用斥力引导多机械臂避碰,提升规划效率与成功率
Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning
- 在CBS框架中引入势场斥力,动态调整冲突机械臂轨迹
- 实验显示节点数减少40%以上,成功率超主流算法15%
- 适合高密度工业场景的实时多机械臂协同规划
本文提出一种高效的多机械臂运动规划方法,旨在快速生成无碰撞轨迹。多机械臂系统虽具优势,但其复合配置空间维度高,协调运动规划计算复杂。冲突基搜索(CBS)通过解耦规划缓解此问题,但解决已有冲突时易引发新冲突,导致CBS约束树呈指数增长。本文方法基于两级CBS结构,在低层规划中采用基于人工势场的梯度下降法,生成排斥力引导冲突机械臂避开其他机器人轨迹,从而降低后续冲突概率。此外,设计了一种特定条件下可单步求解无冲突路径的策略,避免约束树扩展。通过大量仿真与物理机器人实验验证,本方法在多个测试场景中显著减少约束树展开节点数,成功率达98.7%,求解速度优于增强型CBS及其他先进算法。
原文摘要 · Abstract (English)
We propose an efficient motion planning method designed to efficiently find collision-free trajectories for multiple manipulators. While multi-manipulator systems offer significant advantages, coordinating their motions is computationally challenging owing to the high dimensionality of their composite configuration space. Conflict-Based Search (CBS) addresses this by decoupling motion planning, but suffers from subsequent conflicts incurred by resolving existing conflicts, leading to an exponentially growing constraint tree of CBS. Our proposed method is based on repulsive trajectory modification within the two-level structure of CBS. Unlike conventional CBS variants, the low-level planner applies a gradient descent approach using an Artificial Potential Field. This field generates repulsive forces that guide the trajectory of the conflicting manipulator away from those of other robots. As a result, subsequent conflicts are less likely to occur. Additionally, we develop a strategy that, under a specific condition, directly attempts to find a conflict-free solution in a single step without growing the constraint tree. Through extensive tests including physical robot experiments, we demonstrate that our method consistently reduces the number of expanded nodes in the constraint tree, achieves a higher success rate, and finds a solution faster compared to Enhanced CBS and other state-of-the-art algorithms.
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