

array of optical switches to detect a line on the floor. The sensing techniques were tested extensively 

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through experiments on a testbed AGV concluding that odometry combined with vision provided the 

most accurate vehicle positioning. 

Vaz et al. [32] presented a sensor-based docking strategy for a non-holonomic, mobile platform used to 

support material handling operations in industrial-like environments. A low-cost, infrared sensor 

system was designed and implemented to locate the mobile platform relative to the docking station, 

where passive reflectors were installed. With this information, trajectories were generated and 

followed, docking the platform with an accuracy of approximately ± 0.5 cm in x and y, and ± 1° in θ

even though the system was designed using relatively low cost sensors. The paper also presented

relevant experimental results. 

To increase AGV flexibility, Roth and Schilling [33] 1) combined different low-cost sensors such as 

simple CCD-camera, odometry sensors, and ultrasonic sensors, 2) fused their information with 

intelligent algorithms, and 3) stored the reference path in the vehicle’s onboard computer. This required 

an effective path-planning strategy, in particular with respect to navigation, collision avoidance, and 

docking to target stations. The authors suggest that sensors to support these maneuvers need to be 

robust to survive in the rough industrial environment, cheap to be an alternative to the existing wire

guided vehicles, and accurate to meet the performance requirements. The performance of this concept 

was demonstrated using factory tests as part of the European ESPRIT project RETRARO in the 

production environment of a wool processing and spinning company. 

Induction wire-guided vehicles performed typical material transport in a textile manufacturing without 

having the flexibility to adapt transport routes to changes in the production process. Mellado et al. [34] 

developed concepts to overcome these deficits in the RETRARO project. The project included a 

consortium that developed an on-board, AGV operating-system as a general purpose, real-time, 

knowledge-based control system. The control and sensor data processing were based on fuzzy logic 

and real-time expert systems. Both enabled a vehicle to deal autonomously with the typical 

uncertainties of an industrial working environment. The approach provided good navigation, collision 

avoidance, and target docking capabilities, based on measurements by cheaper, less accurate sensors. 

Nygårds et al. [35] developed an approach to dock a mobile robot or AGV to a pallet. The pallet size 

was known but the load was not. The pallet had an initial pose (position and orientation) uncertainty on 

the order of ± 15 cm and ± 20°. The docking error was required to be within ± 1 cm and ± 1° with 

“very low” failure rate. A combination of a range camera and a video camera was used for docking 

with the role of the range camera being emphasized. Successful docking was achieved with typical

errors of ±5 mm. One weak aspect was the integration with the control system onboard the robot and 

the sensor association with the robot given that the resolution of a range camera is strongly distancedependent. A finding in the paper is that this type of docking is feasible and that it can be selfmonitoring. 

Kim et al. [36] proposed a visual docking approach using image-based control for a non-holonomic 

mobile robot equipped with a stereo camera on a pan-tilt. The mobile robot was to use only visual 

feedback to align with a rectangular target. The visual docking process was divided into two stages, 

approach and docking, and was validated in several simulations. The docking stage was implemented 

by introducing a virtual target between the final target location and the current mobile robot position. 

Minten et al. [37] reported on the complexity of a reactive docking behavior which used a vision 

algorithm that grew linearly with the number of image pixels. The robot imprinted (initialized) the 

vision system on a two-colored docking fiducial when leaving the dock, then used region statistics to 

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adapt the color segmentation to changing lighting conditions. The docking behavior was implemented 

on a marsupial team of robots, where a daughter micro-rover re-entered the mother robot from an 

approach zone with a 2 m radius and 140° angular width with a tolerance of ± 5 cm and ±2°, 

respectively. The authors stated that testing during outdoor conditions (noon, dusk) and challenging 

indoor scenarios (flashing lights) showed that using adaptation and imprinting was more robust than 

using imprinting alone. 

Feng and Zeng [38] presented the design, operating principle, and system construction of an ultrasonic 

navigation system for an AGV. They used receiver beacons located at the docking workstations and 

transmitter beacons mounted on the AGVs which removed the common drawbacks of using ultrasonic 

sensors. An electromagnetic signal was used for system synchronization based on a time-of-flight 

counter. To ensure accurate docking, a transducer equalizer was employed to reach a range precision of 

1 mm with low-cost ultrasonic transducers. They showed through experimental results that the 

approach met the requirements of the automatic material handling system of computer integrated 

manufacturing systems. Tong, F. et al. [39] also investigated ultrasonic, sensor-based AGV docking. 

