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With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-tomoving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.

Dependability is a property of a computer system to deliver services that can be justifiably trusted. Formal modelling and verification techniques are widely used for development of dependable computer-based systems to gain confidence in the correctness of system design. Such techniques include Event-B-a state-based formalism that enables development of systems correct-by-construction. While Event-B offers a scalable approach to ensuring functional correctness of a system, it leaves aside modelling of non-functional critical properties, e.g., reliability and responsiveness, that are essential for ensuring dependability of critical systems. Both reliability, i.e., the probability of the system to function correctly over a given period of time, and responsiveness, i.e., the probability of the system to complete execution of a requested service within a given time bound, are defined as quantitative stochastic measures. In this paper, we propose an extension of the Event-B semantics to enable stochastic reasoning about dependability-related non-functional properties of cyclic systems. We define the requirements that a cyclic system should satisfy and introduce the notions of reliability and responsiveness refinement. Such an extension integrates reasoning about functional correctness and stochastic modelling of non-functional characteristics into the formal system development. It allows the designer to ensure that a developed system does not only correctly implement its functional requirements but also satisfies given non-functional quantitative constraints.

Abstract. Event-B provides us with a powerful framework for correctby-construction system development. However, while developing dependable systems we should not only guarantee their functional correctness but also quantitatively assess their dependability attributes. In this paper we investigate how to conduct probabilistic assessment of reliability of control systems modeled in Event-B. We show how to transform an Event-B model into a Markov model amendable for probabilistic reliability analysis. Our approach enables integration of reasoning about correctness with quantitative analysis of reliability.