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:本文使用一个带有协作机器人臂的移动操纵器来操纵机器人最大有效载荷以外的物体。 它提出一个单发概率式的路线图基础方法来用环境支持来规划和优化操纵运动。 该方法使用一个扩大的物体网格模型来检查接触, 并随机探索物体运动, 同时保持接触并获得负担得起的控制力。 它在使用优化的算法获得物体动作后产生机器人运动轨迹。 在所提议方法的帮助下, 我们可以规划接触力丰富的操纵, 而不特别分析物体的接触模式及其转变。 规划者和优化者会自动确定它们。 我们使用模拟和现实世界处决来检查方法的性能。 它能够成功找到能够满足接触、 力和运动限制的操纵动作, 从而允许移动操纵器在利用环境障碍的辅助力时移动重物体。 模具不需要明确分析接触状态和构建接触转换图, 从而提供机器人不理解性操纵、 非先导操纵、 与接触的操纵等的新视图 。</s>