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Terrain Aware Mobility Planning (TAMP)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56HZV-14-C-0272
Agency Tracking Number: A14A-018-0157
Amount: $149,989.00
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): 2014-09-10
Award End Date (Contract End Date): 2015-01-08
Small Business Information
555 Quince Orchard Road, Suite 300, Gaithersburg, MD, -
DUNS: 121257443
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Alberto Lacaze
 (240) 631-0008
Business Contact
 Alberto Lacaze
Title: President
Phone: (240) 631-0008
Research Institution
 Jet Propulsion Laboratory
 Thomas Fuchs
 4800 Oak Grove Drive
La Canada Flintridge, CA, 91011-
 (818) 354-3637
 Nonprofit college or university
Robotic Research, LLC and Jet Propulsion Laboratory (JPL) at the California Institute of Technology are teaming their efforts under the Army STTR topic A14A-T018"Intelligent Terrain-Award Navigation and Mobility of Unmanned Ground Vehicles Operating Under Varying Degrees of Autonomy"to develop an unmanned terrain-aware navigation and mobility system that would enhance soft soil mobility and reduce vehicle rollovers and to evaluate functionality and performance improvements. Additionally, the work Robotic Research, LLC and JPL will perform will enable the vehicle dynamics model to be highly parameterized and account for both aleatory uncertainties in the parameters as well as epistemic uncertainty in the model form. These models will be run off-line to generate a large database of vehicle performance metrics associated with the different parameter sets and their uncertainties. The results of these large parametric simulations will be summarized as binary go/no-go metrics as a baseline as well as uncertainty associated with the binary description. Our approach addresses the fast computational performance requirement for onboard motion planning and the novelty of our effort thus lies in generating binary go/no-go maps and associating uncertainty metrics with the binary decision on both performance of the vehicle on a very large set of parameterized motion primitives. The approach is applicable on manual vehicle teleoperation, semi-autonomous and fully autonomous operations.

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

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