Global path planning with end-effector constraints

Our research is mainly concerned with the path planning problem with general end-eflector constraints (PPGEC) for robot manipulators with many degrees of freedom. For example, a robot manipulator holding a glass of water should keep the glass vertically up all the time, a constraint on end-effector orientation; or, in some other cases, the end-effector may be constrained to move in a plane, a constraint on end-effector position. In this thesis, we show that there are two approaches to deal with the PPGEC problem. The first approach is adapted from the existing randomized gradient descent method [33] for closed-chain robots. The second approach is a new planning algorithm called ATACE, Alternate Task-space And C-space Exploration. Unlike the first approach which works only in the configuration space, ATACE works in both the task space and the configuration space. Instead of finding a path in the configuration space directly, ATACE finds an end-effector path in the task space, and then computes the corresponding configuration space path by tracking this end-effector path. In our simulation environment, we have implemented and compared these two a p proaches. With intuitive explanations, we outline scenarios where one planner is better than the other