Utilizing Graph Theory for Strategy Optimization in Self-Reconfiguring Robots

摘要

Graph theory plays a pivotal role in the self-reconfiguration strategies of robots, optimizing the strategies of Modular Self-Reconfigurable Robots (MSRR) in unknown environments. By incorporating concepts from graph theory, particularly adjacency matrices and Voronoi diagrams, a novel graph-based strategic approach can be devised to effectively guide robots through self-reconfiguration and enhance their adaptability to the environment. Initially, adjacency matrices are utilized to describe the interconnections between robot modules, capturing the robot system's topological structure with precision. Subsequently, the application of Voronoi diagrams further bolsters the understanding of robots’ relative spatial positioning and adjacency, providing vital spatial information for autonomous exploration and decision-making. The synergy of these graph theory tools not only amplifies the analysis of environmental adaptability but also furnishes new theoretical support and practical pathways for strategy selection during the robots’ self-reconfiguration process. In practice, the potential energy values of graphs can be used to illustrate and optimize the robots’ behavioral strategies, revealing the practical value of graph theory in robotic strategy optimization and enabling more accurate adaptation to complex and dynamic environmental conditions. This graph theory-based strategy optimization method not only increases the computational efficiency and decision quality of the robot system but also offers solutions to new challenges encountered in robot technology.