Related papers: DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

URL: http://arxiv.org/abs/2109.07024v1
Date: Tue, 14 Sep 2021 23:51:02 GMT
Title: DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles
Authors: Zhefan Xu, Di Deng, Yiping Dong, Kenji Shimada
Abstract summary: Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. This paper proposes a trajectory planning framework to achieve safe navigation considering complex static environments with dynamic obstacles.
Score: 0.9462808515258462
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. Although plenty of recent works achieve safe navigation in complex static environments with sophisticated mapping algorithms, such as occupancy map and ESDF map, these methods cannot reliably handle dynamic environments due to the mapping limitation from moving obstacles. To address the limitation, this paper proposes a trajectory planning framework to achieve safe navigation considering complex static environments with dynamic obstacles. To reliably handle dynamic obstacles, we divide the environment representation into static mapping and dynamic object representation, which can be obtained from computer vision methods. Our framework first generates a static trajectory based on the proposed iterative corridor shrinking algorithm. Then, reactive chance-constrained model predictive control with temporal goal tracking is applied to avoid dynamic obstacles with uncertainties. The simulation results in various environments demonstrate the ability of our algorithm to navigate safely in complex static environments with dynamic obstacles.