Publications

Krpec and Valasek Publish “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems

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VSCL Alumni Blake Krpec, Dr. John Valasek, and Dr. Stephen Nogar with the DEVCOM Army Research Lab published the paper “Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle” in Journal of Aerospace Information Systems.

Current autonomous unmanned aerial systems (UAS) commonly use vision-based landing solutions that depend upon fiducial markers to localize a static or mobile landing target relative to the UAS. This paper develops and demonstrates an alternative method to fiducial markers with a combination of neural network-based object detection and camera intrinsic properties to localize an unmanned ground vehicle (UGV) and enable autonomous landing. Implementing this visual approach is challenging given the limited compute power on board the UAS, but is relevant for autonomous landings on targets for which affixing a fiducial marker a priori is not possible, or not practical. The position estimate of the UGV is used to formulate a landing trajectory that is then input to the flight controller. Algorithms are tailored towards low size, weight, and power constraints as all compute and sensing components weigh less than 100 g. Landings were successfully demonstrated in both simulation and experimentally on a UGV traveling in both a straight line and while turning. Simulation landings were successful at UGV speeds of up to 3.0 m/s, and experimental landings at speeds up to 1.0 m/s.

 

McQuinn presents at IEEE Aerospace Conference in Big Sky, Montana

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VSCL graduate student Cassie-Kay McQuinn presented “Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering” at the 2024 IEEE Aerospace Conference this month. This work was completed as part of her internship with AFRL in summer 2023.

A fundamental capability for On-orbit Servicing, Assembly, and Manufacturing (OSAM) is inspection of the vehicle to be serviced, or the structure being assembled. The focus of this research is developing Active-Set Invariance Filtering (ASIF) Run Time Assurance (RTA) filters that monitor system behavior and the output of the primary controller to enforce attitude requirements pertinent for autonomous space operations. Slack variables are introduced into the ASIF controller to prioritize safety constraints when a solution to all safety constraints is infeasible. Monte Carlo simulation results as well as plots of example cases are shown and evaluated for a three degree of freedom spacecraft with reaction wheel attitude control. A preprint of the paper is available at: https://arxiv.org/abs/2402.14723

Lehman, Eves, and Valasek Papers Accepted to 2024 AIAA SciTech Forum and Exposition, Orlando, FL, January, 2024

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VSCL Ph.D. student Hannah Lehman, former Ph.D. student Kameron Eves, and VSCL Director John Valasek have had papers accepted to the 2024 AIAA SciTech Forum and Exposition, Orlando, FL, January, 2024.

Machine Learning Across Different Levels of Auction Based Coordination Hierarchies (Lehman and Valasek)

Machine learning has long been discussed as a candidate for facilitating autonomous multiagent vehicle coordination. Many methods of autonomous multiagent coordination have been proposed, however few if any solutions take into account realistic communication challenges. By using machine learning on multiple levels, and a self organizing hierarchical system, an autonomous, pseudo decentralized, heterogeneous, system can dynamically complete tasks without being fully connected. This method will be investigated and demonstrated on a simple, proof of concept rotorcraft simulation.

This publication is part of VSCL’s ongoing work in the area of Tightly Integrated Navigation and Guidance for Multiple Autonomous Agents https://vscl.tamu.edu/research/tightly-integrated-navigation-and-guidance-for-multiple-autonomous-agents-2/

Inlet Unstart Prevention by Adaptive Regulation Using a Nonlinear Longitudinal Timescale Model (Eves and Valasek)

Inlet unstart on hypersonic aircraft causes a rapid and dangerous loss of thrust. Fortunately, proper control design can help prevent inlet unstart. This paper demonstrates how [K]control of Adaptive Multiple timescale Systems (KAMS) can effectively address this challenging problem. First, a multiple-timescale model of a hypersonic aircraft is developed to facilitate the control law design. Then, a KAMS controller is designed using Adaptive Nonlinear Dynamic Inversion to stabilize the reduced subsystems and Sequential Control is used to fuse the control signals for reduced subsystems. The closed-loop system is proven to be stable despite weak non-minimum phase effects. KAMS provides stability guarantees that are more rigorous than prior work and also provides insights into the system’s underlying physics. Numerical results presented in the paper show that KAMS can effectively prevent inlet unstart and mitigate uncertainty using angle-of-attack regulation.

This publication is part of VSCL’s ongoing work in the area of nonlinear multiple time-scale control https://vscl.tamu.edu/research/novel-multiple-time-scale-adaptive-control-for-uncertain-nonlinear-dynamical-systems/

Eves and Valasek Publish “Slow Timescale Adaptive Control for Multiple-Timescale Systems,” in Journal of Guidance, Control, and Dynamics

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Ph.D. student Kameron Eves and Dr. John Valasek of VSCL published the paper “Slow Timescale Adaptive Control for Multiple-Timescale Systems,” in Journal of Guidance, Control, and Dynamics.

