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.
How should autonomous systems be verified is a new challenging question along with their increasing applications @cite_27 . Formal methods must be integrated in order to develop, verify and provide certification evidence for large-scale and complex autonomous systems like robots @cite_9 .
@cite_9 @cite_27
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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. Industrial-sized hybrid systems are typically not amenable to formal verification techniques. For this reason, a common approach is to formally verify abstractions of (parts of) the original system. However, we need to show that this abstraction conforms to the actual system implementation including its physical dynamics. In particular, verified properties of the abstract system need to transfer to the implementation. To this end, we introduce a formal conformance relation, called reachset conformance, which guarantees transference of safety properties, while being a weaker relation than the existing trace inclusion conformance. Based on this formal relation, we present a conformance testing method which allows us to tune the trade-off between accuracy and computational load. Additionally, we present a test selection algorithm that uses a coverage measure to reduce the number of test cases for conformance testing. We experimentally show the benefits of our novel techniques based on an example from autonomous driving. An approach for formally verifying the safety of automated vehicles is proposed. Due to the uniqueness of each traffic situation, we verify safety online, i.e., during the operation of the vehicle. The verification is performed by predicting the set of all possible occupancies of the automated vehicle and other traffic participants on the road. In order to capture all possible future scenarios, we apply reachability analysis to consider all possible behaviors of mathematical models considering uncertain inputs (e.g., sensor noise, disturbances) and partially unknown initial states. Safety is guaranteed with respect to the modeled uncertainties and behaviors if the occupancy of the automated vehicle does not intersect that of other traffic participants for all times. The applicability of the approach is demonstrated by test drives with an automated vehicle at the Robotics Institute at Carnegie Mellon University. A major challenge towards large scale deployment of autonomous mobile robots is to program them with formal guarantees and high assurance of correct operation. To this end, we present a framework for building safe robots. Our approach for validating the end-to-end correctness of robotics system consists of two parts: (1) a high-level programming language for implementing and systematically testing the reactive robotics software via model checking; (2) a signal temporal logic (STL) based online monitoring system to ensure that the assumptions about the low-level controllers (discrete models) used during model checking hold at runtime. Combining model checking with runtime verification helps us bridge the gap between software verification (discrete) that makes assumptions about the low-level controllers and the physical world, and the actual execution of the software on a real robotic platform in the physical world. To demonstrate the efficacy of our approach, we build a safe adaptive surveillance system and present software-in-the-loop simulations of the application.