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A Collaborative Hierarchical Online Motion Planner for UAV in an Uncertainty Environment
Hierarchical Motion Planning
Rapidly-exploring Random Tree
|Issue Date:||2021-07-01 19:57:18 (UTC+8)|
Path planning has always been an important topic in the research of UAV automated navigation. The purpose of this work is to consider the safety and efficiency of UAVs when planning and updating the path in real-time during the movement such that the UAV can reach the goal configuration successfully. Global path planning usually requires a great amount of computing for complex or large areas, which may be beyond the onboard computing power of many UAVs. Even if the computation can be done on board, the long planning time may not guarantee the safety of UAV navigation. In this paper, we investigate the pros and cons of various path planning algorithms and choose Rapidly-exploring Random Tree (RRT) as a base planning algorithm for UAVs. We proposed a collaborative hierarchical computing architecture, which uses asynchronous cooperative planning of the computing resources onboard and at the base station. Our architecture aims to tackle the difficulty in single-layer path planning where the immediacy of planning and the optimality of the path can not be ensured at the same time. Our system can be used to plan the path for a UAV in an uncertain environment in real-time and ensure its safety during the flight and the effectiveness of the output path. We have conducted experiments in simulation for a typical UAV under four different environmental conditions. The experimental results show that our method can effectively reduce flight time or path length and choose a more downwind path if the wind field information is provided.
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