Choosing classification thresholds for mobile robot coverage

Parikshit Maini, Volkan Isler

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

Abstract

Many robotic coverage applications involve detection of spatially distributed targets, followed by path planning to visit them for service. In these applications, the performance of the detection algorithm can have profound effect on planning decisions and costs. Range of operation, in both space and time, for robots is typically finite over a single mission and is a common constraint that needs to be accounted for in decision making. Misclassification may result in wastage of resources and can even jeopardize the completion of a mission if the length of a path extends beyond the range of the robot.In this work, we develop techniques on the computation of planning-aware classification thresholds. We discuss two versions that compute binary classification thresholds as a function of planning budget and detection accuracy. We present an implementation of our methods in path planning applications for an autonomous mower and show results on real and simulated data. Our method allows upto 25% improvement in coverage as compared to standard thresholding methods.

Original languageEnglish (US)
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2630-2635
Number of pages6
ISBN (Electronic)9781728162126
DOIs
StatePublished - Oct 24 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: Oct 24 2020Jan 24 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
CountryUnited States
CityLas Vegas
Period10/24/201/24/21

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This project is supported in part by MN LCCMR program and the SmartFarm project supported by Tiné.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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