Path Planning Improvement for Wheel-Legged Lunar Rovers Based on Fuzzy Control and Ant Colony Optimization

DOI

10.2991/978-94-6463-986-5_43How to use a DOI?

Keywords

Fuzzy Control; Ant Colony Optimization; PyCharm; Path Planning; Wheel-Legged Lunar Rover

Abstract

Path planning constitutes one of the fundamental technologies in robotic navigation. Focusing on the operational environment of wheel-legged lunar rovers on the actual lunar surface, this paper proposes and establishes a novel integrated planning methodology based on fuzzy control theory and ant colony optimization (ACO). First, based on terrain feature weights extracted from actual lunar surface scan data, we construct a three-dimensional lunar terrain model using PyCharm Matplotlib that accurately represents these characteristics. The performance parameters and constraints of the lunar rover are then systematically defined. Subsequently, we implement targeted improvements to the conventional ant colony optimization (ACO) algorithm based on actual operational conditions. A fuzzy logic controller is designed with an empirically initialized rule base, culminating in a novel hybrid control algorithm. Subsequently, the proposed algorithm was implemented in the 3D lunar terrain model. Simulation experiments were conducted under specified parameters and constraints, with comparative testing against both the A* algorithm and the genetic algorithm (GA). Results demonstrate that after 100 iterations, our method outperforms the benchmark algorithms in multiple performance metrics, including path optimality, response speed, and obstacle avoidance success rate, confirming its superior effectiveness and efficiency in lunar surface environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Feiyang Zhang
PY  - 2026
DA  - 2026/02/18
TI  - Path Planning Improvement for Wheel-Legged Lunar Rovers Based on Fuzzy Control and Ant Colony Optimization
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 422
EP  - 431
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_43
DO  - 10.2991/978-94-6463-986-5_43
ID  - Zhang2026
ER  -