Improving efficiency of intricate manipulation planning through mapping of grasp feasibility zones

One intuitive approach for planning robotic manipulation tasks is to first compute a plan for the manipulated object, as if capable to move on its own, then use the obtained plan to find a sequence of arm maneuvers to take the object along the computed plan. Motion planning queries would be performed in relatively low-dimensional configuration spaces, rather than the full configuration space of a robot's arms and grippers, resulting in a more efficient planning method. However, having a plan for the manipulated object does not guarantee there also exists a feasible sequence of maneuvers for the robot to do the manipulation. Knowing different possible grasps on the object increases the robots chance to find a sequence of grasp switches and maneuvers, but makes the search more time consuming as the grasps need to be tested for usefulness. In this paper, we develop a multi-level architecture for complex manipulation planning of rigid bodies which uses communication between the two levels, one for planning the manipulated object motion, the other to plan for the arms, to improve the efficiency of the method. We store grasp zones in the configuration space of the manipulated object as regions where a given grasp seems promising. We use grasp zones to guide searching for grasp switching maneuvers, and to avoid regions of configuration space where few good grasps exist.

I. Introduction

One of the tasks a robot has to do, if operating in a human environment, is to tackle manipulation problems that are second nature to humans. Objects often need to be entangled with, or disentangled from, one another; putting a key or removing it from a key chain, unhooking a hammer from a tool rack, or tying a knot would be some examples. These problems often require narrow features for their solutions and grasp changes during object manipulation.