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This paper uses a mobile manipulator with a collaborative robotic arm tomanipulate objects beyond the robot's maximum payload. It proposes asingle-shot probabilistic roadmap-based method to plan and optimizemanipulation motion with environment support. The method uses an expandedobject mesh model to examine contact and randomly explores object motion whilekeeping contact and securing affordable grasping force. It generates roboticmotion trajectories after obtaining object motion using an optimization-basedalgorithm. With the proposed method's help, we can plan contact-richmanipulation without particularly analyzing an object's contact modes and theirtransitions. The planner and optimizer determine them automatically. Weconducted experiments and analyses using simulations and real-world executionsto examine the method's performance. It can successfully find manipulationmotion that met contact, force, and kinematic constraints, thus allowing amobile manipulator to move heavy objects while leveraging supporting forcesfrom environmental obstacles. The mehtod does not need to explicitly analyzecontact states and build contact transition graphs, thus providing a new viewfor robotic grasp-less manipulation, non-prehensile manipulation, manipulationwith contact, etc.

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