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Volume 2 , Issue 2 , PP: 37-47, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Shahid Mahmood 1 *
Doi: https://doi.org/10.54216/MOR.020204
Automated motion planning is an essential component of any autonomous system that effectively and safely finds the route in different application areas such as industry, hospitals, and cars. New developments in artificial intelligence and machine learning have improved additional attributes of path-planning algorithms in dealing with the complexities of their environment. This review also covers traditional algorithms, including RRT and A*, integrated frameworks, and AI solutions encompassing reinforcement learning, deep neural networks, and the Large Language Model (LLM). This paper looks at these methods' essence, advantages and disadvantages, and use for flexibility, productivity, and feasibility. It also outlines practical problems such as real-world testing, multi-robot operation, and energy issues and finally describes research directions in both cross-disciplinary research and practical application. This review aims to present the current developments and possibilities for robotic path planning to the researcher and practitioner communities.
Path Planning , Autonomous Robots , Artificial Intelligence , Machine Learning , Reinforcement Learning , Dynamic Environments
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