Design, integration, and field evaluation of a robotic apple harvester. Issue 6 (28th April 2017)

• Record Type: Journal Article
• Authors: Silwal, Abhisesh; Davidson, Joseph R.; Karkee, Manoj; Mo, Changki; Zhang, Qin; Lewis, Karen
• Other Names: Ball David guestEditor.; Upcroft Ben guestEditor.; van Henten Eldert guestEditor.; van den Hengel Anton guestEditor.; Tokekar Pratap guestEditor.; Das Jnaneshwar guestEditor.
• Is Part Of: Journal of field robotics. Volume 34:Issue 6(2017)
• Page Start: 1140
• Page End: 1159
• Publication Date: 2017-04-28
• Subjects: apple -- cycle time -- machine vision -- manipulator -- robotic harvesting; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21715
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: Every apple destined for the fresh market is picked by the human hand. Despite extensive research over the past four decades, there are no mechanical apple harvesters for the fresh market commercially available, which is a significant concern because of increasing uncertainty about the availability of manual labor and rising production costs. The highly unstructured orchard environment has been a major challenge to the development of commercially viable robotic harvesting systems. This paper reports the design and field evaluation of a robotic apple harvester. The approach adopted was to use a low‐cost system to assess required sensing, planning, and manipulation functionality in a modern orchard system with a planar canopy. The system was tested in a commercial apple orchard in Washington State. Workspace modifications and performance criteria are thoroughly defined and reported to help evaluate the approach and guide future enhancements. The machine vision system was accurate and had an average localization time of 1.5 s per fruit. The seven degree of freedom harvesting system successfully picked 127 of the 150 fruit attempted for an overall success rate of 84% with an average picking time of 6.0 s per fruit. Future work will include integration of additional sensing and obstacle detection for improved system robustness.