Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynam...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 13; no. 20; p. 4071
Main Authors: Li, Xiangcheng, Ruan, Zhaokai, Ou, Yang, Ban, Dongri, Sun, Youming, Qin, Tuanfa, Cai, Yiyi
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2024
Subjects:
Adaptive algorithms
Algorithms
Ant colony optimization
Asymmetry
Changing environments
Convergence
Diffusion rate
Distance learning
Efficiency
Heuristic
Localization
Optimization algorithms
Optimization techniques
Path planning
Pheromones
Planning
Robotics
Robots
Strategy
Unmanned aerial vehicles
New York
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13204071