UAS Flight Path Planning for Dynamic, Multi-Vehicle Environment
Tong He, Iraj Mantegh, Long Fei Chen, Charles Vidal, Wenfang Xie · 2020
This paper proposes a new path planning method for Unmanned Aerial Systems (UAS) flying in a dynamic 3D environment shared by multiple aerial vehicles posing potential conflict risks. It primarily targets applications such as Urban Aerial Mobility (UAM). A new multi-staged algorithm is designed that combines Artificial Potential Field (AFP) method and Harmonic functions with Kalman filtering and Markov Decision Process (MDP) for dynamic path planning. It starts with estimating the aircraft traffic density in the area and then generates the UAS flight path in a way to minimize the risk of encounters. Hardware-in-the-loop simulations of the algorithm in various scenarios are presented, with a RGB-D camera (RealSense™) and Pixhawk autopilot to track the target. Numerical simulations show satisfactory results for path planning in various scenarios with increasing degree of complexity.