Volume 13 Number 1 (Jan. 2018)
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JCP 2018 Vol.13(1): 58-68 ISSN: 1796-203X
doi: 10.17706/jcp.13.1.58-68

Service Robots Adaptive Mutual-coupled Immune Network Planning Algorithm Research Based on the Distance-weighted

Rong Chen, Hutian Feng
School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.
Abstract—In order to further improve efficiency and accuracy of the multi-service robot path planning in a complex and uncertain environment, based on the mutual-coupled immune network planning algorithm, an improved measurement algorithm was proposed. According to the antigen information of obstacles and the target, situation-oriented and goal-oriented coupling immune network are defined respectively and the weight coefficient is used to control their roles in the overall behavior. In order to further improve the planning performance of the robot, weight coefficient is defined dynamically according to the distance of the robot apart from the obstacles and the target. Thus we are able to optimize the robot’s behavior by choosing the obstacle-avoidance behavior or the tend-to-target behavior in real time. The test results in the static multi-obstacle environment indicate that the algorithm proposed in this paper is more effective than other algorithms in literature, both in the aspects of length and smoothness, which verifies the ability of the algorithm to improve the efficiency and precision of the path planning. And the multi-robot can successfully avoid dynamic obstacles and achieve the targeting result according to the test results in the complex and uncertainty environment, which verifies its flexibility and robustness.

Index Terms—Service robot, path planning, distance weighting, adaptive, immune network.

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Cite: Rong Chen, Hutian Feng, "Service Robots Adaptive Mutual-coupled Immune Network Planning Algorithm Research Based on the Distance-weighted," Journal of Computers vol. 13, no. 1, pp. 58-68, 2018.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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