Software and Behavior Diversification for Swarm Robotics Systems

Abstract

Inspired by natural swarms, swarm robotics systems are used in safety-critical tasks due to their scalability, redundancy, and adaptability. However, their design exposes them to two primary vulnerabilities. First, their homogeneity makes them vulnerable to large-scale attacks. Second, logical flaws within swarm algorithms can be exploited, leading to mission failures or crashes. While existing studies can effectively identify these vulnerabilities using system testing and verification, they are often time-consuming and might require repetition following software updates. To this end, we propose a complementary, two-level diversification approach. The first level tackles system homogeneity through software diversification. The second level introduces algorithmic randomness to minimize the exploitability of logical flaws. By leveraging a social force model, we can ensure that the introduced randomized behaviors do not compromise safety. Our evaluations show that the performance overheads remain within acceptable limits, notably at 2% for missions characterized by self-organizing behaviors.