Path Planning for Mobile Robots in 3D Dynamic Environments

Abstract:

Aiming at the robustness of the path planning of mobile robots in the 3D dynamic environment, an improved ARF (Artificial Potential Field) based path planning algorithm is proposed in this paper. Supposing that all the obstacles move regularly and the robot is on uniform motion in a grid 3D environment. Firstly, the algorithm computes the future statuses of the environment, such as the coordinate of all the obstacles and the goal, until a time step T in which there is at least one route between the start and goal. T is obtained by BFS (Breadth First Search) and environment configuration parameters. Secondly, because in every time step the environment can be consider as being static, ARF is used to determine the potential value of every space position in each time step. Finally, a route along the lowest potential values is found for the robot from the start to goal. Simulation results show that the algorithm makes the robot avoid obstacles effectively and reach the goal safely.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 1401-1404
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.1401
• Online since: November 2011
• Authors: Li Jia Chen, He Jin, Jin Ke Bai, Hai Tao Mao
• Keywords: 3D Dynamic Envrionment, Mobile Robot, Path Planning, Potential Value

References

[1] C. Zhang, Y. Xi. Rolling path planning of mobile robot in dynamic unknow environment, Robot, 2002, vol. 24, pp.71-75.

[2] C. You, J. Han. A path planning approach for mobile robot in 3D dynamic enviroment, Computer Application and Software, 2009, vol. 26, pp.223-225.

[3] J. Yu, S. Cheng. An optimal algorithm of 3D path planning for mobile robots, Journal of Central South University, 2009, vol. 40, pp.471-477.

[4] X. Hu. Path planning in 3-D space for robot based on ant colony algorithm, Journal of ChongQing University (Natural Science Edition), 2004, vol. 27, pp.132-135.

[5] B. Thrun. A Integrating grid-based and topological maps for mobile robot navigation, Proceedings of the AAAI Thirteenth National Conference on Artificial Intelligence. Vol 2, Portland, OR. (1996).

[6] S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. SecondEdition. Englewood Cliffs, NJ: Prentie Hall. (1995).

[7] O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots, International Journal of Robotics Research. 1996, vol. 5, pp.90-98.

DOI: 10.1177/027836498600500106

[8] W. Wu, Q.S. Zheng, J.B. Mbede, H. Xinhan. Research on Path Planning for Mobile Robot among Dynamic Obstacles, Joint 9th IFSA World Congress and 20th NAFIPS International Conference. (2001).

DOI: 10.1109/nafips.2001.944699