Platform for evaluating sensors and human detection in autonomous mowing operations

Abstract

The concept of autonomous farming concerns automatic agricultural machines operating safely and efficiently without human intervention. In order to ensure safe autonomous operation, real-time risk detection and avoidance must be undertaken. This paper presents a flexible vehicle-mounted sensor system for recording positional and imaging data with a total of six sensors, and a full procedure for calibrating and registering all sensors. Authentic data were recorded for a case study on grass-harvesting and human safety. The paper incorporates parts of ISO 18497 (an emerging standard for safety of highly automated machinery in agriculture) related to human detection and safety. The case study investigates four different sensing technologies and is intended as a dataset to validate human safety or a human detection system in grass-harvesting. The study presents common algorithms that are able to detect humans, but struggle to handle lying or occluded humans in high grass.