通过生成拓扑不同的路径，提升机械臂规划的全局最优概率。
Generating and Optimizing Topologically Distinct Guesses for Mobile Manipulator Path Planning with Path Constraints
- 先生成多条拓扑不同的初始路径，避免陷入局部最优。
- 优化后获得多个不同局部最优解，其中最佳解逼近全局最优。
- 适合需高可靠性路径规划的移动机械臂场景。
最优路径规划容易收敛到局部而非全局最优解，尤其对于受障碍物、机器人运动学和约束影响的非凸问题。本文聚焦末端执行器路径约束下的规划问题，提出一种流程：首先发现多条同伦相异的路径，再分别优化以获得多个不同的局部最优解。这些解中表现最佳者很可能接近全局最优。实验验证了该方法在存在路径与障碍物约束条件下，对移动机械臂最优路径规划的有效性。
原文摘要 · Abstract (English)
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.
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