A learning control system for an articulated gripper

Abstract

This paper presents an advanced control system we developed for an articulated gripper. This articulated gripper was previously designed to achieve stable grasp of objects with various shapes and to impart compliant fine motions to the grasped object. In the control system of this device we introduced autonomous reasoning capabilities. Fine motion strategies, needed for mating or grasping, use inductive learning from experiments to achieve uncertainty and error recovery (on sensing, control and model). An overview of the articulated gripper's capabilities is provided for a better understanding of the programming environment we propose. For solving the problem of synthesis programs for fine motion planning we introduce declarative programming facilities in the controller through a time-sensitive expert system. The paper gives some details on the implementation of this expert system. Then we develop an heuristic procedure to obtain an implicit local model of contacts in complex assembly tasks. Finally, a specific example of this approach — a peg-in-hole operation — is outlined.