Detection and Mitigation of Misleading Pheromone Trails in Foraging Robot Swarms

Abstract

This study addresses the overlooked aspect of security in swarm robotics by exploring the vulnerabilities of stigmergic communication in foraging robot swarms. More specifically, we study the swarm's susceptibility to attacks that introduce misleading pheromone trails. Simulated scenarios in which detractor robots lay misleading trails to deceive benign foraging robots effectively reduce the foraging performance of the swarm. We analyze the impact of the attack on the swarm and evaluate the reduction of foraging efficiency. We introduce a defense mechanism using distance-based clustering (DBSCAN) along with a cluster grouping method to isolate large batches of detractors early in the simulation. The isolation strategy also employs an adaptive timing mechanism to estimate pheromone trail travel times and identify the misleading trails laid by detractors. Our attack experiments show a decline in resource collection and an increase in forager captures across an increasing percentage of detractors. However, the defense strategy can effectively identify separate groups of foragers and detractors and isolate all detractors early on in the simulation. This significantly reduces forager capture rates and preserves the foraging performance of the swarm. This research highlights the security vulnerabilities in pheromone-based foraging algorithms and proposes a robust defense mechanism, contributing significantly to the development of more resilient foraging algorithms in swarm robotics. These findings are pivotal for deploying secure and efficient swarm robotics systems in real-world scenarios where both efficiency and security are paramount.