Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.
Robot swarms are collections of simple robots cooperating without centralized control. Control algorithms for swarms are often inspired by decentralised problem-solving systems found in nature. In this paper we conduct a formal analysis of an algorithm inspired by the foraging behaviour of ants, where a swarm of flying vehicles searches for a target at some unknown location. We show how both exhaustive model checking and statistical model checking can be used to check properties that complement the results obtained through simulation, resulting in information that would facilitate the logistics of swarm deployment. An alternative to deploying a single robot of high complexity can be to utilise 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. Self-adaptive systems used in safety-critical and business-critical applications must continue to comply with strict non-functional requirements while evolving in order to adapt to changing workloads, environments, and goals. Runtime quantitative verification (RQV) has been proposed as an effective means of enhancing self-adaptive systems with this capability. However, RQV frequently fails to provide the fast response times and low computation overheads required by real-world self-adaptive systems. In this paper, we investigate how three techniques, namely caching, lookahead and nearly-optimal reconfiguration, and combinations thereof, can help address this limitation. Extensive experiments in a case study involving the RQV-driven self-adaptation of an unmanned underwater vehicle indicate that these techniques can lead to significant reductions in RQV response times and computation overheads. The use of unmanned aircraft for civil applications is expected to increase over the next decade, particularly in so-called dull, dirty, and dangerous missions. Unmanned aircraft will undoubtedly require some form of autonomy to ensure safe operations for all airspace users. However, to be used for civil applications, unmanned aircraft must gain regulatory approval in a process known as “certification”. This paper presents a proof-of-concept approach to the generation of certification evidence for autonomous unmanned aircraft based on a combination of formal verification and flight simulation. In particular, a class of autonomous systems controlled by rational agents is examined, and we give examples of 23 different properties, based on the rules of the air and notions of airmanship, which can be used in the formal model checking of rational agents controlling autonomous unmanned aircraft. Our techniques can be based on either 1) implicit models of the aircraft’s physical environment specified in terms of... Abstract In this paper we present an approach to control a vehicle in a hostile environment with static obstacles and moving adversaries. The vehicle is required to satisfy a mission objective expressed as a temporal logic specification over a set of properties satisfied at regions of a partitioned environment. We model the movements of adversaries in between regions of the environment as Poisson processes. Furthermore, we assume that the time it takes for the vehicle to traverse in between two facets of a region is exponentially distributed, and we obtain the rate of this exponential distribution from a simulator of the environment. We capture the motion of the vehicle and the vehicle updates of adversaries distributions as a Markov Decision Process. Using tools in Probabilistic Computational Tree Logic, we find a control strategy for the vehicle that maximizes the probability of accomplishing the mission objective. We demonstrate our approach with illustrative case studies. Robotic systems are complex and critical: they are inherently hybrid, combining both hardware and software; they typically exhibit both cyber-physical attributes and autonomous capabilities; and are required to be at least safe and often ethical. While for many engineered systems testing, either through real deployment or via simulation, is deemed sufficient the uniquely challenging elements of robotic systems, together with the crucial dependence on sophisticated software control and decision-making, requires a stronger form of verification. The increasing deployment of robotic systems in safety-critical scenarios exacerbates this still further and leads us towards the use of formal methods to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems. There have been many approaches that have used some variety of formal specification or formal verification in autonomous robotics, but there is no resource that collates this activity in to one place. This paper systematically surveys the state-of-the art in specification formalisms and tools for verifying robotic systems. Specifically, it describes the challenges arising from autonomy and software architectures, avoiding low-level hardware control and is subsequently identifies approaches for the specification and verification of robotic systems, while avoiding more general approaches. We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event's occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning. Reinforcement Learning is a well-known AI paradigm whereby control policies of autonomous agents can be synthesized in an incremental fashion with little or no knowledge about the properties of the environment. We are concerned with safety of agents whose policies are learned by reinforcement, i.e., we wish to bound the risk that, once learning is over, an agent damages either the environment or itself. We propose a general-purpose automated methodology to verify, i.e., establish risk bounds, and repair policies, i.e., fix policies to comply with stated risk bounds. Our approach is based on probabilistic model checking algorithms and tools, which provide theoretical and practical means to verify risk bounds and repair policies. Considering a taxonomy of potential repair approaches tested on an artificially-generated parametric domain, we show that our methodology is also more effective than comparable ones.