The defense of civil and military airspace is increasingly challenged by low-cost, small Unmanned Aerial Vehicles (UAVs) with high maneuverability, enabling them to evade modern detection systems, disrupting the airspace, or even leading to a catastrophe. Contrary to existing counter UAS (cUAS) methods that focus on optimal sensor placement or control, this paper identifies vulnerabilities in existing sensor deployments by formulating the problem as a zero-sum differential surveillance-evasion game between an intruding UAV and the detection system. In this formulation, intruders seek paths that minimize detection probability while reaching their goal. Our focus is on identifying optimal intruder best response trajectories given the knowledge of the detection system to evaluate its performance and vulnerabilities. However, planning such trajectories is challenging due to the high dimensionality of the space-time domain and the non-convex visibility regions of panning sensors. To address this, we propose a hybrid approach that combines sampling-based planning with a nonlinear program (NLP) to obtain a locally optimal solution. The sampling solution provides an initial guess, thereby alleviating the computational burden of the NLP and refining the solution to obtain locally optimal paths. Finally, we use a Monte Carlo simulation to demonstrate that our proposed attacker's best response approach reduces the detection probability by 49.6% on average relative to purely sampling-based methods, with an additional average cost of 53.8 seconds per trial.

We present COVEE, a dataset for Cognitive mOdeling with Video, Electroencephalography, and Eye tracker. COVEE aims to be a valuable resource for advancing research on human cognition and the real-time estimation of cognitive load (CL) and situation awareness (SA) in complex operational environments. COVEE subsumes approximately 26 hours of multimodal data collected in carefully designed human subject experiments in realistic task settings, along with self-reported measures for CL and SA. In total, 23 human subjects performed multiple tasks with varying complexity in their roles as remote operators of unmanned aerial vehicles. To establish baselines, extensive experiments are conducted using various modern deep learning architectures and pre-trained large vision models. Insights gained from the experiments demonstrate the potential of COVEE for future research and real-world applications related to brain-computer interfaces, assistive technologies, or other forms of real-time behavioral intervention. Both raw and processed experimental data, along with all codes, are published for the highest reproducibility (https://github.com/Purdue-AIDA3/CM-COVEE).

We present a hierarchical safe auto-taxiing framework to enhance the automated ground operations of multiple unmanned aircraft systems (multi-UAS). The auto-taxiing problem becomes particularly challenging due to (i) unknown disturbances, such as crosswind affecting the aircraft dynamics, (ii) taxiway incursions due to unplanned obstacles, and (iii) spatiotemporal conflicts at the intersections between multiple entry points in the taxiway. To address these issues, we propose a hierarchical framework, i.e., SAFE-TAXI, combining centralized spatiotemporal planning with decentralized MPC-CBF-based control to safely navigate the aircraft through the taxiway while avoiding intersection conflicts and unplanned obstacles (e.g., other aircraft or ground vehicles). Our proposed framework decouples the auto-taxiing problem temporally into conflict resolution and motion planning, respectively. Conflict resolution is handled in a centralized manner by computing conflict-aware reference trajectories for each aircraft. In contrast, safety assurance from unplanned obstacles is handled by an MPC-CBF-based controller implemented in a decentralized manner. We demonstrate the effectiveness of our proposed framework through numerical simulations and experimentally validate it using Night Vapor, a small-scale fixed-wing test platform.

Most of the rigid-body systems which evolve on nonlinear Lie groups where Euclidean control designs lose geometric meaning. In this paper, we introduce a log-linear backstepping control law on SE2(3) that preserves full rotational-translational coupling. Leveraging a class of mixed-invariant system, which is a group-affine dynamic model, we derive exact logarithmic error dynamics that are linear in the Lie algebra. The closed-form expressions for the left- and right-Jacobian inverses of SE2(3) are expressed in the paper, which provides us the exact error dynamics without local approximations. A log-linear backstepping control design ensures exponential stability for our error dynamics; since our error dynamics is a block-triangular structure, this allows us to use Linear Matrix Inequality (LMI) formulation or H_∞ gain performance design. This work establishes the exact backstepping framework for a class of mixed-invariant system, providing a geometrically consistent foundation for future Unmanned Aerial Vehicle (UAV) and spacecraft control design.

