Ambient awareness for agricultural robotic vehicles
This addresses the need for safe and automated operations in agriculture, such as interaction with workers and obstacle detection, but it is incremental as it builds on existing sensor technologies with novel combination methods.
The paper tackled the problem of environmental perception for agricultural robotic vehicles by developing a multi-sensory system combining stereovision, LIDAR, radar, and thermography to detect obstacles and discern traversable areas, with experimental results demonstrating its effectiveness in agricultural contexts.
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.