Efficient Stochastic Mobility Prediction for Mobile Robotic Systems

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$99,963.00
Award Year:
2007
Program:
STTR
Phase:
Phase I
Contract:
W912HZ-07-P-0288
Agency Tracking Number:
A074-026-0191
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
QUANTUM SIGNAL, LLC
3741 Plaza Drive, Suite 1, Ann Arbor, MI, 48108
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
119094493
Principal Investigator:
Mitchell Rohde
COO
(734) 994-0028
rohde@quantumsignal.com
Business Contact:
Mitchell Rohde
COO
(734) 994-0028
rohde@quantumsignal.com
Research Institution:
MIT
Stephen McAlarney
77 Massachusetts Avenue
Cambridge, MA, 02139-
(617) 253-3856
Nonprofit college or university
Abstract
Future Army operations will employ small (i.e. sub-500 kg) autonomous or semi-autonomous UGVs in both cross-country and urban environments. A fundamental requirement of these UGVs is to quickly and robustly predict their ability to successfully negotiate terrain regions and surmount obstacles. This mobility prediction capability is critical to successful deployment of UGVs that can operate effectively in challenging terrain with minimal or no human supervision. The purpose of this proposed research program is to develop a robust, efficient method for UGV mobility prediction that exploits recent advances in statistical simulation to yield a fundamentally new approach to mobility prediction for small UGVs. By coupling rigorous statistical techniques with physics-based UGV and terrain models, the methods will yield accurate predictions of mobility in general 3D terrain and not rely on idealized obstacle "primitives". The result of this Phase I research will be a proof-of-concept demonstration of the proposed mobility prediction method operating on an Army-relevant UGV test bed in a simulation environment. The proposed work would be performed as a collaboration between Quantum Signal, LLC (QS) and the Massachusetts Institute of Technology.

* information listed above is at the time of submission.

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