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Mission Energy Prediction for Unmanned Ground Vehicles Using Real‐time Measurements and Prior Knowledge
Author(s) -
Sadrpour Amir,
Jin Jionghua Judy,
Ulsoy A. Galip
Publication year - 2013
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21453
Subject(s) - unmanned ground vehicle , teleoperation , computer science , energy (signal processing) , bayesian probability , energy consumption , artificial intelligence , simulation , machine learning , robot , engineering , statistics , mathematics , electrical engineering
A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real‐time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least‐squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real‐time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.