Adaptive Repulsive Pheromone Clustering for Foraging Robot Swarms
2026-08-17 • Robotics
Robotics
AI summaryⓘ
The authors improved a robot swarm search method called CPFA, which sometimes wastes time by going to places they've already checked. They created a new technique named ARPC, where robots leave 'repulsive' pheromone markers to show where they've been, helping others avoid those spots and explore new areas instead. This approach makes the swarm better at finding and collecting resources, especially when resources are hard to find. Their tests showed ARPC were more efficient than older methods, especially in larger or more complex search areas.
Central Place Foraging Algorithmpheromone navigationrobot swarmrepulsive pheromoneresource collectiondecentralized searchswarm roboticssearch efficiency
Authors
Carlos Pena-Caballero, Constantine Tarawneh, Qi Lu
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.