Nonprehensile Robotic Manipulation: Controllability and Planning

Abstract A good model of the mechanics of a task is a resource for a robot, just as actuators and sensors are resources. The effective use of frictional, gravitational, and dynamic forces can substitute for extra actuators; the expectation derived from a good model can minimize sensing requirements. Despite this, most robot systems attempt to dominate or nullify task mechanics, rather than exploit them. There has been little effort to understand the manipulation capabilities of even the simplest robots under more complete mechanics models. This thesis addresses that knowledge deficit by studying graspless or nonprehensile manipulation. Nonprehensile manipulation exploits task mechanics to achieve a goal state without grasping, allowing simple mechanisms to accomplish complex tasks. With nonprehensile manipulation, a robot can manipulate objects too large or heavy to be grasped and lifted, and a low-degree-of-freedom robot can control more degrees-of-freedom of an object by allowing relative motion between the object and the manipulator. Two key problems are determining controllability of and motion planning for nonprehensile manipulation. The first problem is to determine whether the goal state of the object is reachable by nonprehensile manipulation, and the second is to find a manipulator motion to bring the object to the goal state