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Ryan McGann

dblp:359/4710 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 50% Robot manipulation · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
agricultural manipulation
0.812024
Containerized Vertical Farming Using Cobots · ICRA 2024
Robotics › Motion planning and robot control › motion planning › manipulation planning
manipulation task planning
0.812024
Containerized Vertical Farming Using Cobots · ICRA 2024
Computational science and engineering
agricultural robotics
0.212024
Containerized Vertical Farming Using Cobots · ICRA 2024

Methods — techniques the papers use, named apart from their topics

learning from demonstration · 1.5RGB-D camera · 1.5
YearPublicationVenuePosition
2024 Containerized Vertical Farming Using Cobots
abstract
Containerized vertical farming is a type of vertical farming practice using hydroponics in which plants are grown in vertical layers within a mobile shipping container. Space limitations within shipping containers make the automation of different farming operations challenging. In this paper, we explore the use of cobots (i.e., collaborative robots) to automate two key farming operations, namely, the transplantation of saplings and the harvesting of grown plants. Our method uses a single demonstration from a farmer to extract the motion constraints associated with the tasks, namely, transplanting and harvesting, and can then generalize to different instances of the same task. For transplantation, the motion constraint arises during insertion of the sapling within the growing tube, whereas for harvesting, it arises during extraction from the growing tube. We present experimental results to show that using RGBD camera images (obtained from an eye-in-hand configuration) and one demonstration for each task, it is feasible to perform transplantation of saplings and harvesting of leafy greens using a cobot, without task-specific programming.
Dasharadhan Mahalingam, Aditya Patankar, Khiem Phi, Ryan McGann, I. V. Ramakrishnan
ICRA5