An Efficient Planning and Control Framework for Pruning Fruit Trees

You, A; Sukkar, F; Fitch, R; Karkee, M; Davidson, JR

An Efficient Planning and Control Framework for Pruning Fruit Trees

You, A Sukkar, F

Fitch, R

Karkee, M Davidson, JR

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2020, 00, pp. 3930-3936
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	You, A
dc.contributor.author	Sukkar, F https://orcid.org/0000-0002-7041-5151
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188
dc.contributor.author	Karkee, M
dc.contributor.author	Davidson, JR
dc.date	2020-05-31
dc.date.accessioned	2021-04-17T20:39:52Z
dc.date.available	2021-04-17T20:39:52Z
dc.date.issued	2020-05-01
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2020, 00, pp. 3930-3936
dc.identifier.isbn	9781728173955
dc.identifier.issn	1050-4729
dc.identifier.uri	http://hdl.handle.net/10453/148186
dc.description.abstract	Dormant pruning is a major cost component of fresh market tree fruit production, nearly equal in scale to harvesting the fruit. However, relatively little focus has been given to the problem of pruning trees autonomously. In this paper, we introduce a robotic system consisting of an industrial manipulator, an eye-in-hand RGB-D camera configuration, and a custom pneumatic cutter. The system is capable of planning and executing a sequence of cuts while making minimal assumptions about the environment. We leverage a novel planning framework designed for high-throughput operation which builds upon previous work to reduce motion planning time and sequence cut points intelligently. In end-to-end experiments with a set of ten different branch configurations, the system achieved a high success rate in plan execution and a 1.5x speedup in throughput versus a baseline planner, representing a significant step towards the goal of practical implementation of robotic pruning.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation
dc.relation.ispartof	IEEE International Conference on Robotics and Automation
dc.relation.isbasedon	10.1109/ICRA40945.2020.9197551
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	An Efficient Planning and Control Framework for Pruning Fruit Trees
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Paris, Texas (Virtual)
utslib.for	091302 Automation and Control Engineering
utslib.for	090602 Control Systems, Robotics and Automation
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-09-15T00:00:00+1000Z
dc.date.updated	2021-04-17T20:39:50Z
pubs.finish-date	2020-08-31
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-05-31
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Dormant pruning is a major cost component of fresh market tree fruit production, nearly equal in scale to harvesting the fruit. However, relatively little focus has been given to the problem of pruning trees autonomously. In this paper, we introduce a robotic system consisting of an industrial manipulator, an eye-in-hand RGB-D camera configuration, and a custom pneumatic cutter. The system is capable of planning and executing a sequence of cuts while making minimal assumptions about the environment. We leverage a novel planning framework designed for high-throughput operation which builds upon previous work to reduce motion planning time and sequence cut points intelligently. In end-to-end experiments with a set of ten different branch configurations, the system achieved a high success rate in plan execution and a 1.5x speedup in throughput versus a baseline planner, representing a significant step towards the goal of practical implementation of robotic pruning.

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http://hdl.handle.net/10453/148186