Accession Number : ADA187746

Title :   Error Detection and Recovery for Robot Motion Planning with Uncertainty.

Descriptive Note : Doctoral thesis,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Donald, Bruce R

PDF Url : ADA187746

Report Date : Jul 1987

Pagination or Media Count : 314

Abstract : Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, controls errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. This document presents a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, complaint motions, and simple pushing motions. It is not always possible to find plans that are guaranteed to succeed. For example, if tolerancing errors render an assembly infeasible, the plan executor should stop and signal failure. In such cases the insistence on guaranteed success is to restrictive. For this reason Error Detection and Recovery (EDR) strategies, there is no possibility that the plan will fail without the executor realizing it. The EDR framework fills a gap when guaranteed plans cannot be found or do not exist: it provides a technology for constructing plans that might work, but fail in a reasonable way when they cannot. Keywords: Robotics; Mobility.

Descriptors :   *MOTION, *ROBOTS, *ERROR DETECTION CODES, CONTROL, DETECTION, ERRORS, FAILURE, GEOMETRY, MODELS, PLANNING, ROBOTICS, SIGNALS, STRATEGY, MOBILITY

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE