About

John-Paul Clarke is a professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin, where he holds the Ernest Cockrell, Jr. Memorial Chair in Engineering. His research focuses on controls, autonomy, and robotics, with particular interests in the environmental impact of aviation, trajectory prediction and optimization, stochastic models and optimization algorithms, and increasingly autonomous aircraft-enabled mobility. Clarke is a leading expert in aircraft trajectory prediction and optimization, especially in developing flight procedures that reduce environmental impact, and in improving the efficiency and robustness of aircraft, airline, airport, and air traffic operations through stochastic modeling. Prior to his current position, Clarke was a faculty member at Georgia Tech, served as Vice President of Strategic Technologies at United Technologies Corporation (now Raytheon), and held faculty positions at MIT. He has also worked as a researcher at Boeing and NASA JPL and co-founded multiple companies, most recently Universal Hydrogen, which aims to develop a comprehensive carbon-free aviation solution. Clarke has contributed significantly to aerospace through his leadership roles, including founding chair of the AIAA Human-Machine Teaming Technical Committee, co-chair of the National Academies Committee on Autonomy Research for Civil Aviation, and advisory roles with organizations such as the FAA, ICAO, NASA, and the US Department of Transportation. He earned his degrees in aeronautics and astronautics from MIT and has received numerous honors, including the National Academy of Engineering Gilbreth Lectureship, the FAA Excellence in Aviation Award, and fellowships in the AIAA and the Royal Aeronautical Society.

Research topics

• Computer Science
• Mathematics
• Algorithm
• Mathematical optimization
• Computer Security
• Artificial Intelligence
• Engineering
• Computer network
• Aeronautics
• Aerospace engineering

Selected publications

• Developing 3D risk-informed no-fly zones for urban UAS operations

  Aerospace Science and Technology · 2025-05-09 · 4 citations

  article
  PublisherDOI

• Cluster & Disperse: a general air conflict resolution heuristic using unsupervised learning

  arXiv (Cornell University) · 2025-01-08

  preprintOpen accessSenior author

  We provide a general and malleable heuristic for the air conflict resolution problem. This heuristic is based on a new neighborhood structure for searching the solution space of trajectories and flight-levels. Using unsupervised learning, the core idea of our heuristic is to cluster the conflict points and disperse them in various flight levels. Our first algorithm is called Cluster & Disperse and in each iteration it assigns the most problematic flights in each cluster to another flight-level. In effect, we shuffle them between the flight-levels until we achieve a well-balanced configuration. The Cluster & Disperse algorithm then uses any horizontal plane conflict resolution algorithm as a subroutine to solve these well-balanced instances. Nevertheless, we develop a novel algorithm for the horizontal plane based on a similar idea. That is we cluster and disperse the conflict points spatially in the same flight level using the gradient descent and a social force. We use a novel maneuver making flights travel on an arc instead of a straight path which is based on the aviation routine of the Radius to Fix legs. Our algorithms can handle a high density of flights within a reasonable computation time. We put their performance in context with some notable algorithms from the literature. Being a general framework, a particular strength of the Cluster & Disperse is its malleability in allowing various constraints regarding the aircraft or the environment to be integrated with ease. This is in contrast to the models for instance based on mixed integer programming.

  PublisherOA PDFDOI

• Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems

  ArXiv.org · 2025-05-31

  preprintOpen access

  We present an adaptive control scheme to enable the emergence of order within distributed, autonomous multi-agent systems. Past studies showed that under high-density conditions, order generated from traffic-following behavior reduces travel times, while under low densities, choosing direct paths is more beneficial. In this paper, we leveraged those findings to allow aircraft to independently and dynamically adjust their degree of traffic-following behavior based on the current state of the airspace. This enables aircraft to follow other traffic only when beneficial. Quantitative analyses revealed that dynamic traffic-following behavior results in lower aircraft travel times at the cost of minimal levels of additional disorder to the airspace. The sensitivity of these benefits to temporal and spatial horizons was also investigated. Overall, this work highlights the benefits, and potential necessity, of incorporating self-organizing behavior in making distributed, autonomous multi-agent systems scalable.

  PublisherOA PDFDOI

• Analyzing Lunar Imagery Using Computer Vision Models to Improve Path Planning

  2025-07-16 · 1 citations

  article

  Accurate global path planning is critical to the success of lunar rover missions, yet current approaches are fundamentally constrained by uncertainty in remote sensing data. Orbital imagery provides only partial and low-resolution information about surface hazards, rendering deterministic planning strategies brittle to unexpected terrain conditions. To address this challenge, we propose a method for generating probabilistic terrain models suitable for stochastic optimization. A convolutional neural network classifier is trained on lunar imagery to predict traversability distributions across surface regions, enabling the construction of uncertainty-aware cost maps. Testing shows that the model performs well, significantly surpassing traditional image processing techniques and human performance at the classification task. To better capture realistic terrain variability, spatial correlations between adjacent regions are incorporated into the sampling process. These uncertainty-aware maps can be sampled to generate coherent terrain realizations, providing inputs to two-stage stochastic programming models for robust global path planning under uncertainty.

