A Robot Path Planning Method Based on Improved Genetic Algorithm and Improved Dynamic Window Approach

Author:

Li Yue1ORCID,Zhao Jianyou1,Chen Zenghua23,Xiong Gang2ORCID,Liu Sheng2

Affiliation:

1. School of Automobile, Chang’an University, Xi’an 710061, China

2. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 310013, China

3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 310013, China

Abstract

Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

Funder

National Key Research and Development Program of China

CAS STS Dongguan Joint Project

National Natural Science Foundation of China

CAS Key Technology Talent Program

Guangdong Basic and Applied Basic Research Foundation

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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