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Autonomous mobile robots (AMRs) are increasingly used in various applications, including transportation, logistics, and healthcare. One of the central challenges in AMR deployment is path planning, which involves finding an optimal and collision-free route from the robot’s initial position to its destination. This review, which builds upon a two-part study, presents a comprehensive overview of state-of-the-art techniques for AMR path planning. This paper focuses on classical and heuristic-based strategies, providing valuable insights into their foundational roles in autonomous mobile robot navigation. We categorize existing approaches based on their core principles, including graph-based, heuristic-based, metaheuristic-based, and learning/reasoning-based methods. For each category, we analyze the strengths and limitations of the representative techniques and evaluate their effectiveness in various operational scenarios. Furthermore, we identify emerging research directions and ongoing challenges in AMR path planning, such as multi-robot coordination, adaptation to dynamic environments, and integration with human–robot interaction frameworks.
Aremu et al. (Wed,) studied this question.