Optimisation Techniques for Multi‐Robot Path Planning: A Review of Collision Avoidance and Performance Metrics in Connectivity, Efficiency and Safety

Path planning is critical for multi-robot systems (MRS), directly affecting task efficiency, execution time and operational cost. Despite extensive research and the successful application of numerous algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with in-creasing numbers of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on roadmap planning methods that are widely used for multi-robot path planning (MRPP). It focuses on three graph-based algorithms: Multi-Robot Path Planning algorithm, Central Algorithm (CA), and the Optimisation Central Algorithm (OCA). These algorithms utilise visibility graphs (VG) for environment representation and the Dijkstra’s algorithm for shortest-path computation, while incorporating algebraic connectivity to improve coordination, safety and scalability. In addition, the technological context and implementation platforms, including simulation environments, cloud robotics, and AI-based frameworks, are conceptually examined. The potential applications of these methods in assistive robotics are highlighted, particularly in supporting safe and reliable navigation in healthcare and human-centered environments. The paper synthesises theoretical and practical insights, identifies current limitations and challenges, and outlines future re-search directions for efficient, scalable and robust MRPP.