用概率模型评估农业机器人避障安全，量化不同设计的风险收益。
Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment
- 构建机器人、传感器与人类的三状态机模型，生成概率化风险框架。
- 通过PRISM工具分析，量化高性能检测系统与预警系统的减险效果。
- 适用于早期设计决策，帮助权衡成本与安全性提升方案。
农业机器人的持续应用依赖于农民对其可靠性、鲁棒性和安全性的信任。本文针对农业机器人在检测、追踪和避障方面的能力，提出一种面向早期开发阶段的概率建模与风险分析框架。基于危害识别与风险评估矩阵，采用三个状态机分别刻画移动平台、传感器与感知系统以及在场人员的行为。自动生成的概率模型由PRISM概率模型检查器求解与分析，可量化不同风险缓解措施及设计概念带来的风险降低效果。例如，采用更高性能但更昂贵的目标检测系统，或部署更复杂的预警系统以提升人机警觉性，均能有效降低事故风险。尽管本研究聚焦于概念设计阶段，该框架亦可延伸至实施、部署与运行阶段，为全生命周期安全提供支持。
原文摘要 · Abstract (English)
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.
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