Title: Improved NSGA-II for the minimum constraint removal problem

Authors: Bo Xu; Feng Zhou; Yonghui Xu; Haoran Xu; Kewen Xu

Addresses: School of Information Science, Guangdong University of Finance and Economics, Guangzhou, China ' School of Information Science, Guangdong University of Finance and Economics, Guangzhou, China ' School of Software Engineering, South China University of Technology, Guangzhou, China ' School of Information Science, Guangdong University of Finance and Economics, Guangzhou, China ' School of Information Science, Guangdong University of Finance and Economics, Guangzhou, China

Abstract: This paper proposes a comprehensive multi-objective evaluation model to derive a feasible solution to MCR path planning that is from the robot's individual perspective, is driven by the costs and benefits, and takes into account factors such as the minimum constraint set, the route length, and the cost. The feasible solution to the MCR path is evaluated using this model. A typical multi-objective algorithm NSGA-II is applied to the MCR problem. The algorithm test and real scenario test results show that compared with single objective algorithm planning, the NSGA-II-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform the MCR path planning more effectively.

Keywords: minimum constraint removal; MCR; minimum constraint set; path planning; multi-objective optimisation; robot.

DOI: 10.1504/IJES.2021.10033854

International Journal of Embedded Systems, 2021 Vol.14 No.1, pp.27 - 35

Received: 03 Jan 2020
Accepted: 15 Mar 2020

Published online: 22 Dec 2020 *

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