Efficient Stochastic Mobility Prediction for Mobile Robotic Systems

Award Information
Agency: Department of Defense
Branch: Army
Contract: W912HZ-07-P-0288
Agency Tracking Number: A074-026-0191
Amount: $99,963.00
Phase: Phase I
Program: STTR
Awards Year: 2007
Solicitation Year: 2007
Solicitation Topic Code: A07-T026
Solicitation Number: N/A
Small Business Information
QUANTUM SIGNAL, LLC
3741 Plaza Drive, Suite 1, Ann Arbor, MI, 48108
DUNS: 119094493
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mitchell Rohde
 COO
 (734) 994-0028
 rohde@quantumsignal.com
Business Contact
 Mitchell Rohde
Title: COO
Phone: (734) 994-0028
Email: 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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