A Review of Path Planning Techniques for Multi-Mobile Robots
Tongchuan Xia, Donglin Li, Dewen Chen, Zhengqi Bian, Shiyu Zhang · 2025
Multi-mobile robot path planning constitutes a critical area of research. It is fundamentally distinguished from single-robot planning by the inherent need for coordination, collision prevention among robots, and avoidance of static and dynamic environmental obstacles. This paper presents a comprehensive review of multi-mobile path planning approaches, a crucial area for enhancing autonomous robotic systems across diverse applications, including military, industry, security, and healthcare. The analysis is predominantly based on recent literature, with most referenced works published in the past five years, ensuring the survey reflects the latest advancements in the field. The survey summarizes the fundamental aspects of this field, categorizing approaches primarily by their coordination architectures and underlying path planning methodologies, while highlighting the critical challenges that persist, such as scalability and real-time performance. The paper examines the algorithmic landscape through classical methods, heuristic methods, optimization-based algorithms, and emerging learning-based approaches, with particular attention to their performance in static versus dynamic environments. Future research directions point toward sophisticated reinforcement learning algorithms and neural-evolutionary approaches. Additionally, hybrid methods integrating global planning with local collision avoidance show promise for enhanced adaptability in dynamic real-world conditions.