Multi-agent planning for coordinated robotic weed killing

This work presents techniques for predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment Weed World. In addition to prediction, this method provides physical insight into the seed bank distribution of the field. In this work we extend the evolving Gaussian process model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Secondly, we show that one may decouple the component of the model representing weed growth from the component which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model, and show that the evolving Gaussian process method gives better results, even without assuming this model information. Finally, we use an improved planning index, entropic value-at-risk (EVaR) in conjunction with the Whittle index, which allows a balanced trade-off between exploration and exploitation, and ensures model improvement when used with these various prediction schemes.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• McAllister, Wyatt Spalding

Contributors dc:contributor

• Chowdhary, Girish
• Davis, Adam
• Srikant, Rayadurgam
• Belabbas, Mohamed Ali

• Multi-agent, coordinated robotics, industrial agriculture

Statement dc:rights

• Copyright 2020 Wyatt McAllister

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108240

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F108240