Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Strategic directions in real-time and embedded systems
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
KLAIM: A Kernel Language for Agents Interaction and Mobility
IEEE Transactions on Software Engineering
KLAVA: a Java package for distributed and mobile applications
Software—Practice & Experience
Model checking mobile stochastic logic
Theoretical Computer Science
Formal verification of probabilistic swarm behaviours
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Towards temporal verification of emergent behaviours in swarm robotic systems
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
A review of probabilistic macroscopic models for swarm robotic systems
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Simulation and analysis of distributed systems in klaim
COORDINATION'10 Proceedings of the 12th international conference on Coordination Models and Languages
Property-driven design for swarm robotics
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Modelling and analyzing adaptive self-assembly strategies with maude
WRLA'12 Proceedings of the 9th international conference on Rewriting Logic and Its Applications
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We present a novel formal verification approach for collective robotic systems that is based on the use of the formal language Klaim and related analysis tools. While existing approaches focus on either micro- or macroscopic views of a system, we model aspects of both the robot hardware and behaviour, as well as relevant aspects of the environment. We illustrate our approach through a robotics scenario, in which three robots cooperate in a decentralized fashion to transport an object to a goal area. We first model the scenario in Klaim. Subsequently, we introduce random aspects to the model by stochastically specifying actions execution time. Unlike other approaches, the specification thus obtained enables quantitative analysis of crucial properties of the system. We validate our approach by comparing the results with those obtained through physics-based simulations.