Avoiding space robot collisions utilizing the NASA/GSFC tri-mode skin sensor

A capacitance based proximity sensor, the 'Capaciflector' (Vranish 92), has been developed at the Goddard Space Flight Center of NASA. We had investigated the use of this sensor for avoiding and maneuvering around unexpected objects (Mahalingam 92). The approach developed there would help in executing collision-free gross motions. Another important aspect of robot motion planning is fine motion planning. Let us classify manipulator robot motion planning into two groups at the task level: gross motion planning and fine motion planning. We use the term 'gross planning' where the major degrees of freedom of the robot execute large motions, for example, the motion of a robot in a pick and place type operation. We use the term 'fine motion' to indicate motions of the robot where the large dofs do not move much, and move far less than the mirror dofs, such as in inserting a peg in a hole. In this report we describe our experiments and experiences in this area

A Single-Query Manipulation Planner

In manipulation tasks, a robot interacts with movable object(s). The configuration space in manipulation planning is thus the Cartesian product of the configuration space of the robot with those of the movable objects. It is the complex structure of such a "Composite Configuration Space" that makes manipulation planning particularly challenging. Previous works approximate the connectivity of the Composite Configuration Space by means of discretization or by creating random roadmaps. Such approaches involve an extensive pre-processing phase, which furthermore has to be re-done each time the environment changes. In this paper, we propose a high-level Grasp-Placement Table similar to that proposed by Tournassoud et al. (1987), but which does not require any discretization or heavy pre-processing. The table captures the potential connectivity of the Composite Configuration Space while being specific only to the movable object: in particular, it does not require to be re-computed when the environment changes. During the query phase, the table is used to guide a tree-based planner that explores the space systematically. Our simulations and experiments show that the proposed method enables improvements in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl

Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation

Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201