Tuning responsivity-persistence trade-off in swarm robotics: A motion salience threshold approach

In swarm robotics, balancing two crucial properties is essential: responsivity, which enables quick reactions to environmental changes, and persistence, which maintains stable goal-directed behavior despite distractions. Responsivity is necessary for tasks like evading obstacles or responding to threats. In contrast, persistence is key to ensuring coordinated movement and focus on long-term goals, such as migration or search missions. To address the challenge of balancing these conflicting properties, we introduce the Motion Salience Threshold (MST). This approach enables swarm robots to selectively respond to significant motion cues, thereby enhancing overall system performance by minimizing unnecessary reactions to less critical changes. This tuning mechanism is particularly useful in real-world applications where the environment is unpredictable and demands both flexibility and stability from the robotic swarm. Our research demonstrates that lower threshold values increase responsivity, enabling the swarm to react quickly in highly dynamic environments, whereas higher values bolster persistence, reducing the impact of false positive signals and maintaining focus on long-term goals. The proposed approach offers a targeted method for fine-tuning swarm behavior, validated through extensive simulations and real-world experiments with robotic systems. These findings provide valuable insights for designing adaptive robotic swarms that can navigate complex and unpredictable environments more effectively.