Education:

University of Michigan
PhD Computer Science and Engineering ’99
MS Computer Science and Engineering ’95

Massachusetts Institute of Technology
MS Aeronautics and Astronautics ’90
BS Aeronautics and Astronautics ’88

Teaching: ^top

AERO 552 Aerospace Information Systems
AERO 450 Flight Software Systems
AERO 201 Introduction to Aerospace Engineering
ROB 550 Robotic Systems Laboratory
AERO 740 (now 552) Aerospace Information Systems
ENGR 151 Accelerated Intro. to Computers & Programming (new)
ENGR 101 Intro. to Computers & Programming

Research Interests: ^top

Atkins is motivated by the needs of the Aerospace application, and so are many students. As engineers we are looking to make the world a better place. For aviation, she pursues autonomy research to improve safety of flight and enable new missions. For space, she is interested in augmenting onboard decision systems and supporting closer astronaut-robot collaboration. Increasingly autonomous systems for any vehicle must be resilient to failures and must continue to make rational decisions in the presence of unexpected or anomalous events. These challenge problems require advances in sensing and decision-making. Cloud-based data sources can be fused with real-time sensor feedback to better inform decision systems.

Atkins overall research goal is to identify, adapt, and advance an appropriate set of models and algorithms from the control systems and computer science communities to best solve key Aerospace research challenges. Her PhD research revealed challenges and opportunities in defining the best representation or abstraction for decision-making, particularly when the set of features and values might be incomplete or incorrect. Many presume “state” is fully-defined by a vector of real numbers, yet human cognition is based on symbols that translate to objects, actions, and measures or attributes of each object or action. Dynamics and control system researchers have developed capable and mathematically-correct motion planning, guidance, navigation, and feedback control techniques. A central challenge in application-driven autonomy research is when and how to apply existing techniques versus defining a new abstraction or new algorithm that might be a more effective strategy.

Section “c” of Atkins’ CV describes her past, current, and proposed research projects. These projects span a variety of fundamental and application topics, but they all involve “systems” problems best solved with multidisciplinary models and methods. Atkins’ first project in emergency landing planning relied on an established geometric path construction method, the Dubins path, to connect an initial aircraft state with a landing runway given a no-thrust (gliding) failure case. Perhaps the most important contribution of this work was not in path construction but instead in landing site selection. The simple multi-objective cost function combining common-sense utility terms didn’t receive too much attention, yet every pilot informed of this approach has agreed that inclusion of the more “practical” utility terms beyond time and energy use is important (and novel). This initial research has led to a long series of “emergency flight planning” studies, many in collaboration with researchers who provide essential adaptive control and system identification capabilities underlying the landing site selection and emergency landing planning layer on which my work has focused. The more recent extension to flight safety assessment and management (FSAM) addresses a long-standing challenge of how automation and crew can monitor and serve as safety backups to each other. While neither the deterministic (timed automaton) nor stochastic (Markov Decision Process) modeling constructs is fundamentally novel, perhaps the most important research contributions of this work are in abstracting the state space to forms that efficiently capture the decision space and that can be explained and understood by human operators and air traffic controllers.

Atkins’ research in cyberphysical systems (CPS) has resulted in two important research contributions, discussed in the context of collaborations and supported students in her CV. Both the “co-regulation” and “co-optimization” concepts show promise for offering a new dimension in multidisciplinary optimization. As emerging small UAS, CubeSats, and a variety of other small robotic systems become prevalent in research and commercial applications, we will see an increasing number of cases where computing, communication, and physical sensing and actuation systems must negotiate resource sharing in real-time rather than assuming one subsystem (physical or cyber) dominates.

As increasingly autonomous systems are proposed, Atkins envisions a wealth of new opportunities to inform and exploit cloud-based data, real-time perceptions, and appropriate model abstractions to make optimal decisions for long-duration autonomy and for collaborative human-machine systems. Autonomy is great to study, but an autonomous system is useless unless it ultimately accomplishes a mission that we, the humans, want to accomplish. We must harness the power of increasingly autonomous systems to educate and improve quality of life for people worldwide, not fall into a trap where the next generation grows dependent on autonomous systems without gaining a new evolutionary advantage.

