Monte Carlo Tree Search Algorithms for Strategic Planning in Humanoid Robotics

With the increasing complexity and versatility of humanoid robots, there is a growing imperative for efficient and adaptable planning methodologies. This evolution marks a crucial step towards enhancing the overall capabilities and practicality of humanoid robotic systems. This research delves into the application of Monte Carlo Tree Search (MCTS) algorithms for enhancing strategic planning in the context of humanoid robotics. The study explores the integration of MCTS into humanoid robotics to enable effective decision-making processes that account for uncertainties, sensor noise, and real-time adaptability. By simulating future scenarios and evaluating potential actions through iterative sampling, MCTS facilitates the creation of robust and context-aware strategies. The research considers various aspects of strategic planning, including path finding, task allocation, and goal achievement. This investigation contributes to the burgeoning field of robotics by providing a comprehensive analysis of the benefits and challenges associated with implementing MCTS in humanoid robotic systems. The proposed MCTSA model shows better efficiency in Resource Allocation 88%, SuccessRate (Navigation) 85%, Average Planning Time 2.5 seconds and Success Rate (Object Manipulation) 92% which is higher when compared to the existing methods. The findings aim to inform the development of intelligent and autonomous robots capable of making strategic decisions in dynamic and uncertain environments, thereby advancing the capabilities of humanoid robotics in various practical applications.