Efficient Multi-Robot Inspection of Row Crops via Kernel Estimation and Region-Based Task Allocation

Merrill Edmonds, Jingang Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Modern agriculture relies on accurate and timely data. Currently, most of this data is gathered using remote sensing, which uses a combination of satellite and aerial imagery. However, ground robots are needed to fill in the gaps for finer ground-level data and the execution of physical tasks such as sample collection. The scales at which crops are produced preclude the inspection of each and every plant, thus requiring the selection of a smaller number of inspection targets. In this paper, we solve this multi-robot inspection problem using a novel task allocation algorithm. The algorithm derives its utility function from a model based on Gaussian process machine learning with a kernel that is learned from previous data. The algorithm also considers the physical limitations of moving within crop rows by dividing the plot into geodesic Voronoi regions based on robot locations. Simulation studies are performed to validate the method.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8919-8926
Number of pages8
ISBN (Electronic)9781728190778
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: May 30 2021Jun 5 2021

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN (Print)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period5/30/216/5/21

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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