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.

翻译：本文研究利用配备协作机械臂的移动操作手搬运超出其最大有效载荷的物体，提出基于单次概率路标图的方法，通过环境支撑实现操作运动的规划与优化。该方法采用扩展物体网格模型检测接触关系，在保持接触状态与可控抓取力的前提下随机探索物体运动轨迹，并运用优化算法生成机械臂运动路径。借助所提方法，可在无需专门分析物体接触模式及其转换的情况下进行接触富集操作规划——规划器与优化器将自动完成相关决策。通过仿真与实物实验验证方法性能：该方法成功生成满足接触约束、力约束与运动学约束的操作轨迹，使移动操作手能借助环境障碍物的支撑力搬运重物。由于无需显式分析接触状态及构建接触过渡图，本方法为机器人无抓取操作、非预抓取操作、接触式操作等领域提供了新视角。