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Lookup NU author(s): Jianqiao Long, Dr Jichun LiORCiD
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© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Path planning is a core challenge in autonomous navigation and continuously attracts significant attention in mobile robotics. While optimization algorithms are widely employed for solving robot path planning problems, the Aquila Optimizer (AO) suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose a robot path planning method based on a Multi-strategy Enhanced Aquila Optimizer (MEAO). In MEAO, the initial population is enhanced using opposition-based learning, and an adaptive parameter mechanism balances exploration and exploitation. During the narrowed exploration phase, a phasor operator enables non-parametric optimization to improve global search capability, while a differential evolution mutation strategy is embedded to strengthen local exploitation. The algorithm’s performance is validated on the CEC2022 benchmark functions with ablation studies confirming the effectiveness and synergy of the various strategies. MEAO is further applied to robot path planning, with simulations performed on various complex two-dimensional grid maps, and comparisons made against several intelligent optimization-based algorithms. In addition, to address the limitations of the traditional Dynamic Window Approach (DWA) in terms of dynamic obstacle avoidance robustness and susceptibility to local minima, we introduce a dynamic threat response mechanism and an adaptive heading trap detection strategy. A collaborative framework combining MEAO-based global planning with the improved DWA for local obstacle avoidance is then established. Experimental results demonstrate that MEAO achieves shorter path lengths and faster convergence, while the improved DWA significantly enhances obstacle avoidance robustness in complex environments. The proposed collaborative framework thus ensures globally optimal paths and reliable real-time local obstacle avoidance, demonstrating the practicality and efficiency of the MEAO algorithm and improved DWA for mobile robot navigation.
Author(s): Zhou Y, Liu X, Long J, Lu Y, Cheng J, Li J
Publication type: Article
Publication status: Published
Journal: Expert Systems with Applications
Year: 2026
Volume: 312
Print publication date: 25/05/2026
Online publication date: 03/02/2026
Acceptance date: 01/02/2026
ISSN (print): 0957-4174
ISSN (electronic): 1873-6793
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.eswa.2026.131489
DOI: 10.1016/j.eswa.2026.131489
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