Mobile Robot Dynamic Path Planning Based on Artificial Potential Field Approach

Abstract:

Artificial potential field based mobile robot path planning approaches have been widely used. However, most methods are applied in the static environment where the target and the obstacles are stationary. In this paper, a potential field approach used in dynamic situation is proposed. Its major characteristics include a new attractive potential function as well as a repulsive potential function. The former takes the relative position and velocity between the robot and the target into consideration; the latter takes into account the relative position and velocity between the robot and the obstacles. The proposed approach guarantees the robot can track the moving target while escape from moving obstacles. Simulation experiments are carried out and the results demonstrate the effectiveness of the new potential field method.

• Periodical: Advanced Materials Research (Volumes 490-495)
• Pages: 994-998
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.490-495.994
• Online since: March 2012
• Authors: Pu Shi, Jian Ning Hua
• Keywords: Artificial Potential Field (APF), Dynamic Situation, Mobile Robot, Relative Position, Velocity Potential Function

References

[1] D.H. Kim, Y.J. Kim, K.C. Kim et al.: Vector Field Based Path Planning and Petri-net based Role Selection Mechanism with Q-learning for the Soccer Robot System, Intelligent Automation and Soft Computing Vol. 6 (1) (2000) , p.75–87.

DOI: 10.1080/10798587.2000.10768161

[2] J. Borenstein, Y. Koren: Real-time Obstacle Avoidance for Fast Mobile Robots, IEEE Transactions on Systems, Man, and Cybernetics Vol. 20 (4) (1989), p.1179–1187.

DOI: 10.1109/21.44033

[3] Lu Yin, Yixin Yin: An Improved Potential Field Method for Mobile Robot Path Planning in Dynamic Environments, Proceedings of the 7th World Congress on Intelligent Control and Automation Vol. 11 (2008), p.4847–4852.

DOI: 10.1109/wcica.2008.4593709

[4] S. S. Ge, Y. J. Cui: Dynamic Motion Planning for Mobile Robots Using Potential Field Method, Autonomous Robots Vol. 13 (3) (2002) , p.207–222.

[5] Q. X. Cao, Y. W. Guan and J. L. Zhou: An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot, IEEE/ RSJ Conf. on Intelligent Robots and Systems Vol. 18(2006) , p.331–337.

DOI: 10.1109/iros.2006.282508

[6] M. J. Gao, J. Xu and J. W. Tian: Mobile Robot Global Path Planning Based on Improved Augment Ant Colony Algorithm, Proc. IEEE International Conference on Genetic and Evolutionary Computing Vol. 11(3) (2008) , p.273–276.

DOI: 10.1109/wgec.2008.39

[7] Prahlad Vadakkepat, Kay Chen Tan and Minglian Wang: Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning, Proceedings of the 2000 Congress on Evolutionary Computation Vol. 2 (2001), p.256–263.

DOI: 10.1109/cec.2000.870304

[8] N. E. Leonard, E. Fiorelli: Artificial potentials and coordinated control of groups, Proc. of the 40th IEEE Intl. Conf. on Decision and Control Vol. 3(2) (2001) , p.2968–2973.

DOI: 10.1109/cdc.2001.980728