A self-organizing state trajectory planner applied to an anthropomorphic robot hand

Abstract

An incremental self-organizing map, called State Trajectory Generator (STRAGEN) is employed to plan state trajectories of a robot. STRAGEN can deal with different criteria to construct topological maps of the problem space, choosing neighbors that match these criteria and optimize different measures of the learned map. STRAGEN can also learn heterogeneous information, such as angles, torques and positions of a manipulator, preserving their characteristics. This algorithm was tested by generating trajectories for a robotic hand called Kanguera. Kanguera presents a new concept of anthropomorphic robot hand. The hand offers a suitable environment for experimental purposes due to its novel and more accurate transmission system. The implementation of adduction and abduction capacity for both the fingers and the thumb allows the execution of more complex movements. Simulations and experiments related to Kanguera hardware are also presented.