Motion and Force Planning for Manipulating Heavy Objects by Pivoting
This work provides a method for robots to manipulate objects heavier than their payload limits, which is a significant problem for robotics in manufacturing and logistics. It is an incremental improvement on existing manipulation techniques.
This paper addresses the challenge of manipulating heavy objects beyond a robot's payload by using a sequence of pivoting motions. It proposes an algorithmic approach for automated plan generation, including manipulator joint angles, object poses, and joint torques, by exploiting the subgroup structure of pivoting motions within SE(3).
Manipulation of objects by exploiting their contact with the environment can enhance both the dexterity and payload capability of robotic manipulators. A common way to manipulate heavy objects beyond the payload capability of a robot is to use a sequence of pivoting motions, wherein, an object is moved while some contact points between the object and a support surface are kept fixed. The goal of this paper is to develop an algorithmic approach for automated plan generation for object manipulation with a sequence of pivoting motions. A plan for manipulating a heavy object consists of a sequence of joint angles of the manipulator, the corresponding object poses, as well as the joint torques required to move the object. The constraint of maintaining object contact with the ground during manipulation results in nonlinear constraints in the configuration space of the robot, which is challenging for motion planning algorithms. Exploiting the fact that pivoting motion corresponds to movements in a subgroup of the group of rigid body motions, SE(3), we present a novel task-space based planning approach for computing a motion plan for both the manipulator and the object while satisfying contact constraints. We also combine our motion planning algorithm with a grasping force synthesis algorithm to ensure that friction constraints at the contacts and actuator torque constraints are satisfied. We present simulation results with a dual-armed Baxter robot to demonstrate our approach.