Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction-based RRT* method
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ORIGINAL ARTICLE
Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction‑based RRT* method Naifeng Wen1 · Rubo Zhang1 · Junwei Wu1 · Guanqun Liu1 Received: 29 January 2019 / Accepted: 28 May 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract A heuristic Dual sampling domain Reduction-based Optimal Rapidly-exploring Random Tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for an unmanned surface vehicle (USV) under constraints. First, an elliptic sampling domain, which is subject to an elliptic equation and the shortest obstacle avoidance path estimation, is created to plan short paths. Second, by the consideration of the USV motion states, obstacles and external interferences of the current, the near sampling domains of tree nodes are reduced to exclude high-cost sampling domains. Path feasibility is ensured by explicitly handling motion constraints. Third, a safe distance-based collision detection (CD) scheme and a velocity-based bounding box of USV are proposed to decrease the path collision probability. Additionally, a layered USV online path planning framework is built in accordance with the model predictive control method, and the path smoothing scheme is applied via the Dubins curve under the curvature constraint. Results demonstrate that the proposed dual sampling domain reduction method outperforms traditional reduction schemes in terms of improving the execution efficiency of RRT*. Meanwhile, the proposed CD method is more reliable than the conventional one. Keywords Unmanned surface vehicle · Online path planning · Obstacle avoidance · Sampling space reduction · Rapidlyexploring random tree · Collision detection
1 Introduction In practice, a preplanned global path tends to be invalid when the environment is uncertain and varying in time. Thus, the USV system should include an online path planning (OPP) module for a high level of autonomy while adopting a global path as a heuristic to maximize the use of the known environmental information. Several remarkable results have been obtained in this way [1]; however, * Rubo Zhang [email protected] Naifeng Wen [email protected] Junwei Wu [email protected] Guanqun Liu [email protected] 1
School of Electromechanical Engineering, Dalian Minzu University, Dalian, China
research on this topic remains lacking, especially in the improvement of path planning efficiency, the enhancement of obstacle avoidance (OA) accuracy, and the advancement of the OPP framework. These gaps are the main motivation for the present study. The efficiency is the first requirement of the OPP problem since the planning time is limited. The high computational complexity of complete methods limits their online application in complex environments characterized by information changes; irregularly
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