Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, they face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of cybersecurity risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of detecting speed-related attacks. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD’s effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining high stealthy rates across three state-of-the-art detectors.

Unmanned aerial vehicle (UAV) through-the-wall (TTW) synthetic aperture radar (SAR) extends traditional remote sensing into penetration perception of obstructed areas in high-rise buildings with significant advantages. However, image defocusing caused by motion errors severely hinders the application of UAV TTW SAR. The mismatched signal model in the TTW condition leads the ineffectiveness of conventional airbone autofocus, while wide-beam and wide-band characteristics of the UAV TTW SAR system further aggravate the defocusing issue. In the paper, an effective metric-based motion error estimation with ground Cartesian back-projection (GCBP) algorithm is proposed. Unlike conventional SAR scenarios, a parametric signal model of UAV TTW SAR is established by incorporating refraction approximation which is a critical distinction absent in traditional remote sensing. Phase errors are further analysed from the perspective of geometric, frequency-dependent, time-variant, and space-variant characteristics. Then, aperture division and subband division are integrated with GCBP algorithm for efficient imaging. With the constraint of the UAV motion continuity, a metric-based optimization method is designed. Gradient descent combined with the line search strategy constitutes an iterative optimization mechanism. Finally, through the verifications of simulations and experiments, the proposed algorithm realizes precise motion error estimation and achieves fabulous refocusing performance in UAV TTW SAR. This technology holds immense potential for large-scale through-wall sensing of high-rise buildings, bridging the gap between traditional remote sensing and concealed space sensing in urban environments.

A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this complexity, leading to frequent conflicts, such as redundant agents performing the same task. Different from the existing single-type game model, this article introduces a distributed mixed cooperative-noncooperative (DMCN) model that considers nondifferentiable constraints. In order to deal with nondifferentiable task layer constraints, we use approximation operators and splitting schemes to transform the original optimization function into the primal-dual differentiable function. In order to obtain more stable solutions, a distributed approximate gradient descent optimization algorithm and conflict resolution mechanism are proposed, which enhances the convergence of our method. We use Lyapunov theory to verify the exponential convergence of the algorithm in the time range. Simulation and experiments demonstrate the superiority of this method and its applicability in engineering applications.

Localizing an aggressively moving platform is a considerable challenge in the SLAM domain. This paper presents a dataset, SwiftBase, crafted to facilitate research into precise localization under such conditions. It includes high-speed cameras with over 200Hz sampling rate, capturing detailed visual data for analyzing rapid external dynamics. The dataset features two IDS high-speed cameras, a low-frequency camera, and a high-precision integrated inertial measurement unit (IMU). Calibration parameters are provided, and sensor data is synchronized using ROS system time. SwiftBase is recorded in indoor environments, utilizing pulleys and suspension ropes to simulate high-speed conditions, with ground truth data supplied by OptiTrack. SwiftBase has been instrumental in evaluating advanced VI-SLAM algorithms. However, there is still an urgent need for new algorithms capable of robust and real-time tracking in High-Speed localization.1

Accurate and timely onboard perception is a prerequisite for mobile robots to operate in highly dynamic scenarios. The bio-inspired event camera can capture more motion details than a traditional camera by triggering each pixel asynchronously and therefore is more suitable in such scenarios. Among various perception tasks based on the event camera, ego-motion removal is one fundamental procedure to reduce perception ambiguities. Recent ego-motion removal methods are mainly based on optimization processes and may be computationally expensive for robot applications. In this paper, we consider the challenging perception task of detecting fast-moving objects from an aggressively operated platform equipped with an event camera, achieving computational cost reduction by directly employing IMU motion measurement. First, we design a nonlinear warping function to capture rotation information from an IMU and to compensate for the camera motion during an asynchronous events stream. The proposed nonlinear warping function improves the compensation accuracy by 10%-15%. Afterward, we segmented the moving parts on the warped image through dynamic threshold segmentation and optical flow calculation, and clustering. Finally, we validate the proposed detection pipeline on public datasets and real-world data streams containing challenging light conditions and fast-moving objects.

In this article, we focus on enabling the autonomous perception and control of a small unmanned aerial vehicle (UAV) for a façade inspection task. Specifically, we consider the perception as a planar object pose estimation problem by simplifying the building structure as a concatenation of planes, and the control as an optimal reference tracking control problem. First, a vision-based adaptive observer is proposed for plane pose estimation which converges fast and is insensitive to noise under very mild observation conditions. Second, a model predictive controller (MPC) is designed to achieve stable plane following and smooth transition in a multiple-plane scenario, while the persistent excitation (PE) condition of the observer and the maneuver constraints of the UAV are satisfied. The stability of the observer and the MPC controller is also investigated to ensure theoretical completeness. The proposed autonomous plane pose estimation and plane tracking methods are tested in both simulation and practical building façade inspection scenarios, which demonstrate their effectiveness and practicability.

