Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm-Reference-Cited by-同舟云学术

Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm

Published:2021-02-20 Issue:1 Volume:33 Page:11-23
ISSN:1883-8049
Container-title:Journal of Robotics and Mechatronics
language:en
Short-container-title:J. Robot. Mechatron.

Author:

Schäfle Tobias Rainer,Mitschke Marcel,Uchiyama Naoki, ,

Abstract

This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. The HGA algorithm is validated using the following three different fitness functions: the number of cell visits, traveling time, and a new energy fitness function based on experimentally acquired energy values of fundamental motions. Computational results show that compared to conventional methods, HGA improves paths up to 38.4%; moreover, HGAs have a consistent fitness for different starting positions in an environment. Furthermore, experimental results prove the validity of the fitness function.

Publisher

Fuji Technology Press Ltd.

Subject

Electrical and Electronic Engineering,General Computer Science

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