A kinematics-based probabilistic roadmap method for high DOF closed chain systems

We consider the motion planning problem for arbitrary articulated structures with one or more closed kinematic chains in a workspace with obstacles. This is an important class of problems and there are applications in many areas such as robotics, closed molecular chains, graphical animation, and reconfigurable robots. We use the kinematics-based probabilistic roadmap (KBPRM) strategy proposed in [Han, L and Amato, NM (March 2000)] that conceptually partitions the linkage into a set of open chains and applies random generation methods to some of the chains and traditional inverse kinematics methods to the others. The efficiency of the method depends critically on how the linkage is partitioned into open chains. The original method assumed the partition was provided as input to the problem. We propose a fully automated method for partitioning an arbitrary linkage into open chains and for determining which should be positioned using the inverse kinematic solver. Even so, the size (number of links) of the closed loops that can be handled by this method is limited because the inverse solver can only be applied to small chains. To handle high dof closed loops, we show how we can use the iterative relaxation of constraints (IRC) strategy proposed by Bayazit to efficiently handle large loops while still only using inverse kinematics for small chains. Our results in 3-dimensional workspaces both for planar and spatial linkages show that our framework performs well for general linkages. We also use our planner to simulate an adjustable lamp called Luxo. Using IRC, our planner can handle a single loop of up to 98 links.