Path planning with general end-effector constraints: using task space to guide configuration space search

In this paper, we address the path planning problem with general end-effector constraints (PPGEC) for robot manipulators. Two approaches are proposed. The first approach is adapted from an existing randomized gradient descent (RGD) method for closed-chain robots. The second approach is radically different. We call it ATACE alternate task-space and configuration-space exploration. Unlike the first approach which searches purely in C-space, ATACE works in both task space and C-space. It explores the task space for end-effector paths satisfying given constraints, and utilizes trajectory tracking technique(s) as a local planner(s) to track these paths in the configuration space. We have implemented both approaches and compare their relative performances in different scenarios. ATACE outperforms RGD in majority (but not all) of the scenarios. We outline intuitive explanations for the relative performances of these two approaches.