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Article: Plant Phenotyping by Deep-Learning-Based Planner for Multi-Robots

TitlePlant Phenotyping by Deep-Learning-Based Planner for Multi-Robots
Authors
KeywordsPlanning
Robot sensing systems
Manipulators
Multi-robot systems
Three-dimensional displays
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE
Citation
IEEE Robotics and Automation Letters, 2019, v. 4 n. 4, p. 3113-3120 How to Cite?
AbstractManual plant phenotyping is slow, error prone, and labor intensive. In this letter, we present an automated robotic system for fast, precise, and noninvasive measurements using a new deep-learning-based next-best view planning pipeline. Specifically, we first use a deep neural network to estimate a set of candidate voxels for the next scanning. Next, we cast rays from these voxels to determine the optimal viewpoints. We empirically evaluate our method in simulations and real-world robotic experiments with up to three robotic arms to demonstrate its efficiency and effectiveness. One advantage of our new pipeline is that it can be easily extended to a multi-robot system where multiple robots move simultaneously according to the planned motions. Our system significantly outperforms the single robot in flexibility and planning time. High-throughput phenotyping can be made practically.
Persistent Identifierhttp://hdl.handle.net/10722/273147
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, C-
dc.contributor.authorZeng, R-
dc.contributor.authorPan, J-
dc.contributor.authorWang, CCL-
dc.contributor.authorLiu, YJ-
dc.date.accessioned2019-08-06T09:23:24Z-
dc.date.available2019-08-06T09:23:24Z-
dc.date.issued2019-
dc.identifier.citationIEEE Robotics and Automation Letters, 2019, v. 4 n. 4, p. 3113-3120-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/273147-
dc.description.abstractManual plant phenotyping is slow, error prone, and labor intensive. In this letter, we present an automated robotic system for fast, precise, and noninvasive measurements using a new deep-learning-based next-best view planning pipeline. Specifically, we first use a deep neural network to estimate a set of candidate voxels for the next scanning. Next, we cast rays from these voxels to determine the optimal viewpoints. We empirically evaluate our method in simulations and real-world robotic experiments with up to three robotic arms to demonstrate its efficiency and effectiveness. One advantage of our new pipeline is that it can be easily extended to a multi-robot system where multiple robots move simultaneously according to the planned motions. Our system significantly outperforms the single robot in flexibility and planning time. High-throughput phenotyping can be made practically.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE-
dc.relation.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectPlanning-
dc.subjectRobot sensing systems-
dc.subjectManipulators-
dc.subjectMulti-robot systems-
dc.subjectThree-dimensional displays-
dc.titlePlant Phenotyping by Deep-Learning-Based Planner for Multi-Robots-
dc.typeArticle-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LRA.2019.2924125-
dc.identifier.scopuseid_2-s2.0-85069797481-
dc.identifier.hkuros300335-
dc.identifier.volume4-
dc.identifier.issue4-
dc.identifier.spage3113-
dc.identifier.epage3120-
dc.publisher.placeUnited States-

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