Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles

Nathan Goulet; Beshah Ayalew

doi:10.1016/j.ifacol.2023.12.015

Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles

Nathan Goulet, Beshah Ayalew

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Generally, large discrepancies exist between predicted and realized energy consumption with energy-aware trajectory planning algorithms for off-road vehicles. Conservative planners typically pre-compensate for the expected discrepancy by demanding high confidence thresholds. Global path planners often ignore the substantial energy needed for turning on off-road deformable terrains, contributing to this mismatch. In this paper, we improve energy predictions by adding an additional energy cost for turning maneuvers in the global path planner and reformulate the high-confidence global planner's cost function to reduce conservatism. We couple the proposed global planner with a nominal local planner to show the robustness and improved performance compared to existing energy-aware motion planners for off-road vehicles.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Marcello Canova
Publisher	Elsevier B.V.
Pages	145-150
Number of pages	6
Edition	3
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.12.015
State	Published - Oct 1 2023
Externally published	Yes
Event	3rd Modeling, Estimation and Control Conference, MECC 2023 - Lake Tahoe, United States Duration: Oct 2 2023 → Oct 5 2023

Publication series

Name	IFAC-PapersOnLine
Number	3
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	3rd Modeling, Estimation and Control Conference, MECC 2023
Country/Territory	United States
City	Lake Tahoe
Period	10/2/23 → 10/5/23

Funding

This work was supported by Clemson University's Virtual Prototyping of Autonomy Enabled Ground Systems (VIPR-GS), a US Army Center of Excellence for modeling and simulation of ground vehicles, under Cooperative Agreement W56HZV-21-2-0001 with the US Army DEVCOM Ground Vehicle Systems Center (GVSC). DISTRIBUTION A. Approved for public release; distribution unlimited. OPSEC #: 7467 ⋆ This work was supported by Clemson University’s Virtual Pro- Despite such measures to improve energy predictions, re-ArTmhyisCwenotrekrwoafsEsxucpeplleonrtceedfobrymColedmelsinogn aUnndivseirmsiutlya’tsioVnirotfuaglroPurnod- Despite such measures to improve energy predictions, re-totyping of Autonomy Enabled Ground Systems (VIPR-GS), a US Despite such measures to improve energy predictions, re-toethyipcliensg, uonfdAerutCoonoopmeyraEtinvaebAlegdreGemroeunntdWS5y6sHteZmVs-2(1V-I2P-0R0-0G1Sw)i,tha tUhSe alized energy consumption is intrinsically uncertain and veShiAclrems,yunDdEeVr CCOooMpeGrartoivuendAgVreehemicleenSt yWst5e6mHsZCVe-n2t1e-r2-(0G0V01SwCi)t.h the alized energy consumption is intrinsically uncertain and vehicles,underCooperativeAgreementW56HZV-21-2-0001withtheArmyCenterofExcellenceformodelingandsimulationofground oaftelizendgereneargterythaconsnumtheptionomninais linpretrindicsictioallyn.unOlceivertairainetandal. DeIhSiTclResI,BuUnTdeIrOCNoAop.eAraptpivroevAedgrfeoermpeunbtliWc r5e6leHaZseV;-d2i1s-t2ri-b0u00ti1onwiutnhlitmhe-alitzeendgerneaertgery tchoannsutmheptnioomn iinsalinptrreindsiicctaiollny. uOnlciveeritraainetanadl. DISTRIBUTION A. Approved for public release; distribution unlim- (2016) and Quann et al. (2019) implement Gaussian DIited.STROPIBUSETIC #:ON746A.7Approved for public release; distribution unlim-(2016) and Quann et al. (2019) implement Gaussian ited.DISTROPIBUSETIC #:ON746A.7Approvedforpublicrelease;distributionunlim-(2016) and Quann et al. (2019) implement Gaussian ited. OPSEC #: 7467 ited.2405-8963 Copyright OPSEC #: 746©7 2023 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2023.12.015

