Hongyu Chu

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera.

As a very serious problem for control systems, actuator saturation often leads to an unstable system. The maneuvering flight of quadrotors may lead to actuator saturation, which commonly affects the flight quality, including trajectory-tracking accuracy. Quadrotors usually adopt double-closed-loop control, of which the outer loop is the position control used to calculate the desired attitude angle and the inner loop is the attitude control used to generate control actions for the vehicle. To address actuator saturation during the maneuvering flight of quadrotors, an attitude controller based on the conditioned super-twisting algorithm (CSTA) was designed. The sign functions in CSTA were replaced by a hyperbolic tangent function, which suppresses the chattering of CSTA. In order to improve the rapidity of the improved controller, the tanh-CSTA (TCSTA), a gain adjustment factor was utilized. The rapidity and smoothness of the controller can be satisfied by adjusting the gain factor. Additionally, a sliding surface was designed according to the characteristics of the quadrotor control structure, so that the TCSTA controller can be applied to the second-order system. The performances of the designed improved controller were simulated using MATLAB, and the results showed that the designed controller is more robust than the PID controller. Moreover, compared with the CSTA controller, the chattering of the designed controller was obviously suppressed without reducing the control accuracy.

Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS.

Radioactive pollution detection plays a key role in nuclear technology application. In this paper, an array-type of nuclear pollution detection system is designed for the detection scenario of complex surfaces. Firstly, to get the three-dimensional point cloud of the surface, a complex surface was modeled based on the geometric ranging model of a two-dimensional laser profilometer and the motion model of a two-degree-of-freedom displacement platform. Secondly, an ‘S’ type scanning scheme of profilometer was developed to overcome the problem of limited scanning area of the profilometer. Thirdly, Euclidean distance weighted median filtering was used to solve the impulsive noise that may occur during the point cloud acquisition process. Finally, the 3D point cloud information of the complex surface was used for controlling the movement of the 6 × 6 array channel pollution detector to complete the α and β particle measurement tasks. A mechanical platform was constructed for experiments, the results are as follows. The working range of this system is from −5 cm to 5 cm in elevation difference of surfaces, and the accuracy is 12 μm in surface height measuring. It takes 26.13 s to perform a detection task including surface scanning and the detector moving, and scanning accuracy is 0.35 × 0.35 mm². The maximum control error of the surface contamination detector is 0.4 mm. Specifically, the detection area of the system reaches 240 × 240 mm². The results show that the system acquires three-dimensional terrain information, and realizes control over the movement of the pollution detector accurately and then completes the detection of α and β particles effectively.

Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.

The use of quadrotors is proliferating across many civil applications, including those in the areas of the Internet of Things (IoT) and communications. Attitude control plays an important role in these applications, and designing a quadrotor attitude controller is a challenging task. Recently, several design methods for the attitude controller were presented with an accurate model. However, during the flying state, the quadrotor may be affected by several factors that cause unpredictable disturbances to the model. As a result, an accurate model of disturbances is difficult to establish. To address this problem, in this paper, a novel linear feedforward all-coefficient adaptive control (FACAC) strategy based on characteristic modeling is proposed. The contributions of our work are as follows. First, a second-order characteristic model of the quadrotor attitude control is established based on the dynamic equations. Second, an all-coefficient adaptive control (ACAC) controller that consists of the golden-section adaptive control law, the logic integral, and logic differential control laws is designed. The feedforward maintaining and tracking control law is integrated into the ACAC controller to shorten the settling time. Finally, extensive simulation results demonstrate that the performance of the proposed FACAC controller outperforms the state-of-the arts.

To solve the problem with complex structure and low resource utilization of traditional target tracking UAV system based on visual guidance, this paper discusses an implementation method of quadrotor UAV target tracking system based on OpenMV. This system mainly includes flight control system and visual guidance system. TM4C123GH6PMI was selected as the control unit for flight control system. Six-axis motion sensor and digital magnetometer were combined into the pose measurement module. And ultrasonic sensor was selected to get the altitude information for drone. This system took OpenMV as the visual guidance system for target detection and position. For the real-time and accuracy requirements of the tracking system, the centroid-based tracking algorithm was used for calculating and resolving the target. The experiment results show that the system is stable and reliable.

The inverted pendulum is characterized as a typical nonlinear, high order, unstable and underactuated system. The planar inverted pendulum is one of the more complicates ones. In the paper, we designed a controller based on sliding mode strategy to realize the trajectory tracking control of the planar inverted pendulum. Firstly, the simplified dynamical model of the planar invert pendulum is developed. Secondly, based on the simplified model, a fuzzy sliding mode controller is constructed to track the desired trajectory. Finally, we verified the validity of the algorithm through simulation experiments on MATLAB. The results show that the proposed control approach can ensure good stability and robustness.