Institute for
Robotics and Process Control

Deutsch   English

Sensor-Based Grasp-Planning

Project Description

Grasping has evolved from a somewhat marginal topic to an important field in robotics research. This increasing interest in grasping is partly due to the increasing importance of flexible assembly in industrial automation. This project describes the model based grasp planning system Autograsp for automatically grasping objects in a robot's workspace. In contrast to existing grasp planning systems various constraints are taken into account required for a successful execution of a grasp operation. The computations performed by Autograsp are split into offline and online computations, with as much a-priori knowledge as possible used in the offline phase.
During the offline phase a geometric grasp planning is performed using the concept of symbolic grasps. Symbolic grasps are generated by filter operations performing a kind of shape matching between the geometry of the gripper and the objects to be grasped. To reduce computational costs, representative gripper orientations are determined for each symbolic grasp. The new concept of representative gripper orientations guarantees that the gripper's palm can achieve form closure with the objects to be grasped. Thus, higher stability is achieved to resist dynamic disturbance forces arising during the motion of the robot. For each representative gripper orientation, collision free approach trajectories and grasp frames are calculated in a local xy-configuration space respective to the objects. The resulting sets of grasp frames define grasp classes that are evaluated taking into account several evaluation criteria. For the generation of regrasp sequences, placement classes of objects are generated and evaluated. Placement classes describe stable object placements on a horizontal plane. For the evaluation of placement classes a new placement function is proposed yielding obvious evaluation results. During the offline phase grasp and placement classes are used for the generation and evaluation of compatible regrasp operations.
During the online phase all necessary external grasp constraints are taken into account. External grasp constraints result while a grasp operation is executed in a robot's workcell. An important external grasp constraint concerns the stability of the scene to be manipulated. We use an efficient algorithm which analyses the effects of a grasp operation on scene stability:

greifen1_en.gif


If a specified grasp operation cannot be achieved with a single grasp directly, a regrasp sequence is determined automatically. The regrasp problem is solved by an evaluated breadth-first search in the regrasp graph. In contrast to existing regrasp planning systems, the initial and goal nodes of the search are determined during the online phase only. A regrasp sequence is determined taking into account the length of the regrasp sequence and the quality of the applied grasp and placement classes:

greifen2b.gif



It took 0.31s to generate this page.