Probabilistic model-checking of collaborative robots: A human injury assessment in agricultural applications

Current technology has made it possible to automate a number of agricultural processes that were traditionally carried out by humans and now can be entirely performed by robotic platforms. However, there are certain tasks like soft fruit harvesting, where human skills are still required. In this case, the robot's job is to cooperate/collaborate with human workers to alleviate their physical workload and improve harvesting efficiency. To accomplish that in a safe and reliable way, the robot should incorporate a safety system whose main goal is to reduce the risk of harming human co-workers during close human–robot interaction (HRI). In this context, this paper presents a theoretical study, addressing the safety risks of using collaborative robots in agricultural scenarios, especially in HRI situations when the robot's safety system is not completely reliable and a component may fail. The agricultural scenarios discussed in this paper include automatic harvesting, logistics operations, crop monitoring, and plant treatment using UV-C light. A human injury assessment is conducted based on converting the HRI in each agricultural scenario into a formal mathematical representation. This representation is later implemented in a probabilistic model-checking tool. We then use this tool to perform a sensitivity analysis that allows us to determine the probability that a human may get injured according to the occurrence of failures in the robot's safety or perception systems. Results of the sensitivity analysis show that an agricultural robot with a robust human perception system can still harm people if they are not well-trained to interact with the robot for certain scenarios. This illustrates how the probabilistic modeling methodology presented in this work can be used by safety engineers as a guideline to construct their own HRI models and then use the results of the model-checking to enhance the safety and reliability of their robot's safety system architectures and on-site safety policies.