🤖 AI Summary
This study addresses the inefficiency caused by redundant exploration in robotic swarm foraging by proposing an Adaptive Repellent Pheromone Clustering method. This approach innovatively integrates bio-inspired pheromone deposition with nest-centric clustering estimation to mark explored areas and steer agents away from low-value zones, thereby achieving a dynamic balance between resource exploitation and exploratory redundancy. Extensive validation across multiple scenarios using ARGoS simulations demonstrates that the proposed method significantly outperforms traditional strategies. Specifically, it improves early resource discovery rates by 10% and increases late-stage collection efficiency by up to 60%. These results confirm that the method effectively enhances search diversity and overall system performance in swarm foraging tasks.
📝 Abstract
The Central Place Foraging Algorithm (CPFA) combines site fidelity, pheromone-guided navigation, and uninformed random search to enable decentralized resource collection in robot swarms. However, CPFA often revisits previously explored regions while leaving other areas insufficiently searched, reducing efficiency as resources become scarce. In this paper, we propose Adaptive Repulsive Pheromone Clustering (ARPC), a bio-inspired method in which robots deposit repulsive pheromone waypoints to mark previously explored locations. These waypoints are clustered around the nest to estimate low-value search regions, allowing robots to be redirected toward likely unvisited areas. By integrating the exploitation of known resources with systematic avoidance of redundant exploration, ARPC improves search diversity and resource discovery efficiency. Extensive simulations in ARGoS across varying arena sizes, resource densities, and clustered, random, and power-law spatial distributions demonstrate that ARPC consistently outperforms CPFA and the Grid-Based CPFA (GPFA). In particular, ARPC yields significant gains during both early discovery (10\%) and late-stage (up to 60\%) collection, where conventional methods typically degrade. These results indicate that ARPC provides a scalable and robust strategy for large-scale heterogeneous swarm foraging environments.