Simultaneous planning and mapping (SPAM) for a manipulator by best next move in unknown environments

In this paper, we propose a SPAM (Simultaneous Planning and Mapping) technique for a manipulator type robot working in an uncertain environment via a Best Next Move algorithm. Demands for a smart decision to move a manipulator such as humanoid arms in uncertain or crowded environments call for a simultaneous planning and mapping technique. We assume no a priori knowledge of either the obstacles or the rest of the environment exits. For rapid map building and path planning, we use a skin type setup based on 3D depth camera sensors that completely encompass the entire body of a manipulator. The 3D sensors capture the point clouds used to create an instantaneous c-space map whereby a Best Next Move algorithm directs the motion of the manipulator. The Best Next Move algorithm utilizes the gradient of the density distribution of the k-nearest-neighborhood sets in c-space. It has tendency to travel along the direction by which the point clouds spread in space, thus rendering faster mapping of c-space obstacles. The proposed algorithm is compared with several sensor based algorithms for performance measurement such as map completion rate, distribution of samples, total nodes, etc. Some improved performances are reported for the proposed algorithm. Several possible applications include semi-autonomous tele-robotics planning, humanoid arm path planning, among others.