An improved genetic algorithm for mobile robot path planning in grid environment

Qianqian Hu; Kairong Li

doi:10.1117/12.2576111

12 October 2020 An improved genetic algorithm for mobile robot path planning in grid environment

Qianqian Hu, Kairong Li

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740K (2020) https://doi.org/10.1117/12.2576111
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

This paper proposes an improved genetic algorithm to study the path planning of mobile robot in grid environment. First, rasterize the motion plane of the robot, use serial number coding method and design a heuristic median insertion method to establish the initial population, ensure that the planned initial paths are all feasible paths, thereby speeding up the convergence of the algorithm. Then assign different weights to the path length, path security, and path energy consumption and combine them to generate a multi-objective fitness function. Finally, improve some genetic operations to maintain the population diversity of the algorithm in the later period, and avoid the algorithm from falling into avoid premature. Simulation experiments show that the proposed algorithm can quickly plan a feasible path in the grid environment. The path is not only shorter in length, but also more stable. At the same time, the running speed of the algorithm is 45.7% higher than other improved algorithms.

Citation Download Citation

Qianqian Hu and Kairong Li "An improved genetic algorithm for mobile robot path planning in grid environment", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740K (12 October 2020); https://doi.org/10.1117/12.2576111

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available