Biography: ^top

Dr. Ella Atkins is a Professor in the University of Michigan’s Aerospace Engineering Department where she directs the Autonomous Aerospace Systems (A2SYS) Lab and is Associate Director of the Robotics Institute. Dr. Atkins holds B.S. and M.S. degrees in Aeronautics and Astronautics from MIT and M.S. and Ph.D. degrees in Computer Science and Engineering from the University of Michigan. She is an AIAA Fellow, private pilot, and Part 107 UAS pilot. She served on the National Academy’s Aeronautics and Space Engineering Board and the Institute for Defense Analysis Defense Science Studies Group. She has served on several National Academy study committees and co-authored study reports including Advancing Aerial Mobility A National Blueprint (2020) and Autonomy Research for Civil Aviation Toward a New Era of Flight (2014). Dr. Atkins has built a research program in decision-making and control to assure safe contingency management in manned and unmanned Aerospace applications. She is currently Editor-in-Chief of AIAA Journal of Aerospace Information Systems (JAIS) and a member of the 2020-2021 AIAA Aviation Conference Executive Steering Committee.

Awards: ^top

Honors and Awards

• Robotics Leadership Award, 2020
• Fellow, American Institute of Aeronautics & Astronautics (AIAA), 2019
• Inaugural President’s Award for National and State Leadership, University of Michigan, 2017
• Trudy Huebner Service Excellence Award, University of Michigan, 2013.
• Aerospace Engineering Department Award, University of Michigan, 2009.
• NSF CAREER Award, 2004-2009.
• Associate Fellow, American Institute of Aeronautics & Astronautics (AIAA).
• Aerospace Engineering Dept. Faculty Mentor Award, Univ. of Maryland, 2004.
• Sloan Foundation Pre-Tenure Leave Fellowship, 2002-2003.
• GE Pre-Doctoral Fellowship, University of Michigan, 1997-1998.
• Orenstein Fellowship, EECS Department, University of Michigan, 1993-1994.
• Sigma Gamma Tau Aerospace Engineering Honor Society, inducted 1987.
• Tau Beta Pi Engineering Honor Society, inducted 1986.
• America’s Junior Miss Scholastic Achievement Award, 1984.
• National Merit Scholar, 1984.

SOCIETY MEMBERSHIPS

• Member of AIAA, Associate Fellow (lifetime member)
• Senior Member of IEEE (current)
• Associate Editor, AIAA Journal of Aerospace Information Systems (JAIS) (current)
• Member of the AIAA Intelligent Systems Technical Committee (ISTC), past chair  (current member)
• Member of the AIAA Software Technical Committee  (current member)
• Member of the Academy of Model Aeronautics (AMA) (1997 – present)
• Member of the Aircraft Owner’s and Pilots Association (AOPA) (1993 – present)
• Member of the National Reseach Council (NRC) Aeronautics and Space Engineering Board (ASEB) (2011-2015)
• Member of the Defense Science Study Group (DSSG) (2012-2013)

