Robots Path Planning in RoboCup Competitions: A Literature Review

This paper explores and evaluates various path planning approaches in RoboCup competitions, aiming to advance AI research and address real-world challenges. The criteria for evaluation include computational efficiency, path optimality, adaptability to dynamic environments, and real-time performance. The study highlights the importance of path planning for autonomous navigation in dynamic environments, emphasizing the need for efficient and optimal travel routes. It reviews methodologies across different RoboCup leagues, noting a research focus on the Small Size League (SSL) and a gap in studies for other leagues like Rescue, Logistics, and Humanoid. Key findings include the effectiveness of heuristic approaches like A* algorithms, the potential of metaheuristics such as genetic algorithms, and the promise of machine learning (ML) methods. The paper suggests future research directions, including the development of hybrid approaches, enhanced ML techniques, and real-time responsive algorithms. The integration of multiple strategies and exploration of under-researched leagues could significantly advance robotic path planning.