Abstract
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.
Abstract (translated)
我们提出了一种高效的运动规划方法，旨在为多个机械臂高效地寻找无碰撞轨迹。虽然多机械臂系统提供了显著的优势，但由于其复合配置空间的高维度，协调它们的动作在计算上具有挑战性。基于冲突的搜索（CBS）通过解耦运动规划来解决这个问题，但这种方法会因解决现有冲突而产生后续冲突，导致CBS约束树呈指数级增长。 我们的方法建立在CBS双层结构中使用排斥轨迹修改的基础上。与传统的CBS变体不同，低级别计划者采用人工势场中的梯度下降方法。该势场生成排斥力，引导冲突机械臂远离其他机器人的轨迹。因此，后续冲突发生的可能性降低。 此外，在特定条件下，我们开发了一种策略，尝试在一步内直接找到无冲突的解决方案，而无需增长约束树。 通过包括物理机器人实验在内的广泛测试，我们证明了我们的方法能够持续减少约束树中的扩展节点数量，并且相比增强版CBS和其他最先进的算法，成功概率更高、更快地找到了解决方案。
URL
https://arxiv.org/abs/2509.13882
