Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments
Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments
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DOI：
10.1109/lra.2017.2783445
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发表时间：
2018-04-01
影响因子：
5.2
通讯作者：
Rosell, Jan
中科院分区：
文献类型：
--
作者：
Muhayyuddin;Moll, Mark;Rosell, Jan
计划在混乱和不确定的环境中掌握对象的动议是一项具有挑战性的任务，尤其是当不存在无碰撞轨迹的情况下，并且需要妨碍方式的物体仔细抓住并移出。该字母采用了不同的方法，并建议通过使用允许机器人对象和对象对象相互作用的基于随机的运动计划者来解决此问题。主要思想是通过向运动计划者提供物理引擎来评估可能的复杂多体动力学相互作用来避免任务明确的高级推理。该方法能够在复杂的场景中解决该问题，还考虑了对象姿势和接触动力学中的不确定性。这项工作增强了状态有效性检查器，控制采样器和称为Kpiece的运动动力运动计划者的树探索策略。增强的算法（称为p-kpiece）在模拟和实际实验中已得到验证。结果已与基于本体物理学的运动计划者以及任务和运动计划方法进行了比较，从而在计划时间，成功率和解决方案路径的质量方面得到了显着提高。
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This letter takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multibody dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects' pose and in the contact dynamics. The work enhances the state validity checker, the control sampler, and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate, and quality of the solution path.