Efficient task and path planning for maintenance automation using a robot system

TL;DR

This paper presents an integrated approach for autonomous maintenance task and path planning in robot systems, combining offline CAD data with online vision, and employing novel sampling-based and adaptive algorithms to improve efficiency and handle uncertainties.

Contribution

It introduces a new probabilistic filtering method for data fusion, a symbolic disassembly space computation technique, and an adaptive path planning algorithm for maintenance robots.

Findings

Validated the approach through experiments

Reduced planning time with adaptive exploration

Improved handling of environmental uncertainties

Abstract

The research and development of intelligent automation solutions is a ground-breaking point for the factory of the future. A promising and challenging mission is the use of autonomous robot systems to automate tasks in the field of maintenance. For this purpose, the robot system must be able to plan autonomously the different manipulation tasks and the corresponding paths. Basic requirements are the development of algorithms with a low computational complexity and the possibility to deal with environmental uncertainties. In this work, an approach is presented, which is especially suited to solve the problem of maintenance automation. For this purpose, offline data from CAD is combined with online data from an RGBD vision system via a probabilistic filter, to compensate uncertainties from offline data. For planning the different tasks, a method is explained, which use a symbolic description, founded on a novel sampling-based method to compute the disassembly space. For path planning we use global state-of-the art algorithms with a method that allows the adaption of the exploration stepsize in order to reduce the planning time. Every method is experimentally validated and discussed.