Grasp Planning with Incomplete Knowledge About the Object to be Grasped

Abstract

Robotic grasping in domestic environments is nowadays a topic of intensive research, since the big number of variables and constraints in a household scenario makes it a difficult task to accomplish. This paper addresses the problem of grasp planning in these situations. The first problem to deal with is the selection of the grasp pose for the end-effector - the position and orientation in which it should grasp the desired object. To tackle this issue, this work applies a method to detect grasp poses in point clouds with objects that may be unknown to the robot, followed by an approach to select the grasp candidate in terms of its appropriateness for a given scene. The second part of the grasping task is the motion planning that places the end-effector in the grasp pose. This involves getting an Inverse Kinematics (IK) solution for the goal arm configuration and a trajectory for the arm avoiding any obstacle in the scene. This was addressed by using MoveIt! framework together with an additional tolerance method to compute feasible movements. A comparative study regarding the used motion planners and IK solvers was conducted. We show experimental results of the pipeline, featuring the components mentioned above, which can plan and execute grasps with a success rate that may go up to 90%, depending on the scenario.