This Correspondence proposes a new guidance law based on integral sliding mode control (ISMC) technique for maneuvering target interception with impact angle constraint. A time-varying function weighted line-of-sight (LOS) error dynamics, representing the nominal guidance performance, is introduced first. The proposed guidance law is derived by utilizing ISMC to follow the desired error dynamics. The convergence of the guidance law developed is supported by Lyapunov stability. Simulations with extensive comparisons explicitly demonstrate the effectiveness of the proposed approach.

With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.

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 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. Firstly, 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 mAP metric and improves the speed by 79%. Our method can be also performed in real-time on an NVIDIA XAVIER NX with about 30 frames per second. The code is made available on GitHub 1.

Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

For the problem of intercepting a maneuvering target with a desired terminal impact angle, we propose a guidance law in this article based on a nonlinear relative virtual guidance model in which the origin is attached to the target. In this way, the original maneuvering target is transformed to a stationary one. We introduce the idea of polynomial guidance in the range domain into the relative virtual guidance model, then the acceleration command is obtained considering the terminal line-of-sight angle constraint and the terminal virtual look angle constraint. After linearization, the proposed law turns to a similar structure with the trajectory shaping guidance law, which is the optimal guidance law in the linear system. Finally, the performance of the proposed guidance law is demonstrated through numerous nonlinear simulations .