Inverse kinematics-based motion planning for underactuated systems

We study the problem of generating motion plans for kinematically controllable underactuated systems in environments cluttered with obstacles. We develop a computationally efficient motion planning algorithm that finds fast trajectories by exploiting closed-form inverse kinematics of the robot. The completeness property of the motion planning algorithm can be proven using appropriate metrics defined in the configuration space of the kinematically controllable systems. The snakeboard is used as an example of a kinematically controllable underactuated system to test the motion planning algorithm, and motion plans have been implemented on an experimental snakeboard.