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Strategies for selecting best approach direction for a sweet-pepper harvesting robot
Department of Computing Science, Umeå University, Umeå, Sweden.ORCID iD: 0000-0002-4600-8652
Department of Industrial Engineering and Management Ben-Gurion, University of the Negev, Beer Sheva, Israel. (MRO)ORCID iD: 0000-0001-5759-9198
Department of Industrial Engineering and Management Ben-Gurion, University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0002-7430-8468
2017 (English)In: Towards Autonomous Robotic Systems (Taros 2017) / [ed] Yang Gao, Saber Fallah, Yaochu Jin, Constantina Lekakou, Cham: Springer, 2017, p. 516-525Conference paper, Published paper (Refereed)
Abstract [en]

An autonomous sweet pepper harvesting robot must perform several tasks to successfully harvest a fruit. Due to the highly unstructured environment in which the robot operates and the presence of occlusions, the current challenges are to improve the detection rate and lower the risk of losing sight of the fruit while approaching the fruit for harvest. Therefore, it is crucial to choose the best approach direction with least occlusion from obstacles.

The value of ideal information regarding the best approach direction was evaluated by comparing it to a method attempting several directions until successful harvesting is performed. A laboratory experiment was conducted on artificial sweet pepper plants using a system based on eye-in-hand configuration comprising a 6DOF robotic manipulator equipped with an RGB camera. The performance is evaluated in laboratorial conditions using both descriptive statistics of the average harvesting times and harvesting success as well as regression models. The results show roughly 40–45% increase in average harvest time when no a-priori information of the correct harvesting direction is available with a nearly linear increase in overall harvesting time for each failed harvesting attempt. The variability of the harvesting times grows with the number of approaches required, causing lower ability to predict them.

Tests show that occlusion of the front of the peppers significantly impacts the harvesting times. The major reason for this is the limited workspace of the robot often making the paths to positions to the side of the peppers significantly longer than to positions in front of the fruit which is more open.

Place, publisher, year, edition, pages
Cham: Springer, 2017. p. 516-525
Series
Lecture Notes in Computer Science : Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 10454
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
computer and systems sciences
Identifiers
URN: urn:nbn:se:oru:diva-79436DOI: 10.1007/978-3-319-64107-2_41ISI: 000453217100041ISBN: 978-3-319-64107-2 (electronic)ISBN: 978-3-319-64106-5 (print)OAI: oai:DiVA.org:oru-79436DiVA, id: diva2:1388995
Conference
TAROS 2017: the 18th Towards Autonomous Robotic Systems (TAROS) Conference, University of Surrey, Guildford, UK, July 19–21, 2017
Funder
EU, Horizon 2020, 66313Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-24Bibliographically approved

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Ringdahl, OlaKurtser, Polina

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Ringdahl, OlaKurtser, PolinaEdan, Yael
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