Risk Assessment Methods for Autonomous Agricultural Machines: A Review of Current Practices and Future Needs

摘要

Highlights Risk assessment for highly automated and autonomous agricultural machines must consider risks beyond operator risk. Engineering standards are a starting point for autonomous equipment risk assessment but are not yet adequate. Engineers designing highly automated equipment now assess risk holistically but need more tools and support. Education in accredited engineering programs and professional development should include risk assessment. Abstract. Technology continues to advance in agricultural machines and includes the development of highly automated, robotic, autonomous, and other types of machines used in fields, farmsteads, buildings, and other farm production locations. New engineering design and safety-related standards have been developed in the past half-decade, but safety remains a concern of key stakeholders and is a barrier that could influence widespread adoption. A survey of practicing engineers and researchers involved with highly automated and autonomous agricultural machine design will be presented that shows the methods for risk assessment and control currently in use including different frameworks for hazard and failure identification, prediction, and quantification. The use of engineering design standards (ASABE, ISO, and others) among practitioners is discussed including some important needs that go beyond obstacle detection and injury prevention for operators. These include safety and risk issues connected to animals, property, civic infrastructure, downtime, cyber, and environmental risk. Commonly used risk assessment methods such as the related failure modes and effects analysis (FMEA) or hazard analysis and risk assessment (HARA) are a useful starting point but are based on historical data and experience that can be used to estimate the probability and severity levels of undesirable failures or incidents such as injuries. These data do not yet exist as compared to risk assessment data that can be used to assess incident occurrence probability, failure, detectability, or controllability in more traditional machines. Suggestions are presented for further development of standards and practice recommendations including software needs and operational data that might be used by autonomous machines that is informed by what we do know about past farm incidents that could include accidents, injuries, and other unexpected failures. Keywords: Automation, Autonomous agricultural machinery, Engineering design standards, Farm equipment, Risk assessment, Robotics, Safety.