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

TL;DR

This paper presents a probabilistic safety assurance framework for agricultural robots, enabling early risk analysis and design optimization to enhance reliability and safety in obstacle and human detection.

Contribution

It introduces a probabilistic modelling and risk analysis approach using state machines and PRISM for early safety assessment of agricultural robots.

Findings

Quantifies the impact of different risk mitigation strategies.

Provides insights into design choices affecting safety.

Framework applicable across development and deployment phases.

Abstract

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.