JRM Vol.33 No.1 pp. 11-23
doi: 10.20965/jrm.2021.p0011


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

Tobias Rainer Schäfle*, Marcel Mitschke**, and Naoki Uchiyama*

*Department of Mechanical Engineering, Toyohashi University of Technology
1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan

**Andreas Stihl AG & Co. KG
Badstraße 115, Waiblingen 71336, Germany

September 30, 2019
January 28, 2020
February 20, 2021
coverage path planning, hybrid genetic algorithm, path optimization, mobile robot

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.

Generation of optimal coverage paths for mobile robots

Generation of optimal coverage paths for mobile robots

Cite this article as:
T. Schäfle, M. Mitschke, and N. Uchiyama, “Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm,” J. Robot. Mechatron., Vol.33 No.1, pp. 11-23, 2021.
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