  PublisherDOI

• Benefits of Traffic Following in High-Density Autonomous Airspace Operations

  Journal of Air Transportation · 2025-05-26

  articleSenior author
  PublisherDOI

• The Reliability of Remotely Piloted Aircraft System Performance under Aeronautical Communication Uncertainties

  arXiv (Cornell University) · 2025-01-13

  preprintOpen accessSenior author

  Mission-critical operations of highly maneuverable Remotely Piloted Aircraft Systems (RPAS) require reliable communication to ensure safe integration into existing airspace. Understanding system-level performance under stochastic communication conditions is essential for estimating mission success and assessing operational risks. This study quantifies the impact of communication latency and complete signal loss on the mission completion performance of a highly maneuverable RPAS. The mission is defined as a static waypoint tracking task in three-dimensional airspace. We first derive mathematical formulations for key reliability metrics within the Required Communication Performance (RCP) framework. These stochastic communication factors, including latency and availability, are then incorporated into flight control simulations to evaluate system behavior. Extensive multiprocessing Monte Carlo simulations are conducted using high-performance computing to generate mission success rate and mission completion time envelopes. Results show significant degradation in flight performance as communication latency increases or availability decreases, which directly reduces the system stability margin. To better characterize this relationship, we introduce a new reliability metric, communicability, which integrates three key RCP metrics and provides insight into the maximum tolerable latency for flight control. The proposed framework informs RPAS design by revealing trade-offs between communication capability and flight control performance. The code used in this study is publicly available at this \href{https://github.com/YutianPangASU/comm-dynamics}{repository}.

  PublisherOA PDFDOI

• Alpha-Fair Routing in Urban Air Mobility with Risk-Aware Constraints

  2024-07-10 · 3 citations

  article

  In the vision of urban air mobility, air transport systems serve the demands of urban communities by routing flight traffic in networks formed by vertiports and flight corridors. We develop a routing algorithm to ensure that the air traffic flow fairly serves the demand of multiple commu-nities subject to stochastic network capacity constraints. This algorithm guarantees that the flight traffic volume allocated to different communities satisfies the alpha-fairness conditions, a commonly used family of fairness conditions in resource allocation. It further ensures robust satisfaction of stochastic network capacity constraints by bounding the coherent risk measures of capacity violation. We prove that implementing the proposed algorithm is equivalent to solving a convex optimization problem. We demonstrate the proposed algorithm using a case study based on the city of Austin. Compared with one that maximizes the total served demands, the proposed algorithm promotes even distributions of served demands for different communities.

  PublisherDOI

• Benefits of Traffic-Following in High-Density Autonomous Airspace Operations

  2024-07-27

  articleSenior author

  In this paper, we explore the dynamic emergence of traffic order within a distributed multi-agent system, focusing on minimizing inefficiencies stemming from unnecessary impositions of structural rules. We leverage a methodology for creating a dynamically updating traffic pattern map of the airspace. This map utilizes information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent may adjust the degree of traffic-following behavior it exhibits. We show that at low densities, traffic following behavior results in a decrease in entropy of the airspace with low penalties in terms of travel times. As the density of an airspace increases, substantial gains in both airspace entropy and travel times are seen as the degree of traffic following behavior increases. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent's traffic-following behavior based on the density of traffic within an airspace.

  PublisherDOI

• Optimal Airspeed Selection for Cruise and Descent Phases With Backward Time Boundary Propagation under Wind Uncertainty

  2024-07-27

  articleSenior author

  Flight planning in the presence of uncertain wind conditions poses considerable challenges in aviation. The influence of wind on aircraft trajectories is significant, and while wind information is sourced from hourly forecasts prior to flights,uncertainties and real time changes within forecasts disrupt both flight duration and fuel consumption predictions. To address this issue, we have introduced an airspeed selection method that considers new adaptations to wind conditions while placing emphasis on adhering to the Required Time of Arrival (RTA) and conserving fuel during the cruise and descent phases. This method involves dividing the flight route into segments and integrating Stochastic Programming (SP) with Receding Horizon Control (RHC). Additionally, for the SP, we apply a backward propagation time constraint to each segment to reduce computation time. This approach allows us to optimize the airspeed for each segment, ensuring that both fuel efficiency and RTA objectives are met. Numerical studies have yielded satisfactory outcomes, demonstrating enhanced fuel savings and improved RTA accuracy when this method is applied.

  PublisherDOI

• Active Risk Mitigation for Unmanned Aerial Systems

  Lecture notes in networks and systems · 2024-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• Éric Féron

  King Abdullah University of Science and Technology

  28 shared

• Liling Ren

  RTX (United States)

  13 shared

• Ioannis Anagnostakis

  American Institute of Aeronautics and Astronautics

  12 shared

• R. John Hansman

  Massachusetts Institute of Technology

  12 shared

• Adan Vela

  American Society For Engineering Education

  12 shared

• Zhenyu Gao

  10 shared

• Husni Idris

  10 shared

• Leihong Li

  Georgia Institute of Technology

  10 shared

Education

• Ph.D., Aeronautics and Astronautics

  Massachusetts Institute of Technology (MIT)

  1997

• Other, Aeronautics and Astronautics

  Massachusetts Institute of Technology (MIT)

  1992

• Other, Aeronautics and Astronautics

  Massachusetts Institute of Technology (MIT)

  1991

Awards & honors

• 1999 AIAA/AAAE/ACC Jay Hollingsworth Speas Airport Award
• 2003 FAA Excellence in Aviation Award
• 2006 National Academy of Engineering Gilbreth Lectureship
• 2012 AIAA/SAE William Littlewood Lectureship
• Elected to the National Academy of Engineering (2026)