An Evolutionary Game-Theoretic Approach to Triple-Strategy Coordination in RRT*-Based Path Planning

To address the limitations of the RRT-series algorithm and its variants, which have demonstrated a lack of dynamic adaptability in various environments, poor path quality, slow convergence rates, and a tendency to become trapped in local optima, this paper aims to propose a dynamic multi-strategy path-planning algorithm based on EG-DRRT* (Evolutionary Game-Theoretic Dynamic RRT*). The integration of evolutionary game theory allows the algorithm to use the concept of dynamically updating replicators to develop the payoff function for a fusion of multi-strategies. This enables a dynamic adjustment of the usage ratios of RRT*, Dijkstra, and Goal Bias algorithms. This approach directs the search process toward converging on the goal point, ultimately achieving an ESS (Evolutionarily Stable Strategy) equilibrium. The experimental results reveal that EG-DRRT* successfully establishes a dynamic balance between global exploration and local optimization across various environments, demonstrating remarkable adaptability and robustness. Additionally, EG-DRRT* shows substantial advantages compared to existing algorithms.