Title: Real-time Game Theory Based Artificial Potential Field Method for Multiple Unmanned Aerial Vehicles Path Planning
Author(s): Yuan Sun and Li Fu
Published in: Proceedings of the 2018 International Technical Meeting of The Institute of Navigation
January 29 - 1, 2018
Hyatt Regency Reston
Reston, Virginia
Pages: 521 - 528
Cite this article: Sun, Yuan, Fu, Li, "Real-time Game Theory Based Artificial Potential Field Method for Multiple Unmanned Aerial Vehicles Path Planning," Proceedings of the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, January 2018, pp. 521-528.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In
Abstract: With the popularization of UAV applications, the cooperation of multiple UAVs attracts more and more attention in the community of robotics to improve the mission efficiency and range. In practical applications, a high performance path planning result is critical to the security and efficiency of the multiple UAVs cooperation. Among these path planning methods, Artificial Potential Field Method (APFM) is simple and real-time, which has been commonly used for UAVs path planning. However, some problems may exist in APFM, such as the local optimum problem, which may make the UAV falling into a deadlock and moving unsteadily near the obstacles. To solve the problem of the existed APFM, a real-time game theory based APFM for multiple UAVs path planning is proposed in this paper. First, the artificial potential forces of each UAV are built when the UAVs move through the path. Then, to solve the problem of local minima, the collision cone of the UAV caused by the obstacles and other UAVs is applied to estimate the collision risk. Finally, game theory is applied to optimize the path planning for multiple UAVs with low overall collision risk. Experimental results show that our proposed real-time game theory based APFM for multiple UAVs can overcome the local minima of APFM and obtain more optimized path planning results and lower overall collision risk, when compared with the existed APFM for multiple UAVs.