Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-13T15:59:42.331Z Has data issue: false hasContentIssue false
  • English
  • Français

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: dong_xn@uri.edu
Chengzhi Yuan*
Affiliation:
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA. E-mail: dong_xn@uri.edu
Jingjing Wang
Affiliation:
Department of Computer Vocational Education, Guangxi Science and Technology Normal University, Laibin 546199, China. E-mail: 1195378470@qq.com
Xiaonan Dong
Affiliation:
Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA. E-mail: dong_xn@uri.edu
*
*Corresponding author. E-mails: likene@163.com, cyuan@uri.edu
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

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

Information

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

References

Kang, S. M. and Ahn, H. S., “Shape and orientation control of moving formation in multi-agent systems without global reference frame,” Automatica 92, 210–216 (2018).CrossRefGoogle Scholar
Hu, Y., Li, D., He, Y. and Han, J., “Path planning of UGV based on Bézier curves,” Robotica, 37(6), 969–997 (2019).CrossRefGoogle Scholar
Macharet, D. G. and Campos, M. F. M., “A survey on routing problems and robotic systems,” Robotica 36(12), 1781–1803 (2018).CrossRefGoogle Scholar
Krogh, B. H. and Feng, D., “Dynamic generation of subgoals for autonomous mobile robots using local feedback information,” IEEE Trans. Automat. Contr. 34(5), 483–493 (1989).CrossRefGoogle Scholar
Do, K. D., “Global output-feedback path-following control of unicycle-type mobile robots: A level curve approach,” Robot. Auton. Syst. 74, 229–242 (2015).CrossRefGoogle Scholar
Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 1564–1572 (2012).CrossRefGoogle Scholar
Henkel, C., Bubeck, A. and Xu, W., “Energy efficient dynamic window approach for local path planning in mobile service robotics,” IFAC-PapersOnLine 49(15), 32–37 (2016).CrossRefGoogle Scholar
Dao, T. K., Pan, J. S., Pan, T. S. and Nguyen, T. T., “Optimal path planning for motion robots based on bees pollen optimization algorithm,” J. Inform. Telecommun. 1(4), 351–366 (2017).CrossRefGoogle Scholar
Raja, P. and Pugazhenthi, S., “Optimal path planning of mobile robots: A review,” Int. J. Phys. Sci. 7(9), 1314–1320 (2012).CrossRefGoogle Scholar
Yu, J. L., Valeri, K. and Hiroyuki, N., “Path planning algorithm for car-like robot and its application,” Chin. Quart. J. Math. 17(3), 98–104 (2002).Google Scholar
Wei, G. W. and Fu, M. Y., “An algorithm based on neural network for mobile robot path planning,” Comput. Simulat. 27(7), 112–116 (2010).Google Scholar
Kroumov, V., Yu, J. and Negishi, H., “Optimal path planner for mobile robot in 2D environment,” J. Syst. Cybernet. Inform. 2, 45–51 (2004).Google Scholar
Zhang, Q., Chen, D. and Chen, T., “An obstacle avoidance method of soccer robot based on evolutionary artificial potential field,” Energ. Procedia 16, 1792–1798 (2012).CrossRefGoogle Scholar
Bloise, N., Capello, E., Dentis, M. and Punta, E., “Obstacle avoidance with potential field applied to a rendezvous maneuver,” Appl. Sci. 7(10), 1042 (2017).CrossRefGoogle Scholar
Hoang, N. B. and Kang, H. J., “Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force,” Neurocomputing 188, 12–22 (2016).CrossRefGoogle Scholar
Zhang, Z., Zheng, L. and Guo, Q., “A varying-parameter convergent neural dynamic controller of multirotor UAVs for tracking time-varying tasks,” IEEE Trans. Veh. Technol. 67(6), 4793–4805 (2018).CrossRefGoogle Scholar
Zhang, Z. and Zheng, L., “A complex varying-parameter convergent-differential neural-network for solving online time-varying complex Sylvester equation,” IEEE Trans. Cybern. 99, 1–13 (2018).CrossRefGoogle Scholar
Zhang, Z., Lu, Y., Zheng, L., Li, S., Yu, Z. and Li, Y., “A new varying-parameter convergent-differential neural-network for solving time-varying convex QP problem constrained by linear-equality,” IEEE Trans. Automat. Contr. 63(12), 4110–4125 (2018).CrossRefGoogle Scholar
Liao, B., Xiang, Q. and Li, S., “Bounded Z-type neurodynamics with limited-time convergence and noise tolerance for calculating time-dependent Lyapunov equation,” Neurocomputing 325, 234–241 (2019).CrossRefGoogle Scholar
Xiang, Q., Liao, B., Xiao, L. and Jin, L., “A noise-tolerant Z-type neural network for time-dependent pseudoinverse matrices,” Optik 165, 16–28 (2018).CrossRefGoogle Scholar