Analysing robot swarm behaviour via probabilistic model checking

Abstract

An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.

Swarm robotics: a review from the swarm engineering perspective

“…The liveness property is verified when the swarm dynamics actually do evolve over time. A similar approach was used by Konur et al (2012) and Brambilla et al (2012). These authors analyzed swarm robotic systems by using probabilistic model checking, a technique that allows one to formally verify whether a given property is satisfied by a given model.…”

Analysing Robot Swarm Decision-Making with Bio-PEPA

“…Up to now this approach has not been explored extensively in swarm robotics. Examples of model checking in swarm robotics can be found in [13,3].…”

“…Another approach, namely microscopic modelling [8], focuses instead on the behaviour of individual robots. Finally, model checking has been used to verify formal properties of a SRS [13]. These approaches allow a developer to obtain different "views" of the system behaviour.…”

Qualitative and Quantitative Analysis of Systems and Synthetic Biology Constructs using P Systems

“…concurrent 13 and distributed systems, 14 multi-agent systems, 15 pervasive systems 16 and swarm robotics. 17 Recently, it has been also applied to analysis of various biological systems, e.g. ERK/MAPK pathway, 4 FGF signalling pathway, 18 cell cycle in eukaryotes, 19 EGFR pathway, 20 T-cell receptor signaling pathway, 21 cell cycle control, 22 diabetes-cancer signaling network, 23 and genetic Boolean gates.…”