  This paper uses a mobile manipulator with a collaborative robotic arm to
manipulate objects beyond the robot's maximum payload. It proposes a
single-shot 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. We
conducted experiments and analyses using simulations and real-world executions
to examine the method's performance. It can successfully find manipulation
motion that met contact, force, and kinematic constraints, thus allowing a
mobile manipulator to move heavy objects while leveraging supporting forces
from environmental obstacles. The mehtod does not need to explicitly analyze
contact states and build contact transition graphs, thus providing a new view
for robotic grasp-less manipulation, non-prehensile manipulation, manipulation
with contact, etc.