We present an efficient two-phase approach to motion planning for fixed-wing Unmanned Aerial Vehicles (UAV) navigating in complex 3D air slalom environments. Firstly, in discrete 3D workspace, a global planner computer a obstacle-free path roughly which satisfies the kinematic constraints of the UAV. Given a coarse global path, a local planner generate a Dubins curve with collision avoidance based on the UAS's perception constraints, dynamic constraints and the collision perception information received. We also introduce a method of decoupling the horizontal and vertical motion directions of the fixed-wing UAV, realizing the 2D Dubins curve planning in 3D workspace, along with precomputed sets of motion primitives derived from the vehicle dynamics model in order to achieve high efficiency. Finally, the feasibility of two-phase 3D motion planning in appropriate FOV is experimentally demonstrated.

The vulnerabilities of multi-agent-system (MAS) become a critical issue for cybersecurity. The article investigates the formation control problem for MASs under multi-channel denial-of-service (DoS) attacks. In this article, the attacks on each channel are independent, while most of the existing results show that DoS attacks are the same on all channels. Without loss of generality, we consider multi-channel DoS attacks are imposed on a leader-follower MAS. Firstly, we propose a distributed formation control protocol to achieve the desired formation in the presence of DoS attacks. A translation-adaptive method is considered to adjust the interaction weights among neighboring agents online. Furthermore, a performance guarantee is derived based on the state information, and hereafter state errors among all agents can be regulated. Moreover, we derive the sufficient conditions for system stability w.r.t the controller gain and the allowable attack duration in the form of linear matrix inequalities (LMIs). Finally, simulation results are given to illustrate the effectiveness of the proposed method.

In recent years, onboard self-localization (OSL) methods based on cameras or lidar have achieved many significant progresses. However, some issues such as estimation drift and robustness in low-texture environment still remain inherent challenges for OSL methods. On the other hand, infrastructure-based methods can generally overcome these issues, but at the expense of some installation cost. This poses an interesting problem of how to effectively combine these methods, so as to achieve localization with long-term consistency as well as flexibility compared to any single method. To this end, we propose a comprehensive optimization-based estimator for the 15-D state of an unmanned aerial vehicle (UAV), fusing data from an extensive set of sensors: inertial measurement unit (IMU), ultrawideband (UWB) ranging sensors, and multiple onboard visual-inertial and lidar odometry subsystems. In essence, a sliding window is used to formulate a sequence of robot poses, where relative rotational and translational constraints between these poses are observed in the IMU preintegration and OSL observations, while orientation and position are coupled in thebody-offsetUWB range observations. An optimization-based approach is developed to estimate the trajectory of the robot in this sliding window. We evaluate the performance of the proposed scheme in multiple scenarios, including experiments on public datasets, high-fidelity graphical-physical simulation, and field-collected data from UAV flight tests. The result demonstrates that our integrated localization method can effectively resolve the drift issue, while incurring minimal installation requirements.

In recent years, thanks to the continuously reduced cost and weight of 3D lidar, the applications of this type of sensor in the community have become increasingly popular. Despite many progresses, estimation drift and tracking loss are still prevalent concerns associated with these systems. However, in theory these issues can be resolved with the use of some observations to fixed landmarks in the operation environments. This motivates us to investigate a sensor fusion scheme of lidar and inertia measurements with Ultra-Wideband (UWB) range measurements to such landmarks, which can be easily deployed in the environments with minimal cost and time. Hence, data from IMU, lidar and UWB are tightly-coupled with the robot's states on a sliding window based on their timestamps. Then, we construct a cost function comprising of factors from UWB, lidar and IMU preintegration measurements. Finally an optimization process is carried out to estimate the robot's position and orientation. It is demonstrated through some real world experiments that the method can effectively resolve the drift issue, while only requiring two or three anchors deployed in the environment.

Flocking control has been studied extensively along with the wide applications of multivehicle systems. In this article, the distributed flocking control strategy is studied for a network of autonomous vehicles with limited communication range. The main difference from the existing methods lies in that collision avoidance is considered a necessary condition while the vehicles are driven to follow a common desired trajectory under the proximity network. The sufficient conditions for system feasibility and stability are given by the proposed strategy. First, a centralized standard model predictive control (MPC) scheme is adopted to formulate the multivehicle flocking control problem by setting collision avoidance as an optimization constraint under the proximity network. Further, an equivalent distributed MPC (DMPC) is developed based on the consensus of local controllers under the existing framework of the alternating direction method of multiplier (ADMM). However, it may require infinite time to achieve consensus for all vehicles and, thus, the local controllers resulting in a limited number of ADMM iterations may not satisfy the given constraints. The constraints for each local controller are then modified so that the collision between vehicles is avoided all of the time. The feasibility and stability of the proposed method are analyzed under practical conditions. Simulation and experimental results show that the flocking of vehicles can track the common desired trajectory stably with no collisions by the proposed method.

The problem of collaborative self-localization and target tracking method under challenge environment is studied in this paper. Specifically, the scenario with general nonlinear process and sensing model as well as sparse communication is considered by combining the distributed tracking (DT) and the collaborative localization (CL) techniques. To better characterize the statistics after nonlinear transformations, the unscented transformation (UT) approach is adopted. Simulations are extensively studied to show that the proposed method have better performance on both self-localization and target tracking than the solo CL or DT method.