Ambient awareness for agricultural robotic vehicles

TL;DR

This paper presents a multi-sensory perception system combining stereovision, LIDAR, radar, and thermography to enhance environmental awareness for agricultural vehicles, improving obstacle detection and safety in diverse field conditions.

Contribution

It introduces novel methods for sensor data fusion to automatically detect obstacles and identify traversable areas, advancing autonomous agricultural vehicle perception capabilities.

Findings

Effective obstacle detection in agricultural environments

Improved identification of traversable areas

Enhanced ambient awareness for autonomous vehicles

Abstract

In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate and robust environmental perception is a critical requirement to address unsolved issues including safe interaction with field workers and animals, obstacle detection in controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness, in general, towards increased process automation. Given the variety of conditions thatmay be encountered in the field, no single sensor exists that can guarantee reliable results in every scenario. The development of a multi-sensory perception systemto increase the ambient awareness of an agricultural vehicle operating in crop fields is the objective of the Ambient Awareness for Autonomous Agricultural Vehicles (QUAD-AV) project. Different onboard sensor technologies, namely stereovision, LIDAR, radar, and thermography, are considered. Novel methods for their combination are proposed to automatically detect obstacles and discern traversable from non-traversable areas. Experimental results, obtained in agricultural contexts, are presented showing the effectiveness of the proposed methods.