A path planning generator based on the Chaos Game Optimization algorithm

This research paper explores a novel path planning generator that leverages the Chaos Game Optimization (CGO) algorithm, a mathematical technique inspired by the chaos game that creates fractals. The CGO algorithm is applied to analyze fractal configurations and self-similarity problems in path planning. The paper provides detailed information about the initialization of candidate solutions and the iterative process of updating their positions and fitness values. Through MATLAB simulations, the paper demonstrates the CGO algorithm's effectiveness in generating optimal paths in complex scenarios with randomly generated blocks or labyrinth environments. The approach shows great potential in enhancing the capabilities of autonomous robots in navigating dynamic and challenging environments. This paper also simulated the path planning generator using the CGO algorithm in MATLAB. By implementing chaos theory and randomness, the CGO algorithm provides a robust and efficient solution for path planning, enabling robotic systems to handle complex and nonlinear problems. The paper concludes that the application of chaos theory in robotics opens up exciting possibilities for advancing the capabilities of robotic systems and enhancing their performance in real-world scenarios.