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

This article proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable that denotes the probability that the novice will behave similarly to experts. We propose a data-driven method that can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework.

In this paper, a hybrid shared controller is proposed for assisting human novice users to emulate human expert users within a human-automation interaction framework. This work is motivated to let human novice users learn the skills of human expert users using automation as a medium. Automation interacts with human users in two folds: it learns how to optimally control the system from the experts demonstrations by offline computation, and assists the novice in real time without excess amount of intervention based on the inference of the novice’s skill-level within our properly designed shared controller. Automation takes more control authority when the novices skill-level is poor, or it allows the novice to have more control authority when his/her skill-level is close to that of the expert to let the novice learn from his/her own control experience. The proposed scheme is shown to be able to improve the system performance while minimizing the intervention from the automation, which is demonstrated via an illustrative human-in-the-loop application example.

In this paper, we consider a distributed optimization problem with coupled constraints, where a network of agents aim to cooperatively minimize the sum of their local objective functions, subject to individual constraints. The primary goal is to improve the convergence rate of the existing Tracking Alternating Direction Method of Multipliers (TADMM) algorithm to solve the above distributed optimization problem. To this end, an upper bound on the convergence rate factor of the TADMM algorithm is derived in terms of the weight matrix of the network. To achieve faster convergence, the optimal weight matrix is computed using a semi-definite programming (SDP) formulation. Lastly, we implement the optimization problem in a Distributed Model-Predictive Control problem for a group of aircraft in formation flight and solve it using both Fast-Tracking ADMM (F-TADMM) and TADMM to demonstrate faster convergence of the proposed algorithm.

Due to the rapid development of technologies for small unmanned aircraft systems (sUAS’s), the supply and demand market for sUAS’s is expanding globally. With the great number of sUAS’s ready to fly in civilian airspace, an sUAS aircraft traffic management system that can guarantee the safe and efficient operation of sUAS’s is still absent. In this paper, we propose a control protocol design and analysis method for sUAS traffic management (UTM) which can safely manage a large number of sUAS’s. The benefits of our approach are two-fold: at management level, the effort for monitoring sUAS traffic (authorities) and control/planning for each sUAS (operator/pilot) are both greatly reduced under our framework; and at operational level, the behavior of individual sUAS is guaranteed to follow the restrictions. Mathematical proofs and numerical simulations are presented to demonstrate the proposed method.

This paper studies the problem of aircraft midair conflict resolution, which is a key technology to enable the coordinated and decentralized air traffic control envisioned in the Next Generation Air Transportation System (NextGen). The method proposed in this paper is based on stochastic optimal control, which is able to incorporate uncertainties in both aircraft and wind dynamics. The proposed numerical algorithm uses a Markov chain (MC) to approximate the continuous-time aircraft and wind dynamics, then the optimal control law is derived based on the MC. The proposed algorithm is able to resolve the conflicts between aircraft and moving convective weather regions. For conflict resolution between pairs of aircraft, a decomposition technique is proposed to reduce the computational complexity of the numerical algorithm. Simulations show that the proposed algorithm provides robustness against uncertainties in the system and is suitable for real applications.

An algorithm for optimal arrival flight sequencing and spacing in a near-terminal area is proposed. The optimization problem and algorithm proposed in this paper are developed for a decision-support tool for air-traffic control, which uses discrete delay times as optimization variables. The algorithm is applicable to various scenarios with situational and operational constraints such as maximum position shift (MPS) constraints or different sets of discrete delay times, depending on aircraft types or approaching routes. The proposed algorithm is based on a branch-and-bound algorithm with linear programming (LP) and Lagrangian dual decomposition. We formulate the sequencing and scheduling problem as LP with linear matrix inequalities (LMIs), which allows computing the lower bound of the cost for the best first search in the branch-and-bound algorithm and propose Lagrangian dual decomposition for computational efficiency. The proposed algorithm is analyzed and validated through illustrative air-traffic scenarios with various operational constraints, and the simulation results show that the computation time can be significantly reduced using the proposed Lagrangian dual-decomposition method.

The Next Generation Air Transportation System (NextGen) is a proposed transformation of air traffic operations to meet future growth. Under the NextGen, aircraft are expected to perform air traffic management tasks such as flight plan changes, trajectory prediction, and conflict detection. We propose a trajectory prediction algorithm for the NextGen that utilizes an aircraft dynamics model to account for changes in aircraft flight modes. This model is useful for short-term trajectory prediction and is able to account for the correlation in prediction errors due to aircraft maneuvers. We also utilize verified or inferred intents of aircraft to improve the trajectory prediction in the long term. We then propose a computationally efficient analytical algorithm to compute the conflict probability between any pair of maneuvering or nonmaneuvering aircraft. We demonstrate the computational efficiency and the accuracy of the proposed algorithm through various illustrative air traffic scenarios.