A Verification Methodology for Safety Assurance of Robotic Autonomous Systems

Autonomous robots deployed in shared human environments, such as agricultural settings, require rigorous safety assurance to meet both functional reliability and regulatory compliance. These systems must operate in dynamic, unstructured environments, interact safely with humans, and respond effectively to a wide range of potential hazards. This paper presents a verification workflow for the safety assurance of an autonomous agricultural robot, covering the entire development life-cycle, from concept study and design to runtime verification. The outlined methodology begins with a systematic hazard analysis and risk assessment to identify potential risks and derive corresponding safety requirements. A formal model of the safety controller is then developed to capture its behaviour and verify that the controller satisfies the specified safety properties with respect to these requirements. The proposed approach is demonstrated on a field robot operating in an agricultural setting. The results show that the methodology can be effectively used to verify safety-critical properties and facilitate the early identification of design issues, contributing to the development of safer robots and autonomous systems.

Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety assurance framework can also be used during implementation, and subsequent deployment and operation phases.