Formation control of a UAV swarm is challenging in outdoor GNSS-denied environments due to the difficulties in accomplishing relative positioning among the UAVs. This study proposes a vision-based formation control strategy that could be implemented in the absence of an external positioning system. The hierarchical architecture has been constructed for the UAV swarm using the modified leader-follower strategy. The leader UAV derives and broadcasts the locations of the follower UAVs, while the follower UAVs calculate their control inputs to achieve the desired swarm formation. The vision-based localization of the UAVs is accomplished using state-of-the-art deep learning algorithms like YOLOv7 and DeepSORT. The RflySim-based simulation has been conducted to verify the feasibility of the conceptualization, and the validation has been made with a real flight test using a swarm comprised of five quadrotors. Results show the robustness of the vision-based UAV positioning framework with a localization error within 0.3 m. Moreover, the formation control without the GNSS is achieved with a monocular camera and the entry-level AI platforms implemented onboard, which could be promoted to UAV swarm applications for broader scenarios.

In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements.

The quadrotor Unmanned Aerial Vehicle (UAV) belongs to an open-loop unstable nonlinear system, which also has the characteristics of underdrive, strong coupling and external disturbance. In the height control of quadrotor UAVs, the traditional sliding mode control (SMC) and PID methods cannot quickly and effectively eliminate disturbance effects caused by gust, aerodynamic drag and other factors, which indicates that the quadrotor UAV cannot return to its predetermined trajectory. To this end, this paper proposes a dual closed-loop finite-time height control method for the quadrotor UAV. The proposed method is able to estimate and compensate for the disturbance in the height control and make up for the lack of anti-disturbance ability in the control process. More specifically, a finite-time Extended State Observer (ESO) and a finite-time super-twisting controller are designed for the velocity control system to compensate for the total disturbance and track the rapidly changing expected signal. An integral sliding mode controller is designed for the height control system. Simulation results show that the proposed method can reduce the chattering phenomenon of traditional SMC and improve both control accuracy and convergence speed.

Multi-projector display systems are widely used in virtual reality, flight simulators, and other entertainment systems. Geometric distortion and color inconsistency are two key problems to be solved. In this paper a geometric correction principle is theoretically demonstrated and a consistency principle of geometric correction is first proposed. A new method of automatic registration of a multi-projector on a curved screen is put forward. Two pairs of binocular-cameras are used to reconstruct the curved screen. To capture feature points of the curved screen precisely, a group of red-blue coded structured light images is designed to be projected onto the screen. Geometric homography between each projector and the curved screen is calculated to gain a pre-warp template. Work which can gain a seamless display is illustrated by a six-projector system on the curved screen.