Robot assembly planning using a task grammar augmented with heuristic knowledge

Abstract

Automated assembly planning in a manufacturing environment requires not only mathematically sound formal methods and algorithmic computations but also heuristic knowledge. Much of this knowledge can be extracted from manual task manipulation strategies, such as the motion classification scheme used in methods-time measurement (MTM) studies. In this paper, we delineate various task-level operations in the context of robotic assembly, and show how these operations can be organized in the form of a task grammar. The proposed task grammar captures the intrinsic principle on how the sequence of robot operations should be ordered and how one high-level operation can be effectively decomposed into low-level operations. In order to control the process of robot task decomposition, we explicitly represent and apply qualitative heuristic knowledge about task constraints and operation applicability. In the paper, we first describe how syntactical knowledge about robot operations can be formulated for assemblyrelated manipulation tasks. Kext, through illustrative examples, we attempt to show how qualitative knowledge can be effectively used in the task decomposition in three distinct ways: heuristic-based operation pattern matching, spatial-feature-based qualitative state envisionment, and canonical transformation of task environments.