Sampling-based coverage motion planning for industrial inspection application with redundant robotic system
Wei Jing, Joseph Polden, Chun Fan Goh, Mabaran Rajaraman, Wei Lin, Kenji Shimada
Abstract
This paper presents a novel sampling-based motion planning method for shape inspection applications with a redundant robotic system. In this paper, a 7-Degree-of-Freedom (DOF) redundant robotic system consisting of a 6-DOF manipulator and a 1-DOF turntable is used for the industrial inspection problem. A Set Covering Problem (SCP) is formulated to select suitable viewpoints that satisfy the inspection requirements, and a Generalized Travelling Salesman Problem (GTSP) is formulated to determine both the robot poses and the visiting sequences. While previous studies solve the two problems separately, we formulate the SCP and GTSP problems as a combined sequencing SC-GTSP problem. A Random-Key Genetic Algorithm (RKGA) is then used to solve the combined SC-GTSP problem in a one-step optimization process. To validate the effectiveness of our method, we applied the proposed method to several motion planning cases. The results show that the proposed method outperforms the previous approaches by requiring up to 28.1% less total inspection time.
BibTeX
@inproceedings{iros2017_samplingbasedcov,
title = {Sampling-based coverage motion planning for industrial inspection application with redundant robotic system},
author = {Wei Jing and Joseph Polden and Chun Fan Goh and Mabaran Rajaraman and Wei Lin and Kenji Shimada},
booktitle = {IROS 2017},
year = {2017}
}