Optimal path planning is prone to convergence to local, rather than global, optima. This is often the case for mobile manipulators due to nonconvexities induced by obstacles, robot kinematics and constraints. This paper focuses on planning under end effector path constraints and attempts to circumvent the issue of converging to a local optimum. We propose a pipeline that first discovers multiple homotopically distinct paths, and then optimizes them to obtain multiple distinct local optima. The best out of these distinct local optima is likely to be close to the global optimum. We demonstrate the effectiveness of our pipeline in the optimal path planning of mobile manipulators in the presence of path and obstacle constraints.

翻译：最优路径规划容易收敛于局部最优而非全局最优。对于移动机械臂而言，由于障碍物、机器人运动学及约束条件导致的非凸性，这种情况尤为常见。本文聚焦于末端执行器路径约束下的规划问题，旨在规避收敛至局部最优解的问题。我们提出一种流程框架：首先发现多条同伦意义下不同的路径，随后对其进行优化以获得多个不同的局部最优解。这些不同局部最优解中的最佳结果很可能接近全局最优解。我们通过存在路径约束与障碍约束的移动机械臂最优路径规划实例，验证了所提流程框架的有效性。