Abstract Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.

中文翻译：

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目标偏差通知树：基于采样的复杂环境中最优运动规划方法

摘要 针对渐进优化的快速探索随机树星(RRT*)算法在高维复杂环境下产生大量冗余节点，导致收敛速度慢、搜索效率低的问题。在本文中，我们提出了基于目标偏向采样策略和启发式优化策略的改进的 RRT* 路径规划算法，即目标偏向知情树 (TBIT*)。该算法在寻找初始路径的搜索阶段采用了组合目标偏差策略，引导随机树向目标方向快速生长，从而减少了冗余节点的产生，提高了算法的搜索效率；搜索到初始路径后，启发式采样用于优化初始路径而不是优化随机树，可以减少无用计算，提高算法的收敛能力。实验结果表明，本文提出的算法在一定程度上改变了算法的随机性，在复杂环境下的搜索效率和收敛能力都有显着提高，表明改进算法是可行且高效的。