Ambient awareness for agricultural robotic vehicles. (June 2016)

• Record Type: Journal Article
• Authors: Reina, Giulio; Milella, Annalisa; Rouveure, Raphaël; Nielsen, Michael; Worst, Rainer; Blas, Morten R.
• Is Part Of: Biosystems engineering. Volume 146(2016:Jun.)
• Page Start: 114
• Page End: 132
• Publication Date: 2016-06
• Subjects: Agricultural robotics -- Intelligent vehicles -- Safe driving in crop fields -- Advanced perception systems -- Ambient awareness; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2015.12.010
• Languages: English
• ISSNs: 1537-5110

Abstract

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 that may be encountered in the field, no single sensor exists that can guarantee reliable results in every scenario. The development of a multi-sensory perception system to 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. Highlights: Ambient awareness for agricultural vehicles operating in crop fields. Safe-driving in agriculture contexts using multi-sensory perception. Complementary sensor technologies (stereovision, LIDAR, radar, and thermography). Novel methods to detect obstacles and discern traversable from non-traversable areas. Field validation showing the potential for driver assistance navigation systems.