Eye in hand: Towards GPU accelerated online grasp planning based on pointclouds from in-hand sensor

Abstract

This work proposes the usage of in-hand depth cameras in combination with GPU-based collision detection algorithms to realize robotics grasp planning for unknown objects on the fly. Based on pointcloud data captured during an exploratory hand motion we evaluate and optimize grasps with a hybrid Particle Swarm Optimization process. The approach maximizes the contact surface while examining various grasp poses and allows a precise and model-free manipulation of arbitrary objects. Targeted end-effectors are anthropomatic multi-fingered hands with complex kinematics and geometries.