Publications: ^top

Full List of Publications

1. J. Castagno and E. Atkins, “Polylidar – Polygons From Triangular Meshes,” in IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4634-4641, July 2020, doi: 10.1109/LRA.2020.3002212.
2. J. Castagno and E. Atkins, “Roof Shape Classification from LiDAR and Satellite Image Data Fusion using Supervised Learning,” Sensors, MDPI, Vol. 18, No. 11, Nov. 2018, doi: 10.3390/s18113960.
3. Y. Yao and E. Atkins, “The Smart Black Box: A Value-Driven High-Bandwidth Automotive Event Data Recorder,” IEEE Transactions on Intelligent Transportation Systems, February 2020, doi: 10.1109/TITS.2020.2971385.
4. M. Stevens and E. Atkins, “Generating Airspace Geofence Boundary Layers in Wind,” Journal of Aerospace Information Systems, AIAA, Vol. 17, No., 2, February 2020, doi: 10.2514/1.I010792.
5. M. Stevens, H. Rastgoftar, and E. Atkins, “Geofence Boundary Violation Detection in 3D Using Triangle Weight Characterization with Adjacency,” Journal of Intelligent and Robotic Systems, Springer, September 2018, doi: 10.1007/s10846-018-0930-5.
6. H. Rastgoftar and E. Atkins, “Physics-Based Freely Scalable Continuum Deformation for UAS Traffic Coordination,” Transactions on Control of Network Systems (TCNS), IEEE, accepted (November 2019), doi: 10.1109/TCNS.2019.2954521.
7. P. Sharma and E. Atkins, “An Experimental Investigation of Tractor and Pusher Hexacopter Performance,” Journal of Aircraft, AIAA, Vol. 56, No. 5, pp. 1920-1934, Sept. 2019, doi: 10.2514/1.C035319.
8. P. Di Donato and E. Atkins, “Evaluating Risk to People and Property for Aircraft Emergency Landing Planning,” Journal of Aerospace Information Systems, AIAA, Vol. 14, No. 5, pp. 259-278, 2017, doi: 10.2514/1.I010513
9. S. Balachandran and E. Atkins, “A Markov Decision Process Framework for Flight Safety Assessment and Management,” Journal of Guidance, Control, and Dynamics, AIAA, Vol. 40, No. 4, Special Issue on Aircraft Loss of Control, pp. 817-830, 2017, doi: 10.2514/1.G001743.
10. P. Di Donato, S. Balachandran, K. McDonough, E. Atkins, and I. Kolmanovsky, “Envelope Aware Flight Management for Loss of Control Prevention given Rudder Jam,” Journal of Guidance, Control, and Dynamics, AIAA, Vol. 40, Special Issue on Aircraft Loss of Control, pp. 1027-1041, 2017, doi: 10.2514/1.G000252.
11. Z. Li, I. Kolmanovsky, E. Atkins, J. Lu, D. Filev, and Y. Bai, “Road Disturbance Estimation and Cloud-Aided Comfort-Based Route Planning,” Transactions on Cybernetics, IEEE, Vol. 47, No. 11, pp. 3879-3891, Nov. 2017, doi: 10.1109/TCYB.2016.2587673.
12. R. Eubank, J. Bradley, and E. Atkins, “Energy-Aware Multiflight Planning for an Unattended Seaplane: Flying Fish,” Journal of Aerospace Information Systems, AIAA, Vol. 14, No. 2, pp. 73-91, 2017, doi: 10.2514/1.I010484.
13. A. Ten Harmsel, I. Olson, and E. Atkins, “Emergency Flight Planning for an Energy-Constrained Multicopter,” Journal of Intelligent and Robotic Systems, Springer, Springer, Vol. 85, Issue 1, pp. 145-165, Jan. 2017, doi: 10.1007/s10846-016-0370-z.
14. J. Bradley and E. Atkins, “Toward Continuous State-Space Regulation of Coupled Cyber and Physical Systems,” Proceedings of the IEEE, IEEE, Vol. 100, No. 1, pp. 60-74, January 2012, doi: 10.1109/JPROC.2011.2161239.
15. D. Yeo, E. Atkins, L. Bernal, and W. Shyy, “Fixed-Wing Unmanned Aircraft In-Flight Pitch and Yaw Control Moment Sensing,” Journal of Aircraft, AIAA, Vol. 52, No. 2, pp. 403-420, March 2015, doi: 10.2514/1.C032682.
16. E. Atkins, “Education in the Crosscutting Sciences of Aerospace and Computing,” Journal of Aerospace Information Systems, AIAA, Vol. 11, No. 10, pp. 726-737, October 2014, doi: 10.2514/1.I010193.
17. J. Richardson, P. Kabamba, E. Atkins, A. Girard, “Safety Margins for Flight Through Stochastic Gusts,” Journal of Guidance, Control, and Dynamics,>/em> AIAA, Vol. 37, No. 6, pp. 2026-2030, June 2014, doi: 10.2514/1.G000299.
18. J. Broderick, D. Tilbury, and E. Atkins, “Optimal Coverage Trajectories for a UGV with Tradeoffs for Energy and Time,” Autonomous Robots, Springer, Vol. 36, No. 3, pp. 257-271, March 2014, doi: 10.1007/s10514-013-9348-x.
19. W. Ren, R. Beard, and E. Atkins, “Information Consensus in Multivehicle Cooperative Control” Control Systems Magazine, IEEE, Vol. 27, No. 2, April 2007, pp. 71-82, doi: 10.1109/MCS.2007.338264.
20. E. Atkins and M. Xue, “Noise Sensitive Final Approach Trajectory Optimization for Runway-Independent Aircraft,” Journal of Aerospace Computing, Information, and Communication, AIAA, Vol. 1, No. 7, pp. 269-287, July 2004, doi: 10.2514/1.3924.