ABSTRACT
In this paper we consider the motion planning prob lem for closed chain systems. We propose an extension of the PRM methodology which uses the kinematics of the closed chain system to guide the generation and connection of closure configurations. In particular, we break the closed chains into a set of open subchains, apply standard PRM random sampling techniques and forward kinematics to one subset of the subchains, and then use inverse kinematics on the remaining subchains to enforce the closure constraints. This strategy pre serves the PRM sampling philosophy, while addressing the fact that the probability that a random configura tion will satisfy the closure constraints is zero, which has proven problematical in previous attempts to apply the PRM methodology to closed chain systems.
