A critical review of analytical approaches to path planning for autonomous mobile robots

In this research, strategies for mobile robot navigation are studied and discussed critically, especially on the development of path planning methods under various environments and constraints. It discusses classical and intelligent approaches in detail to draw out their merits, demerits and scope for future research. Classical approaches investigated are Dijkstra, A-star, D-star, D-star lite, roadmap approaches and cell decomposition. Intelligent approaches studied include the particle swarm optimization algorithm, the firefly algorithm, the shuffled frog leaping algorithm, the grasshopper optimization algorithm, the bacterium foraging optimization algorithm, the bee colony optimization algorithm, the ant colony optimization algorithm, the grey wolf optimization algorithm, the red wolf optimization algorithm, and neural networks, genetic algorithms, fuzzy logic, and potential fields. Besides, the current review also includes an in-depth examination of the contemporary methods of machine-learning-based approaches, which includes the Reinforcement Learning (RL), the Deep RL, Graph Neural Networks (GNNs), and the Transformer-based methods, which occupy a dominant role in the current academic research. The review addresses navigation in both single-robot and multi-robot systems, across static and dynamic environments. The findings indicate that intelligent strategies offer greater flexibility and effectiveness in diverse terrains compared to classical methods. Moreover, intelligent approaches are often employed to enhance classical techniques through the development of hybrid algorithms. Overall, mobile robot path planning increasingly relies on intelligent approaches. The exploration is climaxed with the presentation of an intense comparative framework that includes new tables and figures that define the execution, computational complexity and flexibility of different navigation strategies. The visualizations support the relevance of the strategies in particular robotic cases, and explain the significant research paths.