SBIR-STTR Award

Artificial Intelligence/Machine Learning to Improve Maneuver of Robotic/Autonomous Systems
Award last edited on: 9/2/2020

Sponsored Program
STTR
Awarding Agency
DOD : Army
Total Award Amount
$1,148,142
Award Phase
2
Solicitation Topic Code
A17A-T019
Principal Investigator
Jeffrey L Ferrin

Company Information

Autonomous Solutions Inc (AKA: ASI)

990 North 8000 West
Petersboro, UT 84325
   (866) 881-2171
   info@asirobots.com
   www.asirobots.com

Research Institution

Georgia Institute of Technology

Phase I

Contract Number: W56KGU-17-C-0066
Start Date: 9/25/2017    Completed: 2/24/2018
Phase I year
2017
Phase I Amount
$149,185
Robotic autonomous systems (RAS) are currently being used for many different applications using a wide variety of vehicle platforms. The environments in which RAS are being used are becoming increasingly complex. Vehicle path planning and control is challenging in environments with many obstacles and uneven terrain. This proposed research will develop and compare multiple techniques to improve vehicle response through difficult terrain. The current state-of-the-art will be researched to find methods that can be used to improve vehicle maneuverability in difficult terrain. Two other methods will be compared. These methods are nonlinear model predictive control (NMPC) and a machine learning method associated with trajectory generation. This work will compare these methods in simulation and then implement and test the best method on a fully automated Ford Escape.

Phase II

Contract Number: W56KGU-20-C-0003
Start Date: 11/21/2019    Completed: 11/20/2021
Phase II year
2020
Phase II Amount
$998,957
Autonomous vehicles are being used for many applications varying from small indoor robots, to large-scale mining trucks, and military vehicles operating in fast-changing environments. Global position systems (GPS) have traditionally been used for vehicle positioning. GPS works well in open environments, but suffers from significant degradation due to terrain, certain operating conditions, and even interference and jamming. Vehicles operating in high-risk scenarios, whether the battlefield or a time-sensitive mine operation or farming harvest, need to always know where they are in order to properly perform path control, particularly in dynamic environments. In these cases, GPS alone will not suffice for vehicle state estimation. Under the previous Phase I contract, Autonomous Solutions, Inc. (ASI), in conjunction with the Georgia Institute of Technology, have developed both GPS-denied and high-speed dynamic control algorithms to mitigate the effects of degrading GPS and to improve vehicle control, respectively. The technology demonstrated shows promise in its usefulness to solve many problematic situations currently expressed by ASI customers in non-ideal environments. The continued development of this GPS-denied work will prove beneficial to ASI customers operating in deep open-pit or underground mines, orchards with impenetrable canopies, and yet others operating at high-latitudes where GPS is unreliable.