He, Tong (Concordia University), Mantegh, Iraj (National Research Council Canada), Chen, Long (Concordia University), Vidal, Charles (National Research Council Canada), XIE, WENFANG (Concordia University)

UAS Flight Path Planning for Dynamic, Multi-Vehicle Environment

Scheduled for presentation during the Regular Session "Path Planning II" (WeB2), Wednesday, September 2, 2020, 15:00−15:20, Kozani

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on April 24, 2024

Abstract

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〗^TM ) and Pixhawk autopilot to track the target. Numerical simulations show satisfactory results for path planning in various scenarios with increasing degree of complexity.