The aerial manipulator is a new type of aerial robot with active operation capability, which is composed of a rotary-wing drone and an actuator. Although aerial manipulation has greatly increased the scope of robot operations, the research on aerial manipulators also faces many difficulties, such as the selection of aerial platforms and actuators, system modeling and control, etc. This article attempts to collect the research team’s Achievements in the field of aerial robotic arms. The main results of the aerial manipulator system and corresponding dynamic modeling and control are reviewed, and its problems are summarized and prospected.

This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.

Unmanned aerial vehicle (UAV) image detection algorithms are critical in performing military countermeasures and disaster search and rescue. The state-of-the-art object detection algorithm known as you only look once (YOLO) is widely used for detecting UAV images. However, it faces challenges such as high floating-point operations (FLOPs), redundant parameters, slow inference speed, and poor performance in detecting small objects. To address the above issues, an improved, lightweight, real-time detection algorithm was proposed based on the edge computing platform for UAV images. In the presented method, MobileNetV3 was used as the YOLOv5 backbone network to reduce the numbers of parameters and FLOPs. To enhance the feature extraction ability of MobileNetV3, the efficient channel attention (ECA) attention mechanism was introduced into MobileNetV3. Furthermore, in order to improve the detection ability for small objects, an extra prediction head was introduced into the neck structure, and two kinds of neck structures with different parameter scales were designed to meet the requirements of different embedded devices. Finally, the FocalEIoU loss function was introduced into YOLOv5 to accelerate bounding box regression and improve the localization accuracy of the algorithm. To validate the performance of the proposed improved algorithm, we compared our algorithm with other algorithms in the VisDrone-Det2021 dataset. The results showed that compared with YOLOv5s, MELF-YOLOv5-S achieved a 51.4% reduction in the number of parameters and a 38.6% decrease in the number of FLOPs. MELF-YOLOv5-L had 87.4% and 47.4% fewer parameters and FLOPs, respectively, and achieved higher detection accuracy than YOLOv5l.

Aimed at resolving the trajectory tracking control problem of aerial manipulation, this paper presents an aerial manipulation system that consists of a quadrotor unmanned aerial vehicle (UAV) and a two-degree-of-freedom manipulator. The system adopted an integrated control strategy; that is, the rotor UAV and the manipulator were considered as a whole, the coupling effect was treated as an internal factor, and the whole was taken as the research object to establish a dynamic model. Then, by combining the nonsingular fast terminal sliding mode controller with the adaptive controller, an adaptive nonsingular fast terminal sliding mode controller was designed. A composite control rate based on the nonlinear disturbance observer and the adaptive nonsingular fast terminal sliding mode was determined in order to compensate for the external disturbance and the internal modeling error of the system as well as to ensure its rapid convergence. Finally, the different control methods were simulated by setting the operational tasks. The simulation results show that the proposed control method is effective.