Research

Our research is focused on bridging the scientific gaps between traditional computer science topics and aerospace engineering topics, while achieving a high degree of closure between theory and experiment. We focus on machine learning and multi-agent systems, intelligent autonomous control, nonlinear control theory, vision based navigation systems, fault tolerant adaptive control, and cockpit systems and displays. What sets our work apart is a unique systems approach and an ability to seamlessly integrate different disciplines such as dynamics & control, artificial intelligence, and bio-inspiration. Our body of work integrates these disciplines, creating a lasting impact on technical communities from smart materials to General Aviation flight safety to Unmanned Air Systems (UAS) to guidance, navigation & control theory. Our research has been funded by AFOSR, ARO, ONR, AFRL, ARL, AFC, NSF, NASA, FAA, and industry.

Autonomy

Intelligent and Safe Technologies for Enhanced UAS Autonomous Air Refueling Operations, Phase I

Air Force Research Laboratory through sub-contract with Barron Associates

1 September 2017 – 30 April 2018

Total award $49,982

Unmanned Aircraft System (UAS) platforms provide many important military roles that require long periods of time aloft. Repeated returns to base for refueling is one scenario that can severely degrade mission operations. There is a critical need to develop autonomous aerial refueling (AAR) capabilities in which both the tanker and receiver aircraft are unmanned. One of the challenges in AAR is minimum airspeed. For this effort the focus will be Groups 4 and 5 UASs with maximum airspeeds of 130 KCAS. The main objective is to radically increase mission length and on-station availability of UAS platforms by developing the capability to reliably conduct (AAR) of Groups 4 and 5 UASs with calibrated airspeeds of 130 KCAS or less.

Additional key technical challenges associated with AAR of UAS are:

The refueling procedure will require the UAS to operate in close proximity of the tanker aircraft. Relative location must be known with a high level of accuracy, employing collision avoidance procedures.
One or both the tanker and UAS must respond quickly if an unsafe refueling condition occurs.
AAR solutions should minimize modifications to both the tanker and the UAS due to cost, maintenance and SWaP considerations.
The refueling system must operate under broad weather and day and night conditions.

Working with me on this project are:

Graduate Students:

-Zeke Bowden, MENG AERO

Prototyping Levels of Automation For Crew Exploration Vehicle (CEV)

Rendezvous and Proximity Operations Tasks

Collaborative Effort with GN&C Autonomous Flight Systems Office, NASA Johnson Space Center
25 August 2005 – present
Collaborator: Howard Hu

A critical component in the CEV development is the delineation of decision-making authority between humans/computers (automation) and ground/onboard (autonomy). By finding the right balance of automation and autonomy, NASA can vastly improve the probability of mission success, increase safety, and decrease overall cost. To identify the appropriate levels of automation and autonomy to design into a human space flight vehicle NASA has created a method called the Function-specific Level of Autonomy and Automation Tool (FLOAAT).

Function-specific Level of Autonomy and Automation Tool Scales

The purpose of this research is to prototype a sub-set of the Rendezvous and Prox Ops functions at the levels of automation specified using FLOAAT. By prototyping at these levels the accuracy of the FLOAAT outputs can be judged. The research will also be used to apply modern decision-making algorithms to help improve the efficiency, safety, and quality in the execution of selected Rendezvous and Prox Ops planning tasks. This research will deal only with the breakdown of human versus computer responsibility (automation) and therefore will not address the issue of ground versus on-board responsibility (autonomy). The issue of autonomy, although important, is difficult to prototype until a more detailed design of the ground control architecture and on-board computing and display capabilities is conducted.

Specific tasks and research objectives:

Prototype selected Rendezvous and/or Prox Ops functions at the levels of automation determined by the Function-specific Level of Autonomy and Automation Tool (FLOAAT) process
Evaluate the prototype versions by comparing to Shuttle/ISS implementations of the same functions
Use this comparison to evaluate the quality of the FLOAAT recommended level of automation
Evaluate the selected decision-making algorithm

A final evaluation will be made to determine if the level of automation was appropriate for each prototyped function and provide suggestions for improvement. This includes an evaluation of the prototyping process, AI techniques used, and the effectiveness of operating at the levels of automation specified by the FLOAAT process. The successfulness of the prototyping effort will be used to gauge the accuracy of the FLOAAT tool to select appropriate levels of automation. It will also determine the applicability of the selected decision-making algorithms for use in human spaceflight.

Working with me on this program is Graduate Research Assistant:

Jeremy Hart

Real Time Adaptive Navigation and Control of Highly Nonlinear Autonomous Systems

United States Navy Office of Naval Research
1 July 1997 – 30 June 2000
Co-P.I.’s John L. Junkins and Donald T. Ward
Total award $563,649

The goal of this program is to investigate novel and highly advanced technologies which will enable autonomous systems with high levels of uncertainty in the presence of noise and unbounded disturbances to achieve breakthrough combat capabilities in future high threat environments. Specific enabling technologies being researched by the collective Texas A&M team on this program include Shape Memory Alloy (SMA) control effector actuation, vision based automatic landing systems, and intelligent autonomous flight controllers. The broad class of system includes autonomous underwater vehicles, robotic land vehicles, and Unmanned Combat Aerial Vehicles (UCAV) such as the type pictured above, which is the vehicle type for this research.

Specific topic areas being researched include:

Robust nonlinear adaptive control.
Online, real-time, nonlinear system identification in the presence of noise.
Intelligent flight directors.
Extremal mapping.
Fighter agility metrics for UCAV’s.

Working with me on this program are Graduate Research Assistants:

Wei Chen
Praveen Joshi
David M. Smith