Mobile robot networks emerged in the past few years as a promising distributed computing model. Existing work in the literature typically ensures the correctness of mobile robot protocols via ad hoc handwritten proofs, which, in the case of asynchronous execution models, are both cumbersome and error-prone.In this paper, we propose the first formal model and general verification (by model-checking) methodology for mobile robot protocols operating in a discrete space (that is, the set of possible robot positions is finite). Our contribution is threefold. First, we formally model using synchronized automata a network of mobile robots operating under various synchrony (or asynchrony) assumptions. Then, we use this formal model as input model for the DiVinE model-checker and prove the equivalence of the two models. Third, we verify using DiVinE two known protocols for variants of the ring exploration in an asynchronous setting (exploration with stop and perpetual exclusive exploration).The exploration with stop we verify was manually proved correct only when the number of robots is k > 17, and n (the ring size) and k are co-prime. As the necessity of this bound was not proved in the original paper, our methodology demonstrates that for several instances of k and n not covered in the original paper, the algorithm remains correct. In the case of the perpetual exclusive exploration protocol, our methodology exhibits a counter-example in the completely asynchronous setting where safety is violated, which is used to correct the original protocol.

Abstract. Recent advances in Distributed Computing highlight models and algorithms for autonomous swarms of mobile robots that self-organize and cooperate to solve global objectives. The overwhelming majority of works so far considers handmade algorithms and correctness proofs. This paper is the first to propose a formal framework to automatically design distributed algorithms that are dedicated to autonomous mobile robots evolving in a discrete space. As a case study, we consider the problem of gathering all robots at a particular location, not known beforehand. Our contribution is threefold. First, we propose an encoding of the gathering problem as a reachability game. Then, we automatically generate an optimal distributed algorithm for three robots evolving on a fixed size uniform ring. Finally, we prove by induction that the generated algorithm is also correct for any ring size except when an impossibility result holds (that is, when the number of robots divides the ring size).

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The feature interaction problem appears in many different kinds of complex systems, especially systems whose elements are created or maintained by separate entities -for example, a modern automobile that incorporates electronic systems produced by different suppliers. Cross-cutting concerns, such as safety and security, require a comprehensive analysis of the possible interactions. However, there is a combinatorial explosion in the number of feature combinations to be considered. Our work approaches the feature interaction problem from a novel point of view: we seek to use the abstract subject matter knowledge of domain experts to deduce why some features will NOT interact, rather than trying to discover or resolve the interactions. In this paper, we present a method that can automatically reduce the required number of combinations and situations that have to be evaluated or resolved for feature interactions. Our tool, called Morse, rules out feature combinations that cannot have interactions based on traceable deductions from relatively simple abstract requirements that capture relevant subject matter knowledge. Our method is useful as a means of focusing attention on particular situations where more detailed functional requirements may be needed to avoid unacceptable risk arising from unintended interactions between features.

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<div class="section abstract"><div class="htmlview paragraph">Machine Learning (ML) based technologies are increasingly being used to fulfill safety-critical functions in autonomous and advanced driver assistance systems (ADAS). This change has been spurred by recent developments in ML and Artificial Intelligence techniques as well as rapid growth of computing power. However, demonstrating that ML-based systems achieve the necessary level of safety integrity remains a challenge. Current research and development work focused on establishing safe operation of ML-based systems presents individual techniques that might be used to gain confidence in these systems. As a result, there is minimal guidance for supporting a safety standard such as ISO 26262 - Road Vehicles - Functional Safety, to enable the development of ML-based systems. This paper presents a survey of recent ML literature to identify techniques and methods that can contribute to meeting ISO 26262 requirements. The surveyed literature is mapped onto the system development lifecycle V-model and the applicability of individual techniques and methods are discussed for each major phase of development.</div></div>