Evaluating Area Coverage Efficiency in Swarm Robotics: A Comparative Study of Different Approaches

Swarm robotics is an emerging field inspired by natural systems like insect colonies, focusing on decentralized, scalable, and robust multi-robot coordination. This paper presents a comparative study of four key area traversal algorithms used in swarm robotics: Random Walk, Flocking, Behavior-Based, and Spiral Search. These algorithms are evaluated on several quantitative parameters, including best position, coverage area, final velocity magnitude, trajectory efficiency, and convergence speed. Random Walk offers high coverage but suffers from inefficiency due to its unpredictable nature. Flocking provides cohesive group movement but is slower to converge. Behavior-Based algorithms balance avoidance and exploration but require fine-tuning for optimal performance. Spiral Search enables systematic exploration with moderate coverage and convergence. The results show that no single algorithm excels in all areas, emphasizing the need for task-specific algorithm selection. Future research should explore hybrid approaches, scalability, and real-world testing to enhance algorithm robustness and adaptability in dynamic environments.