Collision-Free Trajectory Generation and Tracking for UAVs Using Markov Decision Process in a Cluttered Environment
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A collision-free trajectory generation and tracking method capable of re-planning unmanned aerial vehicle (UAV) trajectories can increase flight safety and decrease the possibility of mission failures. In this paper, a Markov decision process (MDP) based algorithm combined with backtracking method is presented to create a safe trajectory in the case of hostile environments. Subsequently, a differential flatness method is adopted to smooth the profile of the rerouted trajectory for satisfying the UAV physical constraints. Lastly, a flight controller based on passivity-based control (PBC) is designed to maintain UAV’s stability and trajectory tracking performance. Simulation results demonstrate that the UAV with the proposed strategy is capable of avoiding obstacles in a hostile environment.
KeywordsCollision-free Differential flatness Markov decision process (MDP) Passivity-based control (PBC) Unmanned aerial vehicle (UAV)
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This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, in part by the National Natural Science Foundation of China under Grant 61573282 and Grant 61603130. The authors would like to express their sincere gratitude to the Editor-in-Chief, the Guest Editors, and the anonymous reviewers whose insightful comments have helped to improve the quality of this paper considerably.
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