They described experimental results obtained on an AGV with a docking precision of around ± 3 mm 

in x and y and ± 1° in bearing. 

Yu et al. [40] researched an intelligent, monocular vision-based AGV system. The embedded system 

and high-performance algorithms were designed to detect docking landmarks, namely Arabic numerals. 

The authors describe that their real-time, two-dimensional, camera images were processed using a 

digital signal processor, including filtering, segmenting, and labeling connected components. Then, the 

vision system calculated the relative distance and the slope of the guideline. The vision system detected 

artificial landmark numbers placed along the path side for use in docking. The simulated results of 

image processing and experimental results on an AGV both demonstrated that the algorithms were 

efficient and robust. 

Andersson and Månsson [41] researched options and found a solution to maneuver and dock an AGV 

between predefined positions with an accuracy of ± 5 mm using dead-reckoning followed by visionbased tracking. A path-tracking technique they called “Quadratic curve” was used together with the 

Extended Kalman filter algorithm for location estimation. Their results showed a mean accuracy of 5.1 

mm with a standard deviation of 3.0 mm over 30 repetitions of the docking phase. They concluded that 

odometry was easy to implement, but suffered from sensitivity to both stochastic (floor irregularities, 

collisions, skid) and systematic (cumulative) errors. The kinematic model was affected by the lack of 

measurement tolerance in the assessment of vehicle dimensions. The vision-based system proved, as 

predicted, to be very accurate but also sensitive to light conditions. The low AGV speed (maximum 

200 mm/s) combined well with the low vision system resolution and frame rate.

Wu et al. [42] developed a parking-position detection and control system for precise palletized 

materials transfer between an AGV and a load transfer station. Figure 3 shows the experimental setup. 

In order to align the AGV roller conveyer with the station using a radio frequency identification tag, it 

was necessary for the AGV to detect the longitudinal, lateral, and orientation deviations of its body 

with respect to the station. A pair of magnetic sensors was used to measure the lateral and orientation 

deviations of the AGV. Fuzzy control was proposed to eliminate these deviations and keep the AGV on 

its path. A set of optical emitters and receivers was located at specific points on the AGV and the 

station respectively; they were used to determine the longitudinal position for material transfer, and to 

coordinate the transmission operation of the AGV and station. The authors performed the transfer

experiment of palletized materials hundreds of times on the AGV prototype system. Experimental results 

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showed their AGV system could achieve the accuracy, repeatability, and reliability needed in industrial 

applications. 

Figure 3 – Experimental setup of precise transfer of palletized materials between an AGV and a load 

transfer station. (courtesy [42])

Lee et al. [43] suggested a docking solution for AGVs conveying products in medium- and small-scale 

factory automation systems such as the one used in the Korean processing facility as shown in Figure 4. 

They dedicated a task manager module to translate high-level tasks into general robot commands for 

navigation and to reuse developed algorithms without modification. When parked in front of a docking 

station, the robot detected the docking goal position by using a laser range-finding sensor and 

segmented out the station legs as shown in Figure 5. When the robot was properly docked with the 

docking station, it lifted up the pallet, and left the docking station carrying the load. 

Figure 4 - Experimental docking in a Korean processing facility [courtesy Lee, 2014] 

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Figure 5 - Results of segmentation for docking where dp is the distance between dock station legs. 

(courtesy [43])

Xing, et al. [44] designed an AGV including a two-sided, three-level, push-pull, load-transfer 

mechanism and a Programmable Logic Controller (PLC). The AGV was used for relatively long

travel, to transfer a pallet of material to a load stand and back. The AGV followed magnetic tape paths 

in the floor and located itself with radio frequency identification tags beside the load stand. The PLC 1) 

detected the docking positions of the AGV and the load stand and 2) controlled the horizontal and 

vertical movement of the load-transfer mechanism with a stepper motor and an electric push rod. Loadtransfer experiments verified the performance of the material transport system. 

As with [18] in the Control section, Gayathri, et al. [45] proposed a Petri-net approach although they 

used a programmable logical controller (PLC) design, which also prevented collisions among AGVs. 

