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Research Papers

A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles

[+] Author and Article Information
Judhajit Roy

Ford Motor Company,
20000 Rotunda Drive,
Dearborn, MI 48124
e-mail: jroy@g.clemson.edu

Nianfeng Wan

IAV Automotive Engineering, Inc.,
303 Twin Dolphin Drive, Suite 6084,
Redwood City, CA 94065
e-mail: nianfengwan@gmail.com

Angshuman Goswami

Department of Mechanical Engineering,
Clemson University,
Flour Daniel Building,
Clemson, SC 29634
e-mail: agoswami@g.clemson.edu

Ardalan Vahidi

Department of Mechanical Engineering,
Clemson University,
Flour Daniel Building,
Clemson, SC 29634
e-mail: avahidi@clemson.edu

Paramsothy Jayakumar

U.S. Army RDECOM TARDEC,
6501 E.11 Mile Road,
Warren, MI 48397
e-mail: paramsothy.jayakumar.civ@mail.mil

Chen Zhang

Ford Motor Company,
20000 Rotunda Drive,
Dearborn, MI 48124
e-mail: czhang56@ford.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 19, 2017; final manuscript received December 28, 2017; published online February 13, 2018. Assoc. Editor: Mahdi Shahbakhti.This material is declared a work of the U.S. Government and is not subject to copyright protection in the U.S. Approved for public release; distribution is unlimited.

J. Dyn. Sys., Meas., Control 140(7), 071011 (Feb 13, 2018) (9 pages) Paper No: DS-17-1310; doi: 10.1115/1.4038905 History: Received June 19, 2017; Revised December 28, 2017

A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.

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Figures

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Fig. 2

Information layers: (a) elevation map with tower location, (b) soil map, (c) visibility map (dark-invisible, light-visible), and (d) cost-to-go map

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Fig. 1

Overview of the route guidance algorithm

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Fig. 8

Effect of obstacles on prescribed route

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Fig. 7

Prescribed routes with different information layers: (a) elevation + soil and (b) elevation + soil + visibility

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Fig. 3

Model predictive control radial steps and the optimal cost-to-go at the end of MPC circular horizon shown as varying fence height

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Fig. 4

Schematic of stepping through MPC's circular optimization horizon

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Fig. 5

Schematic of the proposed fleet formation in which sensor horizon of each vehicle touches of its neighboring vehicle for increased “field of view”

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Fig. 6

Connected fleet optimization and information flow diagrams: (a) procedural flow and (b) information flow

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Fig. 9

Coordinated versus individual path planning: (a) independent vehicles and (b) coordinated fleet

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