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Four-Direction Search Scheme of Path Planning for Mobile Agents

Published online by Cambridge University Press:  13 June 2019

Kene Li ,
Chengzhi Yuan ,
Jingjing Wang  and
Xiaonan Dong
Kene Li
Affiliation:
Department of Automation, School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China. Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA. E-mail: [email protected]
Chengzhi Yuan*
Affiliation:
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA. E-mail: [email protected]
Jingjing Wang
Affiliation:
Department of Computer Vocational Education, Guangxi Science and Technology Normal University, Laibin 546199, China. E-mail: [email protected]
Xiaonan Dong
Affiliation:
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA. E-mail: [email protected]
*
*Corresponding author. E-mails: [email protected], [email protected]
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Summary

This paper presents a neural network-based four-direction search scheme of path planning for mobile agents, given a known environmental map with stationary obstacles. Firstly, the map collision energy is modeled for all the obstacles based on neural network. Secondly, for the shorted path-search purpose, the path energy is considered. Thirdly, to decrease the path-search time, a variable step-length is designed with respect to collision energy of the previous iteration path. Simulation results demonstrate that the variable step-length is effective and can decrease the iteration time substantially. Lastly, experimental results show that the mobile agent tracks the generated path well. Both the simulation and experiment results substantiate the feasibility and realizability of the presented scheme.

Keywords

Neural networkPath planningObstaclesCollision energyIteration
Type
Articles
Information
Robotica , Volume 38 , Issue 3 , March 2020 , pp. 531 - 540
Copyright
© Cambridge University Press 2019 

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