Their implementation involved a surveillance robot with automatic docking for battery recharging, 

potentially for home security. 

Mobile Robot with Robot Arm 

Alessio [46] and Bain [47] suggested that AGVs have limited interaction with workstations. They only 

transport material to and from workstations; other material-handling equipment at the workstation can

transfer the load between the AGV and the workstation’s operating envelope. Two manufacturers 

successfully resolved the limited-interaction problem for their specific applications by equipping AGVs 

with robot arms for loading and unloading workpieces. The research into autonomous mobile robots for 

the materials-handling domain was directed at exploring autonomous mobility as a potential solution to 

the drive-path problem. [46] The TAURO system integrated the movement of a wheelchair with the 

operation of an onboard manipulator. In this way should the goal be out of the manipulator’s reach, the 

wheelchair would move on a path toward the goal until the manipulator was within reach. [47] A photo 

of the system is shown in Figure 6. 

Figure 6 – The TAURO mobile manipulator. [courtesy Bain, 1986] 

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Mandel and Duffie [48] proposed a method to compensate for mobile robot docking inaccuracies by 

modifying the task of an onboard robot arm based on the estimated docking error, which is the offset 

between the desired and actual docking locations of the mobile robot. The docking error is estimated 

using a sensor mounted on the robot arm - either a vision system or a touch trigger probe. They 

presented their algorithms for calculating the spatial docking error for each sensor and for modifying 

the robot's task accordingly. Additionally, they described a method that would allow the mobile robot 

to achieve results even in the presence of perturbed data. The calculation of the spatial offset between 

the actual and the desired locations of the mobile robot using a vision system was implemented and the 

results of experiments were presented and discussed in [48]. Erwin [49] also explored the challenge of 

how a robot can achieve the correct position and orientation with respect to the workstation, within the

required tolerances for the interaction. He developed docking strategies that used lasers for space 

applications [4] or visual techniques in flexible manufacturing systems environments. 

Other Related Research 

Other related research to docking is described in this section as there may be some bearing on ASTM 

F45 developments considering alternatives to typical industrial docking activities. For example, remote 

operator-in-the-loop control, assembly cell limitations from the vehicle, space vehicle docking, and 

autonomous surface vehicle docking. 

Barberá et al. [50] developed a remote, operator interface which was linked to the AGV through a 

wireless Ethernet network. The interface allowed the operator to know both the current tasks and 

current position of the AGV. It also allowed the operator to send a new task to the AGV, to inform the 

AGV of new docking stations or layouts, and to send new navigation maps to the AGV. The ideas and 

algorithms used in this system are common in indoor mobile robotics. The AGV should not collide 

with objects or obstacles while following the trajectory and should be far enough from the docking 

station to allow the AGV to reach an adequate pose for docking. Thus, plant operations only stop 

during the time needed to update the AGV’s topological maps. 

In a (multi-robot) assembly cell, transport of parts, products, tools, and fixtures is of major importance. 

In flexible assembly cells, transport has a substantial influence on assembly efficiency because the 

assembly times are usually much smaller than the transportation times. Storm and Boneschanscher [51] 

describe research at Delft University of Technology on a multidisciplinary research project named 

DIAC (Delft Intelligent Assembly Cell). The project goals were the development, implementation, and 

integration of technology that make up a flexible assembly cell. As part of the project, a prototype 

flexible assembly cell was built having two assembly robots with overlapping workspaces. A handling 

system provided the internal transport using reconfigurable pallets for product-specific parts, 

subassemblies, assembled products, and fixtures. Since both the number of pallets and the buffer space 

within the cell were limited, the efficiency of the assembly system depended upon the assembly 

strategy and the capacity of the handling system. 

Other potentially relevant docking research was performed by Mandel [4] and Kim et al. [52]. Mandel 

described the Laser Docking System (LDS) that consisted of passive docking aids (reflectors) placed 

on the target vehicle in a known location and orientation. These reflectors were acquired and tracked by 

means of a modulated laser beam located on the interceptor vehicle. The LDS enabled the interceptor 

vehicle to analyze the return (reflected) signal in order to determine both relative position and relative 

attitude of the target vehicle during station keeping and docking. Laser ranging experiments were

accomplished at the National Aeronautics and Space Administration, and from these experiments have 

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evolved laser docking concepts. The concepts included angle and attitude measurements that were

capable of providing all of the information needed for automatic docking control by the interceptor 

vehicle. Several designs were compared and plans included the development and testing of: first a 

breadboard model, then an engineering model, and finally qualification and flight systems. 