Keywords

Automated Vehicles
Energy-Aware Planning
High-Confidence Planning

Access to Document

10.1016/j.ifacol.2023.12.015

Cite this

@inproceedings{d07bbd97a61a4400b660f41f50a4b7aa,

title = "Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles",

abstract = "Generally, large discrepancies exist between predicted and realized energy consumption with energy-aware trajectory planning algorithms for off-road vehicles. Conservative planners typically pre-compensate for the expected discrepancy by demanding high confidence thresholds. Global path planners often ignore the substantial energy needed for turning on off-road deformable terrains, contributing to this mismatch. In this paper, we improve energy predictions by adding an additional energy cost for turning maneuvers in the global path planner and reformulate the high-confidence global planner's cost function to reduce conservatism. We couple the proposed global planner with a nominal local planner to show the robustness and improved performance compared to existing energy-aware motion planners for off-road vehicles.",

keywords = "Automated Vehicles, Energy-Aware Planning, High-Confidence Planning",

author = "Nathan Goulet and Beshah Ayalew",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors.; 3rd Modeling, Estimation and Control Conference, MECC 2023 ; Conference date: 02-10-2023 Through 05-10-2023",

year = "2023",

month = oct,

day = "1",

doi = "10.1016/j.ifacol.2023.12.015",

language = "English",

series = "IFAC-PapersOnLine",

publisher = "Elsevier B.V.",

number = "3",

pages = "145--150",

editor = "Marcello Canova",

booktitle = "IFAC-PapersOnLine",

edition = "3",

}

Goulet, N & Ayalew, B 2023, Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles. in M Canova (ed.), IFAC-PapersOnLine. 3 edn, IFAC-PapersOnLine, no. 3, vol. 56, Elsevier B.V., pp. 145-150, 3rd Modeling, Estimation and Control Conference, MECC 2023, Lake Tahoe, United States, 10/2/23. https://doi.org/10.1016/j.ifacol.2023.12.015

TY - GEN

T1 - Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles

AU - Goulet, Nathan

AU - Ayalew, Beshah

PY - 2023/10/1

Y1 - 2023/10/1

N2 - Generally, large discrepancies exist between predicted and realized energy consumption with energy-aware trajectory planning algorithms for off-road vehicles. Conservative planners typically pre-compensate for the expected discrepancy by demanding high confidence thresholds. Global path planners often ignore the substantial energy needed for turning on off-road deformable terrains, contributing to this mismatch. In this paper, we improve energy predictions by adding an additional energy cost for turning maneuvers in the global path planner and reformulate the high-confidence global planner's cost function to reduce conservatism. We couple the proposed global planner with a nominal local planner to show the robustness and improved performance compared to existing energy-aware motion planners for off-road vehicles.

AB - Generally, large discrepancies exist between predicted and realized energy consumption with energy-aware trajectory planning algorithms for off-road vehicles. Conservative planners typically pre-compensate for the expected discrepancy by demanding high confidence thresholds. Global path planners often ignore the substantial energy needed for turning on off-road deformable terrains, contributing to this mismatch. In this paper, we improve energy predictions by adding an additional energy cost for turning maneuvers in the global path planner and reformulate the high-confidence global planner's cost function to reduce conservatism. We couple the proposed global planner with a nominal local planner to show the robustness and improved performance compared to existing energy-aware motion planners for off-road vehicles.

KW - Automated Vehicles

KW - Energy-Aware Planning

KW - High-Confidence Planning

UR - https://www.scopus.com/pages/publications/85184353359

U2 - 10.1016/j.ifacol.2023.12.015

DO - 10.1016/j.ifacol.2023.12.015

M3 - Conference contribution

AN - SCOPUS:85184353359

T3 - IFAC-PapersOnLine

SP - 145

EP - 150

BT - IFAC-PapersOnLine

A2 - Canova, Marcello

PB - Elsevier B.V.

T2 - 3rd Modeling, Estimation and Control Conference, MECC 2023

Y2 - 2 October 2023 through 5 October 2023

ER -

Improved Energy Predictions for High-Confidence Trajectory Planning of Automated Off-Road Vehicles

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Keywords

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