By Rainer Jäkel, Steffen W. Rühl, Sven R. Schmidt-Rohr (auth.), Bruno Siciliano (eds.)
Dexterous and independent manipulation is a key expertise for the private and repair robots of the long run. Advances in Bimanual Manipulation edited through Bruno Siciliano offers the robotics neighborhood with the main visible result of the four-year eu venture DEXMART (DEXterous and self sufficient dual-arm hand robot manipulation with clever sensory-motor talents: A bridge from ordinary to man made cognition). the quantity covers a number of hugely very important subject matters within the box, excited about modelling and studying of human manipulation abilities, algorithms for activity making plans, human-robot interplay, and greedy, in addition to layout of dexterous anthropomorphic palms. the consequences defined during this five-chapter assortment are believed to pave the best way in the direction of the improvement of robot structures endowed with dexterous and human-aware dual-arm/hand manipulation abilities for gadgets, working with a excessive measure of autonomy in unstructured real-world environments.
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Extra info for Advanced Bimanual Manipulation: Results from the DEXMART Project
Grasping and object geometries are not considered in this approach. The extension of a motion planner to account for re-grasp operations is proposed in . The system considers kinematics, collisions and grasps, but is limited 24 R. J¨akel et al. to the domain of pick-and-place with re-grasp operations. A similar approach for pick-and-place operations in complex environments is taken in . In , a manipulation planer based on probabilistic road maps is presented. It accounts for grasps, object placement and collisions.
Choi et al.  propose a system, where action symbols are automatically generated by a motion planner. Point pairs are sampled from the workspace of a robot. A path planner is utilized to ensure that there is a path connecting those points. If there is one, a pick-and-place action transporting an object between those points is generated. This approach has difficulties to scale to real world complexity. A reverse approach is taken by Dornhege et al. . A symbolic planner plans with an action, which can approach arbitrary locations.
Mechanical relations are used to generate and transfer mechanical domain knowledge into the planning domain, for example that moving an object A, which carries another object B, will also move B. For learned actions, a free-space representation is developed, which is used as a precondition for those actions. Layered PbD and Planning for Autonomous Robot Manipulation (a) (d) (b) (e) 25 (c) (f) Fig. 23 Top row: Graspability for the Sauerkraut object. Three different stable planes are visualized. The bottom row shows the corresponding orientations of the Sauerkraut tin.