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Title: 不確定的環境中無人機的階層式協力運動規劃
A Collaborative Hierarchical Online Motion Planner for UAV in an Uncertainty Environment
Authors: 蔡苡雋
Tsai, Yi-Chuan
Contributors: 李蔡彥
Li, Tsai-Yen
Tsai, Yi-Chuan
Keywords: 無人機
Hierarchical Motion Planning
Path Planning
Rapidly-exploring Random Tree
Date: 2021
Issue Date: 2021-07-01 19:57:18 (UTC+8)
Abstract: 路徑規劃一直是無人機自動化研究中重要的課題,其目的在於確保無人機的安全性及效率,在移動中隨時更新與即時規劃路徑,讓無人機順利到達目標點。無人機的機上運算對於複雜或廣大的區域,需要消耗很多計算時間,而對於無人機的安全性來說,需要較短的規劃時間來達到飛行安全的目的。在本論文中,我們探討了不同的路徑規劃演算法的優劣勢,並決定採用快速搜索隨機樹(RRT)作為無人機的路徑規劃演算法,且提出了一種階層式協力計算架構,此架構利用機上運算與基地台的非同步合作規劃,嘗試解決在單層路徑規劃上較難同時確保規劃即時性與路徑最優性的問題。本系統可用於無人機行駛在不確定的環境中即時規劃路徑,並確保無人機在飛行時的安全,及產出有效率的路徑。我們以四種不同環境條件下進行即時模擬飛行實驗,實驗結果顯示本架構可有效降低飛行花費時間或是飛行路徑長度,並且能在有提供風場資訊的環境中,選擇更順風的路徑飛行。
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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