Considering that coverage evaluation and control is one of the critical areas in surveillance, in this study we use deployment entropy to assess the coverage quality of a swarm of micro air vehicles (MAVs). Depending on the evaluation result of deployment entropy, we also develop a two-level redeployment algorithm to cope with the MAVs' redeployment problem for maximizing coverage. With the goal of distributing the MAVs evenly over the interested region, we divide the redeployment process into two levels: the global level and the local level. We propose a strategy of MAV redeployment for maximizing the deployment entropy at both levels. From numerical simulation, results show that by computing the value of deployment entropy, it is possible to evaluate the distribution of the MAVs' network in a wide area and the two-level algorithm can improve the sensing coverage of MAVs.

This study addresses containment control problem for multi‐agent systems with general linear dynamics. The main contribution of this study is to solve the containment control problem with multiple leaders whose control inputs might be non‐zero, time varying, and the interaction graph among the followers is directed. On the basis of the neighbouring state information, a distributed static containment controller is designed, the containment error asymptotically converges to zero if the subgraph associated with the followers is directed and at least one leader has a directed path to each follower. Then, an observer‐based adaptive containment controller is proposed without knowing any global information about the topology, especially the eigenvalues of the Laplacian matrix. Simulations indicate the capabilities of the algorithm.

The intrinsic similarity between camouflaged objects and background environment impedes the automatic detection/segmentation of camouflaged objects, and novel network architectures for deep learning are promising to overcome this challenge and improve detection accuracy. However, these existing network architectures for distinguishing between camouflaged objects and their backgrounds do not account for the constraint of detection speed, which results in high computational complexity and the inability to meet the requirements of rapid detection. Therefore, based on the human visual system, this study proposes a single-stage lightweight camouflage object detection network using multilevel feature fusion, integrating features of various feature layers and receptive field sizes. Using three benchmark datasets for normal camouflaged objects, the lightweight network (LINet) model demonstrated an accuracy superior to those of six existing mainstream camouflaged object detection methods. Its detection speed, 126.3 frames per second, is significantly higher than those of the existing mainstream methods, enabling rapid detection with a maximum increase of 187.62%. The accuracy of LINet is the minimum and maximum for Resnet101 and Resnet152, respectively. These findings pave the way for diverse applications of camouflaged target detection algorithms.

In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.

With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance.

This article presents a study on the flow field and aerodynamic characteristics of the propellers and rudders for the design of a small ducted fan aircraft. In the analysis, an equivalent actuation disk was created as a simplified model of the propellers. A momentum source is determined by equivalent actuation disk numerical simulation using ANSYS CFX software based on vortex theory. In addition, the momentum source distribution function for the equivalent actuation disk model is improved by using experimental data. The computational fluid dynamics method in the ANSYS CFX software is then used to simulate the flow field of the propellers slipstream and determine the key aerodynamic and design parameters for the built-up rudders of the aircraft. The improved equivalent actuation disk method is validated by using the mixing plane method and their results show a good comparison. The simulation and analysis results provide the basis for optimal design of the built-up rudder arrangement and layout for a small ducted fan aircraft.