The IEEE Drone Chase Challenge was held in 2022 and 2023 to foster development in Unmanned Aerial Systems and to provide a venue for collegiate students developing integrated UAS solutions in which to compete. The challenge is comprised of two stages: an online simulator-based stage and a physical in-person final. The development of each competitor’s unique solutions and difficulties faced by each finalist team are described herein. Improvements for other future competitions are suggested based on the experiences of the competitors and hosts from the 2023 IEEE Drone Chase Challenge. First, software integration and documentation must be complete and easy to follow for competitors, allowing them to focus on solution development, rather than troubleshooting errors. Second, scoring metrics must be designed to test for robustness to mitigate the effect of luck and other external conditions on the evaluation of a solution. Despite the current limitations realized during the competition, competitors, hosts, and the research community benefit from developing soft and technical skills through competition participation.

Coordination of multi-robot systems (MRSs) relies on efficient sensing and reliable communication among the robots. However, the sensors and communication channels of these robots are often vulnerable to cyberattacks and faults, which can disrupt their individual behavior and the overall objective of the MRS. In this work, we present a multi-robot integrity monitoring framework that utilizes inter-robot range measurements to (i) detect the presence of cyberattacks or faults affecting the MRS, (ii) identify the affected robot(s), and (iii) reconstruct the resulting localization error of these robot(s). The proposed iterative algorithm leverages sequential convex programming and alternating direction of multipliers method to enable real-time and distributed implementation. Our approach is validated using numerical simulations and demonstrated using PX4-SiTL in Gazebo on an MRS, where certain agents deviate from their desired position due to a GNSS spoofing attack. Furthermore, we demonstrate the scalability and interoperability of our algorithm through mixed-reality experiments by forming a heterogeneous MRS comprising real Crazyflie UAVs and virtual PX4-SiTL UAVs working in tandem.

A large class of nonlinear systems in engineering and robotics evolves on geometric manifolds, such as fixed-wing aircraft, quadrotors, etc., whose kinematic motion can be described using Lie groups, in particular SEn(3). The existing techniques involve numerical integration methods such as Runge-Kutta integration to propagate their motion forward in time. However, these methods are approximate solutions and do not respect the geometric constraints of the nonlinear differential equation, leading to higher computational costs and numerical integration errors. To this end, we propose a geometric closed-form approach to solve the initial value problem for a class of nonlinear systems that evolves on the SEn(3) Lie group. Through numerical simulations, we show that our closed-form solution is more efficient than the Runge-Kutta 4th-order (RK-4) integrator and reduces floating-point operations by 45% in a strapdown-inertial-navigation (SINS) case study. We believe that the efficiency and accuracy gains of our approach warrant the general adoption of this method for numerical integration in applications such as SINS.

Unmanned Aerial Vehicles (UAVs) have become a critical focus in robotics research, particularly in the development of autonomous navigation and target-tracking systems. This journal article provides an overview of a multi-year IEEE-hosted drone competition designed to advance UAV autonomy in complex environments. The competition consisted of two primary challenges. The first competition hosted was the Rover Chase Challenge. In this competition a UAV was tasked with autonomously tracking and following a ground rover as it maneuvers through an obstacle-filled environment. The drone relied on onboard sensors such as cameras and LiDAR to estimate the rover’s trajectory and adjust its flight path accordingly. The second competition hosted was the Maze Navigation Challenge. In this challenge, the UAV navigated through a structured maze using LiDAR-based environment mapping and obstacle avoidance, without relying on external positioning systems such as GPS. Developing robust autonomous drone algorithms for such tasks requires extensive data collection, simulation, and testing, which can be costly and time-intensive. To address this, competitors completed this competition using a PX4-Gazebo based simulator. This dataset includes sensor data recorded in rosbag format, comprising LiDAR, IMU, GPS, and other telemetry readings. This dataset enables researchers to benchmark algorithms, conduct reproducible experiments, and develop robust UAV autonomy, perception, and GPS-denied navigation systems in both simulated and real-world contexts.

