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Tight Collision Probability for UAV Motion Planning in Uncertain Environment by Liu, Tianyu; Zhang, Fu; Gao, Fei; Pan, Jia is a scholarly article available to read on EtoBox.

What is Tight Collision Probability for UAV Motion Planning in Uncertain Environment about?

Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and appro

Author

Liu, Tianyu; Zhang, Fu; Gao, Fei; Pan, Jia

Published

2023

Language

EN