Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms

TL;DR

This paper introduces a symmetry reduction technique for model checking swarm navigation algorithms, significantly reducing verification time and enabling analysis of larger, more complex emergent behaviors in robotic swarms.

Contribution

The authors propose a novel relative encoding method based on symmetry properties, improving model checking scalability for swarm algorithms like Alpha.

Findings

Enabled verification of larger grid sizes (up to 16x16) with more robots.

Reduced verification time from 10 hours to 7 minutes for certain scenarios.

Demonstrated transferability of the approach to other swarm algorithms.

Abstract

The emergent global behaviours of robotic swarms are important to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A common problem in model checking is the state-space explosion that arises when the states of the model are numerous. We propose a novel implementation of symmetry reduction, in the form of encoding navigation algorithms relatively with respect to a reference, based on the symmetrical properties of swarms in grids. We applied the relative encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model checker. A comparison of the state-space and verification results with an absolute (or global) and a relative encoding of the Alpha algorithm highlights the advantages of our approach, allowing model checking larger grid sizes and number of robots, and consequently, verifying more complex emergent behaviours. For example, a property was verified for a grid with 3 robots and a maximum allowed size of 8x8 cells in a global encoding, whereas this size was increased to 16x16 using a relative encoding. Also, the time to verify a property for a swarm of 3 robots in a 6x6 grid was reduced from almost 10 hours to only 7 minutes. Our approach is transferable to other swarm navigation algorithms.