Multiple-timescale systems are a noteworthy class of dynamical systems that can be modeled with singularly perturbed differential equations. Adaptive control has not been studied in the context of singularly perturbed plants. This paper introduces and evaluates three methods of adaptive control for multiple-timescale systems. Each method is a framework that is valid for a wide class of adaptive control methods. Full-Order Adaptive Control (FOAC) applies adaptive control to the system as a whole.  It is straightforward but can be sensitive to timescale effects.  Reduced-Order Adaptive Control (ROAC) applies adaptive control to either the fast or slow modes only. This simplifies synthesis but can also constrain the range of valid timescale separation. [K]Control of Adaptive Multiple-timescale Systems (KAMS) fuses two adaptive control signals using multiple-timescale techniques.  KAMS takes advantage of model reduction unlike FOAC, and allows for unstable fast dynamics unlike ROAC. Generalized formal definitions, stability criteria, and examples are developed and presented for each method.  Results presented in the paper for the control of a Boeing 747-100/200 on approach show that [K]Control of Adaptive Multiple-timescale Systems has a desirable blend of performance and robustness because each reduced-order model is stabilized separately.

This publication is part of VSCL’s ongoing work in the area of nonlinear multiple time-scale control.  The early access version of the article can be viewed at https://arc.aiaa.org/doi/full/10.2514/1.G007439

VSCL Student Presents at Interactive Learning with Implicit Human Feedback Workshop at 2023 International Conference on Machine Learning (ICML)

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VSCL graduate student M.D. Sunbeam will present a workshop paper on 29 July at the 2023 International Conference on Machine Learning (ICML) in Honolulu, Hawaii.

Sunbeam will be presenting the paper “Imitation Learning with Human Eye Gaze via Multi-Objective Prediction,”. Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning work utilizes only behavioral information from the demonstrator, i.e. which actions were taken, and ignores other useful information. In particular, eye gaze information can give valuable insight towards where the demonstrator is allocating visual attention, and holds the potential to improve agent performance and generalization. In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.

We apply GRIL to a visual navigation task, in which an unmanned quadrotor is trained to search for and navigate to a target vehicle in a photorealistic simulated environment. We show that GRIL outperforms several state-of-the-art gaze-based imitation learning algorithms, simultaneously learns to predict human visual attention, and generalizes to scenarios not present in the training data. Supplemental videos can be found at https://sites.google.com/view/gaze-regularized-il/, and code will be made available.

Jares and Valasek Publish “Control Acquisition Attack of Aerospace Systems via False Data Injection,” in Journal of Aerospace Information Systems

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Ph.D. student Garrett Jares and Dr. John Valasek of VSCL published the paper “Control Acquisition Attack of Aerospace Systems via False Data Injection,” in Journal of Aerospace Information Systems.

The cyber threat to aerospace systems has been growing rapidly in recent years with several real-world and experimental cyberattacks observed. This growing threat has prompted investigation of cyber-attack and defense strategies for manned and unmanned air systems, spacecraft, and other aerospace systems. The work in this paper seeks to further understand these attacks by introducing and developing a novel cyberattack for autonomous aerospace systems. The problem faced by the attacker is posed and discussed analytically using false data injection of state measurements to exploit the vehicle’s onboard controller to take control of the system. It is shown that the attacker can utilize traditional control techniques to exert control over the system and eliminate the control of the victim by intercepting and modifying the vehicle’s measurement data. The attacker is able to accomplish this objective without any prior knowledge of the system’s plant, controller, or reference signal. The attack is demonstrated on the elevator-to-pitch-attitude-angle dynamics of a Cessna T-37 aircraft model. It is shown to be successful in eliminating the victim’s control influence over the system and driving the system to its own target state.

This publication is part of VSCL’s ongoing work in the area of cybersecurity. The article can be viewed at https://arc.aiaa.org/doi/full/10.2514/1.I011199.

Leshikar, McQuinn, and Valasek Publish Invited Paper “System Identification of Unmanned Air Systems at Texas A&M University,” in Journal of Aircraft

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Ph.D. student Christopher Leshikar, M.S. student Cassie McQuinn, and Dr. John Valasek of VSCL published the invited paper “System Identification of Unmanned Air Systems at Texas A&M University,” in Journal of Aircraft.

This paper presents a summary of system identification flight testing and results for a variety of large and small fixed-wing and multirotor Unmanned Air Systems at Texas A&M University from 1999-2023. The six different types of vehicles range from a large powered-parafoil, to a fixed-wing vehicle with synthetic jet actuated roll control effectors, to a radially asymmetric multirotor, to large and small fixed-wing vehicles, and a Steppe eagle. The Observer/Kalman Filter Identification algorithm is used to generate linear time invariant state-space models, and results for both near real-time online model generation, and post-flight offline model generation are presented. The use and efficacy of a variety of test input types and their sensitivity to exogenous inputs such as turbulence, in addition to identified model evaluation and selection criteria are discussed. Several generations of low size, weight, power, and cost flight test instrumentation including the Developmental Flight Test Instrumentation data acquisition package are also presented. Challenges that arose from the flight testing campaigns along with solutions are highlighted in the paper.

This publication is part of VSCL’s ongoing work in the area of system identification. The early access version of the article can be viewed at https://doi.org/10.2514/1.C037314.