Dexterous manipulation planning using probabilistic roadmaps in continuous grasp subspaces

Abstract

Abstract-In this paper, we propose a new method for the motion planning problem of rigid object dexterous manipulation with a robotic multi-fingered hand, under quasi-static movement assumption. This method computes both object and finger trajectories as well as the finger relocation sequence. Its specificity is to use a special structuring of the research space that allows to search for paths directly in the particular subspace GSn which is the subspace of all the grasps that can be achieved with n grasping fingers. The solving of the dexterous manipulation planning problem is based upon the exploration of this subspace. The proposed approach captures the connectivity of GSn in a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method.

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

“…Very powerful methods such as those based on probabilistic roadmap and its variants [25,26] use a C-space description of the environment and graph based methods for search. Another approach to the same problem has been adopted by using various control schemes in conjunction with offline grasp planners [21] or visual tracking systems [27].…”

Finger Readjustment Algorithm for Object Manipulation Based on Tactile Information

“…In [14], a random sampling process of the system object-hand is developed in order to obtain a graph which represents the topology of all the possible object-hand configurations. In both systems, the dexterous manipulation problem is solved by an A* search of a path in the RRT or the graph.…”

Grasp point optimization by online exploration of unknown object surface

“…Grasp poses optimized w.r.t. certain quality criteria [4] become arranged in a pose graph [5] to plan manipulation sequences using state-ofthe-art motion planning methods like RRT [6] or PRM [7], tackling e.g. the problem of screwing a light bulb [8].…”