Implicit Contact-Rich Manipulation Planning for a Manipulator with Insufficient Payload

Purpose of this paper: This paper studies using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot's maximum payload. Design/methodology/approach: The paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method's help, we can plan contact-rich manipulation without particularly analyzing an object's contact modes and their transitions. The planner and optimizer determine them automatically. Findings: We conducted experiments and analyses using simulations and real-world executions to examine the method's performance. The method successfully found manipulation motion that met contact, force, and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. What is original/value of paper: The paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.