Kim et al. [36] discussed docking as an important capability for autonomous surface vehicles (ASVs) 

and as an essential part of a wide class of missions. One such mission was discussed: the emergency 

response and environmental protection problem of containing a floating pollutant. They proposed a 

solution in which multiple robots autonomously navigated to surround the surface matter. Before doing 

so, the robots docked with one another to secure specialized attachments designed to ensnare the 

contaminant. They described the prototypical, physical, robot system developed to perform this task

and detailed the system architecture, sensors, hardware, control system, and visual-processing pipeline. 

While employing multiple ASVs maximized spatial reconfigurability, their success depended on the 

reliability of inter-robot docking capabilities. But achieving robust docking was a significant technical 

challenge because the water continually induced external disturbances on the control system. The

system relied primarily on visual-servoing within a control framework using sensor fusion. Accurate 

disturbance measurements were obtained through traditional sensor modeling and filtering techniques. 

Since the environment was a priori unknown, varied from trial-to-trial, and proved difficult to model, 

they applied 1) a model-free, reinforcement learning-algorithm, State-Action-Reward-State-Action 

(SARSA)(λ), 2) specialized initial conditions, which ensured stable operation, and 3) an exploration 

guidance approach that increased the speed of convergence. A two-loop control scheme was adopted

for visual-servoing to successfully make use of feature descriptors with various (and variable) 

computational times. The approach demonstrated the docking problem with autonomous ground 

vehicles and ASVs. The results from several situations were compared, showing that disturbance 

rejection coupled with SARSA(λ) was an effective approach. 

Cross-Docking 

Cross-docking is a material handling and distribution concept in which goods move directly from 

receiving to shipping. In a typical cross-docking system, the primary objective is to eliminate storage 

and excessive handling. As companies continue to streamline distribution functions, cross-docking is 

becoming a more widely accepted distribution method. The benefits of cross-docking include reduced 

inventory, increased customer responsiveness, and better control of the distribution operation. By 

definition, cross-docking systems require a close connection between receiving and shipping 

operations. This connection calls for robust hardware and software systems to solve the cross docking 

problem [53] [Rohrer, 1995]. This concept is included here for completeness and to avoid confusion 

with the concept of docking. Research topics on cross-docking include: 

• literature review [54] 

• simulation [54] 

• truck docking or scheduling sequence [55] 

• application of the Taguchi method [56] 

• best sequence of inbound and outbound trucks [57] 

• cost-stable scheduling strategy [58, 59] 

• optimal route selection [60] 

Docking Patents 

Lisy [61] invented a warehouse material-handling automation system which included a portable 

module with which to load and unload material to and from an automatic guided vehicle. The system 

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operates such that the automatic guided vehicle can calculate its position relative to a destination and 

follow a route to the destination. In addition, the system operates even when the portable module is 

displaced or moved from its original position to a new destination. The system includes 1) bar-code 

targets affixed to the destination, 2) a scanner on the vehicle for scanning the bar-code targets, and 3) a 

computer on the vehicle for triangulating the vehicle's position relative to a stored path. 

McClure, et al. [62] invented a mobile robot or AGV having a multi-jointed robot arm. The robot arm 

has a gripper and camera mounted at the free end of the arm. The gripper is for engaging, holding, and 

releasing workpieces. The robot arm transfers workpieces to and from process machines or storage 

areas. The robot arm finds the workpiece or the place to set it down by looking at two light-emitting 

diodes placed a known distance and orientation away from the workpiece or set-down place. The robot 

arm and camera are controlled by a computer onboard the vehicle. The AGV and robot arm are used to 

transfer workpieces from place to place in an automated manufacturing environment. 

The invention also provides for at least two active or passive locating beacons in known positions with 

respect to the workpiece nest on a process machine. The beacons are detected by the camera and the 

visual information is then sent to an onboard control computer, which processes the information and 

guides the robot arm in picking up or depositing workpieces. The visual information also can be used to 

guide the robot arm in pushing a switch located on the process machine, for the purpose of letting the 

process machine know that the AGV is ready to load or unload a workpiece. The high degree of 

manipulative accuracy allows the robot arm to transfer very small parts, such as those associated with 

integrated circuit manufacture (see Figure 7). Fully automatic parts transfer is, therefore, possible 

without human assistance. 

