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Localization Algorithm Design and Evaluation for an Autonomous Pollination Robot

Published in Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), 2019

As the population of natural pollinators declines, there is an increased desire to supplement their functionally artificially. To support agriculture productions when natural pollinators are not available, a methodology for realizing artificial pollination that has received considerable interest – both within the academic and commercial communities – is the utilization of autonomous robotic systems. One essential component within the complex architecture of an autonomous pollinating robot is an accurate, efficient, and robust localization and mapping subsystem (i.e., a subsystem that maps the environment and localizes the robot). This paper details the algorithmic design of a factor-graph based localization and mapping subsystem used to operate within a greenhouse environment. The presented system is validated on multiple collected data-sets both in the greenhouse and an outdoor farm.

Recommended citation: @article{yanglocalization, title={Localization Algorithm Design and Evaluation for an Autonomous Pollination Robot}, author={Yang, Chizhao and Watson, Ryan M and Gross, Jason N and others} } https://www.researchgate.net/profile/Ryan_Watson7/publication/336452555_Localization_Algorithm_Design_and_Evaluation_for_an_Autonomous_Pollination_Robot/links/5db30b5192851c577ec34dfa/Localization-Algorithm-Design-and-Evaluation-for-an-Autonomous-Pollination-Robot.pdf

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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