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Robust Terrain-Adaptive Vehicle Planning and Control

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56HZV-14-C-0271
Agency Tracking Number: A14A-018-0183
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A14A-T018
Solicitation Number: 2014.A
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-09-25
Award End Date (Contract End Date): 2015-03-24
Small Business Information
200 N. Ann Arbor St
Saline, MI -
United States
DUNS: 119094493
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mitchell Rohde
 Chief Executive Officer
 (734) 429-9100
Business Contact
 Disa Webb
Title: Business Operations Manager
Phone: (734) 429-9100
Research Institution
 Michelle Hudak
77 Massachusetts Avenue
Cambridge, MA 02139-
United States

 (617) 324-5382
 Nonprofit College or University

Autonomous or teleoperated navigation of unmanned ground vehicles (UGVs) is difficult even in benign environments due to challenges associated with perception, decision making, and human-machine interaction, among others. In environments with rough, sloped, slippery, and/or deformable terrain, the difficulty of the navigation problem increases dramatically. In this effort, Quantum Signal, LLC, University of Michigan, and Massachusetts Institute of Technology propose to collaboratively research methods for robust terrain-adaptive planning and control to enable a future generation of UGVs with assured mobility in highly challenging terrain. The approach will exploit physics-based terrain modeling with data-driven variance estimation, stochastic vehicle motion planning through feasible corridor, and terrain-adaptive predictive vehicle control integrated into a threat-based control arbitration architecture. This architecture will enable operation at (and seamless transition between) any point on the autonomy spectrum, ranging from manual teleoperation to full autonomy. In Phase 1 the team will develop, test, and characterize algorithm performance with Quantum Signal"s high fidelity ANVEL robotic vehicle simulator and determine feasibility. Should the methods prove feasible, Phase 2 will involve the further development, integration, and testing of the methodology on experimental vehicle hardware.

* Information listed above is at the time of submission. *

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