Figure 7 - McClure et al. invention of a robot arm onboard an AGV. [62] 

Pinlam, et al. [63] invented an automatic charging system for AGVs, a typical docking activity. The 

invention provides AGV systems with automatic recharging and a central processor for control. The 

central processor monitors the AGVs charge states, assigns them to tasks, and determines when to 

recharge the AGVs. The central processor can optimize the operation of the AGVs to achieve various 

objectives which can include, for example, maximizing AGV availability, maximizing battery life, 

and/or maximizing recharging station utilization. Systems and methods of the invention can maximize 

the utilization of AGV equipment and minimize the number of AGVs required to accomplish a given 

set of tasks. 

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Aggarwal [64] invented an integrated wafer transport and transfer device which included a vehicle with 

an integrated docking platform for holding a wafer carrier such as a FOUP (front opening unified pod)

as shown in Figure 8. The docking platform is positioned at the correct height for sealing the FOUP to 

the load lock of a process tool. Vertical and/or horizontal movement is required in some cases. Methods 

for delivering wafers to process tools are also described. In a preferred embodiment, wafers are carried 

inside a FOUP on a cart, such as an automatically guided vehicle or a personally guided vehicle. The 

cart is docked at a process tool and the FOUP is sealed to the load lock of the tool without removing 

the FOUP from the cart. After processing on one tool, the cart along with the FOUP can be moved to 

the next process tool for further processing. The FOUP can stay on the same cart until all processing is 

completed. This is especially useful for moving priority lots through the fabrication facility quickly. 

Figure 8 - Aggarwal invention of an integrated wafer transport and transfer device [64] 

Pfeiffer [65] invented an AGV docking station that included a station base-unit and a shift unit adapted 

to move relative to the station base-unit (see Figure 9). Movements occur between an extended 

position and a retracted position, where movement of the shift-unit from the extended position to the 

retracted position defines a shift-unit movement direction. The docking station further includes an 

actuator coupled to the station base unit and the shift-unit, where the actuator is adapted to move the 

shift-unit between the extended position and the retracted position and at least one locator block 

coupled to the shift-unit. The docking station stops an AGV travelling in the shift-unit movement 

direction when a portion of the automated guided vehicle contacts at least one locator block with the 

shift-unit in the extended position. 

In some environments, robots may be used to load and/or unload parts from AGVs. To facilitate 

reliable unloading, the position of the AGVs relative to the robots should be accurate and repeatable. 

Previously, AGVs would drive to docking stations affixed to the floor of the factory. Once robots had 

loaded or unloaded parts from the AGVs, the AGVs would back away from the docking stations and 

then continue along a pre-determined path. Docking stations may be desired that stop AGVs without 

requiring reversal of the direction of travel of the AGVs. 

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Figure 9 - Pfeiffer invention of a docking station for an AGV which included a station base unit and a 

shift unit adapted to move relative to the station base unit. [65] 

Conclusions 

This survey provides a literature review of docking research for AGV or mobile robot research. As 

being defined in ASTM F45.02, docking refers to the arrival and stopping of a vehicle at a position 

relative to another object. Docking can include positioning the vehicle or the equipment onboard the 

vehicle relative to another object, for example a tray station, trailer, or pallet. Research that has been 

completed in referenced articles within this survey considers control and sensors combined with 

intelligent vehicle control as the main areas to study based on the number of articles uncovered. 

Control methods using clothoids, petri nets, and fuzzy logic among other methods were considered to 

improve vehicle approach and stopping performance for docking. Sensory interaction with intelligent 

control algorithms provided much research using radio frequency identification, vision, and laser range 

sensors to provide improved docking performance. Docking research on AGVs or mobile robots with 

onboard robot arms is also included where some researchers use the relatively precise tolerance of 

robot arms as a means for improving docking. Other related research is included on flexible cells and 

docking of other than ground vehicles, e.g., unmanned surface vehicles. Patents uncovered included 

automatic battery charging, actuated docking systems to attach between the vehicle and dock station, 

and robot arms onboard vehicles. 

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