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In this work, an innovative perception-guided approach is proposed for landing zone detection and realization of Unmanned Aerial Vehicles (UAVs) operating in unstructured environments ridden with obstacles. To accommodate secure landing, two well-established tools, namely fuzzy systems and visual Simultaneous Localization and Mapping (vSLAM), are implemented into the landing pipeline. Firstly, colored images and point clouds acquired by a visual sensory device are processed to serve as characterizing maps that acquire information about flatness, steepness, inclination, and depth variation. By leveraging these images, a novel fuzzy map infers the areas for risk-free landing on which the UAV can safely land. Subsequently, the vSLAM system is employed to estimate the platform’s pose and an additional set of point clouds. The vSLAM point clouds presented in the corresponding keyframe are projected back onto the image plane on which a threshold fuzzy landing score map is applied. In other words, this binary image serves as a mask for the re-projected vSLAM world points to identify the best subset for landing. Once these image points are identified, their corresponding world points are located, and among them, the center of the cluster with the largest area is chosen as the point to land. Depending on the UAV’s size, four synthesis points are added to the vSLAM point cloud to execute the image-based visual servoing landing using image moment features. The effectiveness of the landing package is assessed through the ROS Gazebo simulation environment, where comparisons are made with a state-of-the-art landing site detection method.

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Autonomy is the main task of a quadrotor, and visual servoing assists with this task while providing fault tolerance under GPS failure. The main approach to visual servoing is image-based visual servoing, which uses image features directly without the need for pose estimation. The classical sliding surface design of sliding mode control is used by the linear controller law of image-based visual servoing, and focuses only on minimizing the error in the image features as convergence. In addition to providing convergence, performance characteristics such as visual-feature-convergence time, error, and motion characteristics should be taken into consideration while controlling a quadrotor under velocity limitations and disturbance. In this study, an image-based visual servoing system for quadrotors with five different sliding surface designs is proposed using analytical techniques and fuzzy logic. The proposed visual servo system was simulated, utilizing the moment characteristics of a preset shape to demonstrate the effectiveness of these designs. The stated parameters, convergence time, errors, motion characteristics, and length of the path, followed by the quadrotor, were compared for each of these design approaches, and a convergence time that was 46.77% shorter and path length that was 6.15% shorter were obtained by these designs. In addition to demonstrating the superiority of the designs, this study can be considered as a reflection of the realization, as well as the velocity constraints and disturbance resilience in the simulations.