In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRT\(^\text {X}\), which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.

在自主移动机器人领域，基于采样的运动规划方法已经证明了其在复杂环境中的效率。尽管快速探索随机树（RRT）算法及其变体在已知的静态环境中取得了显着的成功，但在未知的动态环境中实现最优运动规划仍然具有挑战性。为了解决这个问题，本文提出了一种新颖的运动规划算法 Bi-HS-RRT\(^\text {X}\)，该算法有助于在不断变化的未知环境中进行渐近最优实时规划。该算法通过采用双向搜索快速确定初始可行路径。当动态障碍物导致规划路径不可行时，及时重新启动双向搜索，重建局部区域的搜索树，从而显着减少搜索规划时间。此外，本文采用混合启发式采样策略来优化规划路径质量和搜索效率。通过将超椭球区域中的局部偏置采样与标称路径和全局启发式采样相结合，加速了该算法的收敛。为了验证该算法在未知动态环境下的有效性和效率，与现有算法进行了大量的对比实验。实验结果表明，所提出的规划算法在规划路径长度和规划时间上具有显着优势。