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.

Autonomous and Nonlinear Control of Cyber-Physical Air, Space and Ground Systems

Vision Based Sensors and Navigation Systems

Cybersecurity for Air and Space Vehicles

Air Vehicle Control and Management

Space Vehicle Control and Management

Advanced Cockpit/UAS Systems and Displays

Control of Bio-Nano Materials and Structures

Crew Exploration Vehicle

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.

Function Level Ti Ellipse

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