Unmanned Aerial Vehicles (UAVs) have become a critical focus in robotics research, particularly in the development of autonomous navigation and target-tracking systems. This journal article provides an overview of a multi-year IEEE-hosted drone competition designed to advance UAV autonomy in complex environments. The competition consisted of two primary challenges. The first competition hosted was the Rover Chase Challenge. In this competition a UAV was tasked with autonomously tracking and following a ground rover as it maneuvers through an obstacle-filled environment. The drone relied on onboard sensors such as cameras and LiDAR to estimate the rover's trajectory and adjust its flight path accordingly. The second competition hosted was the Maze Navigation Challenge. In this challenge, the UAV navigated through a structured maze using LiDAR-based environment mapping and obstacle avoidance, without relying on external positioning systems such as GPS. Developing robust autonomous drone algorithms for such tasks requires extensive data collection, simulation, and testing, which can be costly and time-intensive. To address this, competitors completed this competition using a PX4-Gazebo based simulator. This dataset includes sensor data recorded in rosbag format, comprising LiDAR, IMU, GPS, and other telemetry readings. This dataset enables researchers to benchmark algorithms, conduct reproducible experiments, and develop robust UAV autonomy, perception, and GPS-denied navigation systems in both simulated and real-world contexts.

Recent advancements in uncrewed aerial vehicles (UAVs) bring innovation across different industries and inspire a wide range of applications. Among them, mid-mile delivery stands out for its potential in leveraging UAVs to greatly increase efficiency and explore regions that are hard to reach using existing transportation systems. However, due to the properties of UAVs, that is, relatively small size and mass, limited visual and sensor information, and low flight levels, they are generally: 1) susceptible to weather, 2) dangerous to ground structures, and 3) restricted to various airspaces. This paper aims to find the optimal 3D path for UAV mid-mile delivery by considering these factors. Weather forecasts, ground risks, and airspace information are integrated to build costs and constraints for the 3D path planning algorithm. The simulation results show that the generated 3D path can effectively reduce total mission time, fuel consumption, and risk while ensuring restricted airspace and areas are avoided.

Autonomous aerial vehicles (AAVs) have the potential to transform the future of air mobility. However, a major challenge remains: despite substantial advances in research on artificial intelligence (AI), data-driven machine learning (ML), and significant investment in AAV research, AAVs are not yet fully autonomous. They cannot engage in behavior that allows them to safely perform complex real-world tasks in interaction with other agents and their physical environments, particularly under challenging conditions. In this paper, we present an innovative nationwide infrastructure called AIrTonomy that will allow researchers and industry to resolve this failure. AIrTonomy is an open and remotely accessible research experimentation infrastructure that tightly integrates a uniquely instrumented proving ground for physical flights with a cyberinfrastructure for supporting virtually simulated, as well as mixed-reality flight experiments using continuously updated high-fidelity digital twins. AIrTonomy will support scientists and engineers in a new converging field of aerial autonomy research to verify and validate the safety of AI/ML for autonomous aerial systems so that they can perform complex real-world tasks under constraints and close the Sim2Real gap. The physical infrastructure of AIrTonomy, located at Purdue University, integrates four facilities, a unique fleet, and advanced communication and sensing infrastructures into a 15-mile triangular corridor, supporting experiments where AAVs fly in real urban airspace and perform complex tasks jointly with remotely located humans. Once operational, AIrTonomy will support more than 240 physical and 12,000 simulated virtual flights (similar to 30 min duration) monthly, with a projected user base of 1,300+ researchers over its lifetime.

We demonstrate that the error dynamics of a thrusting spacecraft are nearly group affine on the SE_2(3) Lie group, and the nonlinearity can be bounded, or removed with the application of a dynamic inversion control law. A numerical example validates the results by showing agreement between the error predicted by the log-dynamics and the error obtained from classical integration of trajectories using Newtonian dynamics. The result clarifies how thrusting spacecraft dynamics fit within the invariant systems framework.

