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

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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Zhang, C. , and Vahidi, A. , 2010, “ Real-Time Optimal Control of Plug-In Hybrid Vehicles With Trip Preview,” IEEE American Control Conference (ACC), Baltimore, MD, June 30–July 2, pp. 6917–6922.
Zhang, C. , and Vahidi, A. , 2012, “ Route Preview in Energy Management of Plug-In Hybrid Vehicles,” IEEE Trans. Control Syst. Technol., 20(2), pp. 546–553. [CrossRef]
Zhang, C. , Vahidi, A. , Pisu, P. , Li, X. , and Tennant, K. , 2010, “ Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles,” IEEE Trans. Veh. Technol., 59(3), pp. 1139–1147. [CrossRef]
Jackel, L. D. , Krotkov, E. , Perschbacher, M. , Pippine, J. , and Sullivan, C. , 2006, “ The DARPA LAGR Program: Goals, Challenges, Methodology, and Phase I Results,” J. Field Rob., 23(11–12), pp. 945–973. [CrossRef]
Shiller, Z. , and Gwo, Y.-R. , 1991, “ Dynamic Motion Planning of Autonomous Vehicles,” IEEE Trans. Rob. Autom., 7(2), pp. 241–249. [CrossRef]
Thrun, S. , Montemerlo, M. , Dahlkamp, H. , Stavens, D. , Aron, A. , Diebel, J. , Fong, P. , Gale, J. , Halpenny, M. , Hoffmann, G. , Lau, K. , Oakley, C. , Palatucci, M. , Pratt, V. , Stang, P. , Strohband, S. , Dupont, C. , Jendrossek, L.-E. , Koelen, C. , Markey, C. , Rummel, C. , van Niekerk, J. , Jensen, E. , Alessandrini, P. , Bradski, G. , Davies, B. , Ettinger, S. , Kaehler, A. , Nefian, A. , and Mahoney, P. , 2006, “ Stanley: The Robot That Won the DARPA Grand Challenge,” J. Field Rob., 23(9), pp. 661–692. [CrossRef]
Choi, J. , Lee, J. , Kim, D. , Soprani, G. , Cerri, P. , Broggi, A. , and Yi, K. , 2012, “ Environment-Detection-and-Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment,” IEEE Trans. Intell. Transp. Syst., 13(2), pp. 974–982. [CrossRef]
Stavens, D. , and Thrun, S. , 2006, “ A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous Driving,” 22nd Conference on Uncertainty in Artificial Intelligence (UAI'06), Cambridge, MA, July 13–16, pp. 469–476. https://dl.acm.org/citation.cfm?id=3020476
Lalonde, J.-F. , Vandapel, N. , Huber, D. F. , and Hebert, M. , 2006, “ Natural Terrain Classification Using Three-Dimensional Ladar Data for Ground Robot Mobility,” J. Field Rob., 23(10), pp. 839–861.
Manduchi, R. , Castano, A. , Talukder, A. , and Matthies, L. , 2005, “ Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation,” Auton. Robots, 18(1), pp. 81–102. [CrossRef]
Broggi, A. , Cardarelli, E. , Cattani, S. , and Sabbatelli, M. , 2013, “ Terrain Mapping for Off-Road Autonomous Ground Vehicles Using Rational B-Spline Surfaces and Stereo Vision,” IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia, June 23–26, pp. 648–653.
Richards, A. , and How, J. , 2004, “ Decentralized Model Predictive Control of Cooperating UAVs,” 43rd IEEE Conference on Decision and Control (CDC 2004), Nassau, Bahamas, Dec. 14–17, pp. 4286–4291.
Tisdale, J. , Kim, Z. , and Hedrick, J. K. , 2009, “ Autonomous UAV Path Planning and Estimation,” IEEE Rob. Autom. Mag., 16(2), pp. 35–42. [CrossRef]
Bellingham, J. S. , Tillerson, M. , Alighanbari, M. , and How, J. P. , 2002, “ Cooperative Path Planning for Multiple UAVs in Dynamic and Uncertain Environments,” 41st IEEE Conference on Decision and Control (CDC), Las Vegas, NV, Dec. 10–13, pp. 2816–2822.
Tsourdos, A. , White, B. , and Shanmugavel, M. , 2010, Cooperative Path Planning of Unmanned Aerial Vehicles, Vol. 32, Wiley, Chichester, UK.
Chandler, P. , Rasmussen, S. , and Pachter, M. , 2000, “UAV Cooperative Path Planning,” AIAA Paper No. 2000-4370.
Flint, M. , Polycarpou, M. , and Fernandez-Gaucherand, E. , 2002, “ Cooperative Path-Planning for Autonomous Vehicles Using Dynamic Programming,” IFAC 15th Triennial World Congress, Barcelona, Spain, July 21–26, pp. 1694–1699.
Rhodes, C. , Blewitt, W. , Sharp, C. , Ushaw, G. , and Morgan, G. , 2014, “ Smart Routing: A Novel Application of Collaborative Path-Finding to Smart Parking Systems,” IEEE 16th Conference on Business Informatics (CBI) Geneva, Switzerland, July 14–17, pp. 119–126.
