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Planning the sequence of tasks for harvesting robots
Örebro University, School of Science and Technology. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel. (Centre for Applied Autonomous Sensor System (AASS))ORCID iD: 0000-0003-4685-379X
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0002-7430-8468
2020 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 131, article id 103591Article in journal (Refereed) Published
Abstract [en]

A methodology for planning the sequence of tasks for a harvesting robot is presented. The fruit targets are situated at unknown locations and must be detected by the robot through a sequence of sensing tasks. Once the targets are detected, the robot must execute a harvest action at each target location. The traveling salesman paradigm (TSP) is used to plan the sequence of sensing and harvesting tasks taking into account the costs of the sensing and harvesting actions and the traveling times. Sensing is planned online. The methodology is validated and evaluated in both laboratory and greenhouse conditions for a case study of a sweet pepper harvesting robot. The results indicate that planning the sequence of tasks for a sweet pepper harvesting robot results in 12% cost reduction. Incorporating the sensing operation in the planning sequence for fruit harvesting is a new approach in fruit harvesting robots and is important for cycle time reduction. Furthermore, the sequence is re-planned as sensory information becomes available and the costs of these new sensing operations are also considered in the planning.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 131, article id 103591
Keywords [en]
Harvesting robot, Task sequencing, Traveling salesman problem, Sweet pepper, Agriculture robotics
National Category
Robotics Agricultural Science, Forestry and Fisheries Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-83370DOI: 10.1016/j.robot.2020.103591ISI: 000557188600020Scopus ID: 2-s2.0-85086827649OAI: oai:DiVA.org:oru-83370DiVA, id: diva2:1443780
Funder
EU, Horizon 2020
Note

Funding Agencies:

European Commission Joint Research Centre 644313

Ben-Gurion University of the Negev through the Helmsley Charitable Trust  

Ben-Gurion University of the Negev through the Agricultural, Biological and Cognitive Robotics Initiative  

Ben-Gurion University of the Negev through the Marcus Endowment Fund  

Ben-Gurion University of the Negev through the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering 

Available from: 2020-06-18 Created: 2020-06-18 Last updated: 2022-02-11Bibliographically approved

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Kurtser, Polina

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