Skip to main content Accessibility help

Four-Direction Search Scheme of Path Planning for Mobile Agents

  • Kene Li (a1) (a2), Chengzhi Yuan (a2), Jingjing Wang (a3) and Xiaonan Dong (a2)


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.


Corresponding author

*Corresponding author. E-mails:,


Hide All
1.Kang, S. M. and Ahn, H. S., “Shape and orientation control of moving formation in multi-agent systems without global reference frame,” Automatica 92, 210216 (2018).
2.Hu, Y., Li, D., He, Y. and Han, J., “Path planning of UGV based on Bézier curves,” Robotica, 37(6), 969997 (2019).
3.Macharet, D. G. and Campos, M. F. M., “A survey on routing problems and robotic systems,” Robotica 36(12), 17811803 (2018).
4.Krogh, B. H. and Feng, D., “Dynamic generation of subgoals for autonomous mobile robots using local feedback information,” IEEE Trans. Automat. Contr. 34(5), 483493 (1989).
5.Do, K. D., “Global output-feedback path-following control of unicycle-type mobile robots: A level curve approach,” Robot. Auton. Syst. 74, 229242 (2015).
6.Tuncer, A. and Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng. 38(6), 15641572 (2012).
7.Henkel, C., Bubeck, A. and Xu, W., “Energy efficient dynamic window approach for local path planning in mobile service robotics,” IFAC-PapersOnLine 49(15), 3237 (2016).
8.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), 351366 (2017).
9.Raja, P. and Pugazhenthi, S., “Optimal path planning of mobile robots: A review,” Int. J. Phys. Sci. 7(9), 13141320 (2012).
10.Yu, J. L., Valeri, K. and Hiroyuki, N., “Path planning algorithm for car-like robot and its application,” Chin. Quart. J. Math. 17(3), 98104 (2002).
11.Wei, G. W. and Fu, M. Y., “An algorithm based on neural network for mobile robot path planning,” Comput. Simulat. 27(7), 112116 (2010).
12.Kroumov, V., Yu, J. and Negishi, H., “Optimal path planner for mobile robot in 2D environment,” J. Syst. Cybernet. Inform. 2, 4551 (2004).
13.Zhang, Q., Chen, D. and Chen, T., “An obstacle avoidance method of soccer robot based on evolutionary artificial potential field,” Energ. Procedia 16, 17921798 (2012).
14.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).
15.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, 1222 (2016).
16.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), 47934805 (2018).
17.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, 113 (2018).
18.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), 41104125 (2018).
19.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, 234241 (2019).
20.Xiang, Q., Liao, B., Xiao, L. and Jin, L., “A noise-tolerant Z-type neural network for time-dependent pseudoinverse matrices,” Optik 165, 1628 (2018).


Four-Direction Search Scheme of Path Planning for Mobile Agents

  • Kene Li (a1) (a2), Chengzhi Yuan (a2), Jingjing Wang (a3) and Xiaonan Dong (a2)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed