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Technical Brief

Cooperative Robot Exploration Strategy Using an Efficient Backtracking Method for Multiple Robots

[+] Author and Article Information
Jinho Kim

Department of Mechanical Engineering,
University of Maryland, Baltimore County,
Baltimore, MD 21250
e-mail: umbcjhkim@umbc.edu

Stephanie Bonadies

Department of Mechanical Engineering,
University of Maryland, Baltimore County,
Baltimore, MD 21250
e-mail: stephanie.bonadies@ngc.com

Charles D. Eggleton

Department of Mechanical Engineering,
University of Maryland, Baltimore County,
Baltimore, MD 21250
e-mail: eggleton@umbc.edu

S. Andrew Gadsden

Fellow ASME
College of Engineering and Physical Sciences,
University of Guelph,
Guelph, ON N1G 2W1, Canada
e-mail: gadsden@uoguelph.ca

1Present address: Northrop Grumman Corporation, Baltimore, MD 21240.

2Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received September 23, 2017; final manuscript received August 22, 2018; published online October 1, 2018. Assoc. Editor: Xilun Ding.

J. Mechanisms Robotics 10(6), 064502 (Oct 01, 2018) (7 pages) Paper No: JMR-17-1322; doi: 10.1115/1.4041332 History: Received September 23, 2017; Revised August 22, 2018

This paper presents a cooperative robot exploration (CRE) strategy that is based on the sensor-based random tree (SRT) star method. The CRE strategy is utilized for a team of mobile robots equipped with range finding sensors. Existing backtracking techniques for frontier-based (FB) exploration involve moving back thorough the previous position where the robot has passed before. However, in some cases, the robot generates inefficient detours to move back to the position that contains frontier areas. In an effort to improve upon movement and energy efficiencies, this paper proposes the use of a hub node that has a frontier arc; thereby, the robots backtrack more directly to hub nodes by using the objective function. Furthermore, each robot cooperatively explores the workspace utilizing the data structure from the entire team of robots, which consists of configuration data and frontier data. Comparative simulations of the proposed algorithm and the existing SRT-star algorithm are implemented and described. The experiment is presented to demonstrate the application of the proposed strategy in real-time. Utilizing the proposed algorithm and exploration strategy, the results indicate that a team of robots can work more efficiently by reducing the distance of exploration and the number of node visited.

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Copyright © 2018 by ASME
Topics: Robots , Algorithms , Sensors
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References

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Figures

Grahic Jump Location
Fig. 1

The kth robot located at (xk, yk)

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Fig. 2

The division of workspace in FB-SRT strategy

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Fig. 3

The definition of frontier arcs with mid, left, right-point in FB-SRT-star

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Fig. 4

An example of FB-SRT-star exploration. The robot is moving from 1 to 2, and the thick outer lines represent frontier arcs at 2.

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Fig. 5

An example of the CRE exploration. Robot1 is moving from 1 to 2, and thick outer lines represent frontier arcs of Robot1 at 2.

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Fig. 6

An example of exploration using the FB-SRT-star method

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Fig. 7

Designation of qback_cand and computing distances, dc_i, between qcurr and qback_cand(i), and dh_i, between qhub and qback_cand(i)

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Fig. 8

Case 1: progress of the CRE with three robots in the square environment. The thin lines represent walls, the three large circles are robots, and the dotted circles around the robots are the sensor range areas, and the small white circles are nodes that the robot already passed by. (Iteration: (a) 0, (b) 10, (c) 20, (d) 44).

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Fig. 9

Case 2: progress of the CRE with three robots in the office environment. The thin lines represent walls, the three large circles are robots, and the dotted circles around the robots depict the sensor range areas, and the small white circles represent nodes that the robot already passed by. (Iteration: (a) 0, (b) 10, (c) 20, (d) 31).

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Fig. 10

Case 1: The number of visited nodes per robot (above) and the distance traveled per robot (below). Squares and asterisks are results of CRE algorithm and SRT algorithm, respectively.

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Fig. 11

Case 2: the number of visited nodes per robot (above) and distance traveled per robot (below). Squares and asterisks are results of CRE algorithm and SRT algorithm, respectively.

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Fig. 12

Experiment setting

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Fig. 13

The experiment of CRE with three e-puck robots ((a) 0 s, (b) 11 s, (c) 26 s, (d) 79 s, (e) 121 s)

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