Non-prehensile manipulation methods usually use a simple end effector, e.g., a single rod, to manipulate the object. Compared to the grasping method, such an end effector is compact and flexible, and hence it can perform tasks in a constrained workspace; As a trade-off, it has relatively few degrees of freedom (DoFs), resulting in an under-actuation problem with complex constraints for planning and control. This paper proposes a new non-prehensile manipulation method for the task of object retrieval in cluttered environments, using a rod-like pusher. Specifically, a candidate trajectory in a cluttered environment is first generated with an improved Rapidly-Exploring Random Tree (RRT) planner; Then, a Model Predictive Control (MPC) scheme is applied to stabilize the slider's poses through necessary contact with obstacles. Different from existing methods, the proposed approach is with the contact-aware feature, which enables the synthesized effect of active removal of obstacles, avoidance behavior, and switching contact face for improved dexterity. Hence both the feasibility and efficiency of the task are greatly promoted. The performance of the proposed method is validated in a planar object retrieval task, where the target object, surrounded by many fixed or movable obstacles, is manipulated and isolated. Both simulation and experimental results are presented.

翻译：非抓取式操作方法通常使用简单末端执行器（如单根杆件）操控物体。相较于抓取方法，这类末端执行器结构紧凑且灵活，可在受限空间内执行任务；但代价是其自由度相对较少，导致规划与控制面临复杂的欠驱动约束问题。本文针对杂乱环境中的目标物体提取任务，提出一种基于推杆的新型非抓取式操作方法。具体而言，首先通过改进的快速扩展随机树（RRT）规划器生成杂乱环境中的候选轨迹；随后采用模型预测控制（MPC）策略，通过必要的障碍物接触来稳定滑块的位姿。与现有方法不同，所提方法具备接触感知特性，能够综合实现主动排除障碍物、避碰行为以及切换接触面以提升灵巧性的效果，从而显著提升任务的可行性与效率。该方法在平面目标提取任务中完成性能验证，该任务需将受固定或可移动障碍物包围的目标物体进行操控分离。文中给出了仿真与实验结果。