• Durga B

This review paper examines advanced learning-based strategies for path planning in autonomous robots, focusing on their application in unknown and dynamic terrains. The study delves into five distinct approaches: Q-learning, Bayesian learning, Deep reinforcement learning (DRL), Fuzzy Q-learning, and neural networks combined with hierarchical reinforcement learning (HRL). Each approach is analyzed for its ability to navigate complex environments, handle uncertainties, and optimize path planning efficiency. Q-learning is explored for its iterative learning capabilities in unpredictable terrains, while Bayesian learning is assessed for its probabilistic decision-making in scenarios with limited information. The paper also reviews studies from DRL applied to grid path planning, highlighting its potential for handling large state spaces. Fuzzy Q-learning is discussed for its effectiveness in managing imprecision, and the integration of neural networks with HRL is examined for its ability to decompose complex tasks into manageable sub-goals. The comparative analysis provides insights into the strengths and limitations of each technique, with a focus on their adaptability, computational demands, and real-world applicability. The paper concludes with a discussion of future directions, emphasizing the potential for hybrid approaches and the need for further research to enhance scalability and robustness in robotic path planning.

Q Learning, Deep Reinforcement Learning, Hierarchical Reinforcement Learning, Bayesian Learning, Path Planning, Fuzzy Learning

"Comparative Analysis of Robotic Path Planning Techniques Using Reinforcement Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.f207-f212, August-2024, Available :http://www.jetir.org/papers/JETIR2408629.pdf

"Comparative Analysis of Robotic Path Planning Techniques Using Reinforcement Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppf207-f212, August-2024, Available at : http://www.jetir.org/papers/JETIR2408629.pdf