Pathfinding Performance: A Comparative Study of Modified Firefly and A* Algorithm for Mobile Robot Navigation

Path planning constitutes a foundational aspect of robotics, pivotal for enabling autonomous navigation and obstacle avoidance. The A* algorithm, recognized for its heuristic search approach, is widely favored due to its efficiency and optimality within certain parameters. Conversely, drawing inspiration from nature, the Firefly algorithm offers a metaheuristic optimization method known for its adeptness in global optimization and resilience against local optima. This study delves into the performance evaluation of both algorithms across diverse simulated environments. Through systematic experimentation and analysis, we scrutinize the strengths and limitations of each algorithm across various scenarios, including static environments. Our findings enhance the comprehension of algorithmic strategies for mobile robot path planning, shedding light on the selection of appropriate methodologies contingent on specific application needs and environmental circumstances. Ultimately, this comparative investigation endeavors to facilitate informed decision-making concerning algorithm selection for proficient and dependable path planning in mobile robotics applications. In comparison to the Firefly Algorithm (FA), the modified A* algorithm demonstrates a reduction of 13.08% and 12.73% in the optimal average path cost and average smooth path cost, respectively.