An optimal cooperative guidance law for multiple vehicles with weak maneuverability against a superior target is studied in this paper. Firstly, the range of zero-effort-miss distance under the relative-frame-based optimal guidance law is derived by considering the acceleration limitation. Secondly, by adopting the concept of a joint maneuver domain that encompasses the escape area, the reachable sets with bias for each vehicle are devised through the establishment of time-varying virtual aiming points. Then, the guidance command for is obtained by minimum energy consumption optimization. Moreover, the relations among the number of vehicles, the coverage area of the target’s large maneuver and the coefficient of guidance law are analyzed by designing a non-overlapping coverage strategy. Finally, simulation results demonstrate the feasibility and performance of the proposed method with bias term, which can realize the perfect interception of the weak multi-vehicles against the strong maneuvering target.

This paper proposes a precision autonomous landing system for unmanned aerial vehicles (UAVs) targeting high-speed moving platforms. By integrating gimbal-based precise positioning, smooth trajectory generation, and dynamically robust control, the system addresses key challenges in high-speed landing scenarios, such as significant visual localization deviations and difficulties in dynamic trajectory planning and control. The study introduces the Comprehensive Coordinate System (CCS-3AG) to eliminate dynamic optical-axis misalignment errors in the gimbal, thereby enhancing the gimbal’s ranging accuracy and control precision. We combine an enhanced single-stage minimum control (MINCO) trajectory framework (L-MINCO) with a bidirectional command update strategy to achieve fast and accurate trajectory planning that accounts for dynamic delays, and designs an Incremental Nonlinear Dynamic Inversion (INDI) controller for high-dynamic command tracking. Simulation and real-flight experiments demonstrate that, at target speeds between 0 and 7.7 m/s, the system attains an average landing precision of 0.108 meters, with a success rate of 97.78% across 90 actual landing tests, outperforming existing landing methods. This work provides a highly robust solution for UAV logistics delivery and emergency landing scenarios.

This paper investigates the problem of distributed multiple pursuers against multiple evaders. And an integrated assignment and guidance strategy is proposed to address the issue. The proposed approach is composed of two loops, the inner loop for guidance and the outer loop for assignment. A general method for analytically deriving the control expenditure of the inner optimal guidance for each pursuer-evader pair is first studied as the performance index for the assignment loop. Then, by minimizing total guidance expenditure, a distributed method with the constraint of local target information is proposed based on the auction-based consensus assignment to obtain a globally optimal allocation scheme. Since the assignment and guidance loops share a unified merit index, the proposed algorithm is believed to outperform the methods for designing the two loops separately. Numerical simulations are performed to support the analytical findings.

With the rapid development of remote sensing technology, the quality of satellite imagery (SI) is getting higher, which contains rich cartographic information that can be translated into maps. However, existing methods either only focus on generating single-level map or do not fully consider the challenges of multilevel translation from satellite imageries, i.e., the large domain gap, level-dependent content differences, and main content consistency. In this article, we propose a novel level-aware fusion network for the SI-based multilevel map generation (MLMG) task. It aims to tackle these three challenges. To deal with the large domain gap, we propose to generate maps in a coarse-to-fine way. To well-handle the level-dependent content differences, we design a level classifier to explore different levels of the map. Besides, we use a map element extractor to extract the major geographic element features from satellite imageries, which is helpful to keep the main content consistency. Next, we design a multilevel fusion generator to generate a consistent multilevel map from the multilevel preliminary map, which further ensures the main content consistency. In addition, we collect a high-quality multilevel dataset for SI-based MLMG. Experimental results show that the proposed method can provide substantial improvements over the state-of-the-art alternatives in terms of both objective metric and visual quality.

Object detection is a very challenging task due to the serious object scale diversity. There is an obvious scale distribution in the oblique images captured by the unmanned aerial vehicles (UAVs): the objects at the top of the image are smaller in scale, while the objects at the bottom of the image are larger in scale. Based on this prior information, we propose an object detector with the divide-and-conquer strategy. First, we estimate the object scale using inertial measurement unit (IMU) information. Then the small objects at the top of image are detected by shallow networks with small receptive field and the big objects at the bottom of image are detected by deep networks with big receptive field. Compared with YOLOv5, our method improves the accuracy by 4.6% on mean of average precision (mAP) metric and improves the speed by 79%. Our method can also be performed in real-time on an NVIDIA XAVIER NX with about 30 frames/s. The code is made available on GitHub (https://github.com/bitshenwenxiao/UAVYOLO).

Ellipse detection is an important task in machine vision and can be used to solve other high-level visual problems. In the past, many studies have been conducted by researchers at high precision ellipse fitting, but most of the proposed solutions are not fast enough to be applied in real-time detection for larger images or with limited hardware resources. This paper improves the accuracy of ellipse detection by improving the candidate ellipse scoring method and geometric constraints. Combined with ellipse fitting algorithm, the proposed method can perform real-time ellipse and semi ellipse detection. The efficiency of the algorithm is proven by using three popular datasets which shows the state of the art ellipse and semiellipse detection performance.