Sampling-based path planning to cartesian goal positions for a mobile manipulator exploiting kinematic redundancy

A robot path planner is presented which integrates both collision-free path planning and finding an inverse kinematics solution in a single search stage. Thereby the degrees of freedom resulting from redundant robot kinematics with regard to goal positions specified in the Cartesian workspace are used to optimize the path length, whereas the usual two-stage approach of decoupled inverse kinematics computation and path planning in the configuration space fails to exploit this potential. In addition to sampling-based exploration of the configuration space, the proposed algorithm uses a computationally efficient gradient descent method for approaching the Cartesian goal pose. Furthermore, the planner is extended by the possibility to respect end-effector path orientation constraints. A detailed evaluation of the planning performance in comparison with a two-stage planner is presented.