BBoE通过预计算路径片段，快速规划复杂障碍环境中的低代价运动轨迹。
BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning
- 利用预处理的机器人状态传播路径，按顺序排序以优化探索与利用
- 相比以往方法，规划时间更短、解的质量更高、成功率提升明显
- 适合需要快速生成可行轨迹的机器人动态路径规划场景
本文提出BBoE，一种双向、动力学约束、基于采样的运动规划算法，在不同障碍物密集度的环境中均能快速且稳定地找到低代价路径。该算法结合探索与利用策略，依赖于预先计算的机器人状态转移序列，实现高效收敛。核心贡献包括：(i) 通过排序和调度预处理的前向传播路径，有效穿越高障碍密度区域；(ii) BBoE算法采用该策略，生成快速且可行的运动解。与现有方法相比，该框架显著降低规划时间、减少解的代价并提高成功概率。
原文摘要 · Abstract (English)
In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds low-cost solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot state traversals, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to navigate through obstacle-rich spaces by sorting and sequencing preprocessed forward propagations; and ii) BBoE, a robust bidirectional kinodynamic planner that utilizes this strategy to produce fast and feasible solutions. The proposed framework reduces planning time, diminishes solution cost and increases success rate in comparison to previous approaches.
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