Towards Safety in Open-field Agricultural Robotic Applications: A Method for Human Risk Assessment using Classifiers

Abstract

Tractors and heavy machinery have been used for decades to improve the quality and overall agriculture production. Moreover, agriculture is becoming a trend domain for robotics, and as a consequence, the efforts towards automatizing agricultural task increases year by year. However, for autonomous applications, accident prevention is of prior importance for warrantying human safety during operation in any scenario. This paper rephrases human safety as a classification problem using a custom distance criterion where each detected human gets a risk level classification. We propose the use of a neural network trained to detect and classify humans in the scene according to these criteria. The proposed approach learns from real-world data corresponding to an open-field scenario and is assessed with a custom risk assessment method.