Megherbi, D. , and Kim, M. , 2015, “ A Collaborative Distributed Multi-Agent Reinforcement Learning Technique for Dynamic Agent Shortest Path Planning Via Selected Sub-Goals in Complex Cluttered Environments,” IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), Orlando, FL, Mar. 9–12, pp. 118–124.
Sturtevant, N. R. , and Buro, M. , 2006, “ Improving Collaborative Pathfinding Using Map Abstraction,” AIIDE, American Association for Artificial Intelligence, Menlo Park, CA, pp. 80–85.
Fredman, M. L. , and Tarjan, R. E. , 1987, “ Fibonacci Heaps and Their Uses in Improved Network Optimization Algorithms,” J. ACM, 34(3), pp. 596–615. [CrossRef]
U.S. Geological Survey, 2017, “ The National Map,” U.S. Department of the Interior, Washington, DC, accessed Jan. 5, 2016, http://nationalmap.gov/index.html
U.S. Geological Survey, 2016, “ 3D Elevation Program (3DEP),” U.S. Department of the Interior, Washington, DC, accessed Jan. 5, 2016, http://nationalmap.gov/3DEP/index.html
National Resources Conservation Service, 2016, “ Web Soil Survey,” U.S. Department of Agriculture, Washington, DC, accessed Jan. 5, 2016, https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx
U.S. Army, and U.S. Air Force, 1994, “Planning and Design of Roads, Airfields, and Heliports in the Theater of Operations–Road Design,” Vol. 1, U.S. Department of the Army/U.S. Department of the Air Force, Washington, DC, Report No. FM 5-430-00-1/AFJPAM 32-8013. https://webapp1.dlib.indiana.edu/virtual_disk_library/index.cgi/821003/FID577/pubs/af/32/afjpam32-8013v1/afjpam32-8013v1.pdf
Campbell, S. , Naeem, W. , and Irwin, G. W. , 2012, “ A Review on Improving the Autonomy of Unmanned Surface Vehicles Through Intelligent Collision Avoidance Manoeuvres,” Annu. Rev. Control, 36(2), pp. 267–283. [CrossRef]
Li, P. , Zhu, J. , and Peng, F. , 2014, “ Comparison of A* and Lambda* Algorithm for Path Planning,” Engineering Technology and Applications: International Conference on Engineering Technology and Applications (ICETA 2014), Tsingtao, China, Apr. 29–30, p. 171.
Knowles, B. A. , 2016, “In the Face of Anticipation: Decision Making Under Visible Uncertainty as Present in the Safest-With-Sight Problem,” Master's thesis & Specialist Projects, Western Kentucky University, Bowling Green, KY. http://digitalcommons.wku.edu/theses/1565
Sidran, D. E. , 2007, “Good Decisions Under Fire,” Doctoral thesis, University of Iowa, Iowa City, IA. http://www.riverviewai.com/papers/GoodDecisionsUnderFire.pdf
Kamphuis, A. , Rook, M. , and Overmars, M. H. , 2005, “ Tactical Path Finding in Urban Environments,” First International Workshop on Crowd Simulation, Lausanne, Switzerland, Nov. 24–25, pp. 51–60.
Carver, S. , and Washtell, J. , 2012, “ Real-Time Visibility Analysis and Rapid Viewshed Calculation Using a Voxel-Based Modelling Approach,” GISRUK 2012 Conference, Lancaster, UK, Apr. 11–13.
Sniedovich, M. , 2006, “ Dijkstra's Algorithm Revisited: The Dynamic Programming Connexion,” Control Cybern., 35(3), p. 599. http://matwbn.icm.edu.pl/ksiazki/cc/cc35/cc3536.pdf
Wang, Q. , and Müller, S. , 2016, “ A Hierarchical Controller for Path Planning and Path Following Based on Model Predictive Control,” Advanced Vehicle Control: 13th International Symposium on Advanced Vehicle Control (AVEC'16), Munich, Germany, Sept. 13–16, p. 195. http://www.avec16.com/images/papers/47300.pdf
Rasekhipour, Y. , Khajepour, A. , Chen, S.-K. , and Litkouhi, B. , 2017, “ A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles,” IEEE Trans. Intell. Transp. Syst., 18(5), pp. 1255–1267. [CrossRef]


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Fig. 1

Overview of the route guidance algorithm

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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. 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. 7

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

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Fig. 8

Effect of obstacles on prescribed route

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Fig. 9

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



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