Fixed-wing Unmanned Aerial Vehicle (UAV) testbeds are crucial to safely support advancements toward Human-Autonomy Teaming (HAT) research. While HAT algorithms aim to unify human and Artificial Intelligence (AI) collaboration for autonomous vehicle operations, a safe and secure experimental platform enables reliability testings before such algorithms’ deployment into real-world operations. However, hardware tests can pose external risks and air regulatory issues for full-scale outdoor experiments, and setting up a Ground Control Station (GCS) to monitor and command multiple UAVs on-site can be challenging due to equipment and logistical limitations. Therefore, this paper proposes a Mixed-Reality (MR) testbed that allows verification and validation of remotely operated UAV operations, enabling HAT frameworks to safely perform controls of physical fixed-wing UAVs in a controlled indoor environment. We further discuss the design for the communication architecture of fixed-wing UAVs for indoor flight experiments and its integration into the MR system. Finally, we present a use case where our proposed testbed is applied in HAT research involving dynamic path planning problems.

We present a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be utilized to assess the impact of FDI attacks, benchmark attack detector performance, and validate the effectiveness of mitigation/reconfiguration strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework leverages high-fidelity simulations of Gazebo and a Motion Capture system to emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor measurements in real-time. We propose an empirical approach to faithfully recreate signal characteristics such as latency and noise in these measurements. Finally, we illustrate the efficacy of our proposed framework through a Mixed Reality experiment consisting of an emulated GNSS attack on an actual UAV, which (i) demonstrates the impact of false data injection attacks on GNSS measurements and (ii) validates a mitigation strategy utilizing a distributed camera network developed in our previous work. Our open-source implementation is available at https://github.com/CogniPilot/mixed_sense

In this paper, we use the derivative of the exponential map to derive the exact evolution of the logarithm of the tracking error for mixed-invariant systems, a class of systems capable of describing rigid body tracking problems in Lie groups. Additionally, we design a log-linear dynamic inversion-based control law to remove the nonlinearities due to spatial curvature and enhance the robustness of the controller. We apply Linear Matrix Inequalities (LMIs) to bound the tracking error given a bounded disturbance amplified by the distortion matrix and leverage the tracking error bound to create flow pipes. To demonstrate the usefulness of our method, we show its application with Urban Air Mobility (UAM) scenarios using a simplified kinematic aircraft model and polynomial-based path planning methods.

The introduction of the Unmanned Aerial System (UAS) opened up a new challenge for safety and security in the aerospace community. These small, agile aerial vehicles can fly undetected within existing surveillance systems and perform malicious tasks. We present a UAS path planning algorithm capable of surveillance avoidance with consideration of both temporal and spatial constraints on vehicle dynamics. A probabilistic sampling model is implemented to allow the vehicle to wait to seek a window of opportunity to further reduce the computation time and total path length. We simulate a hypothetical urban environment with a deployed surveillance camera system as a defender. The simulation results show the success in exploiting the weakness of the surveillance system deployment, which can be leveraged to suggest improvements in the deployment of the defender's surveillance sensors.

To address the security of cyber-physical systems, stealthy attacks, a class of false data injection attacks that can impact a system without being detected, have been studied in the recent decade. The existing discussions on stealthy attack design have rarely considered systems with switching structures, but recent literature shows the importance of this problem. Therefore, we are motivated to investigate a stealthy man-in-the-middle attack strategy that can be applied to switched systems. Specifically, we first investigate how to design a stealthy attack without using mode information. Then, we consider whether the attacker can infer the mode information that can be used to design a stealthy attack. By combining mode identification and stealthy attack design, a stealthy man-in-the-middle attack strategy for switched systems is proposed. In addition, the feasibility and effectiveness of the strategy are discussed, and an illustrative numerical example is given to demonstrate the proposed strategy.

This paper presents an approach that employs log-linearization in Lie group theory and the Newton-Euler equations to derive exact linear error dynamics for a multi-rotor model, and applies this model with a novel log-linear dynamic inversion controller to simplify the nonlinear distortion and enhance the robustness of the log-linearized system. In addition, we utilize Linear Matrix Inequalities (LMIs) to bound the tracking error for the log-linearization in the presence of bounded disturbance input and use the exponential map to compute the invariant set of the nonlinear system in the Lie group. We demonstrate the effectiveness of our method via an illustrative example of a multi-rotor system with a reference trajectory, and the result validates the safety guarantees of the tracking error in the presence of bounded disturbance.