This paper addresses the problem of leader-follower formation control of mobile robots using only onboard monocular cameras that subjected to visibility constraints. An adaptive image-based visual servoing control strategy was proposed following the prescribed performance control methodology. Firstly, the leader-follower visual kinematics in the image plane and an error transformation with predefined performance specifications are presented. Then an adaptive control law with online estimating the inverse height between the optical center of camera and the single feature point attached to leader is designed to ensure the global stability of the closed-loop system. Finally, the applicability and performance of the proposed control scheme are demonstrated by the numerical simulations and hardware experiments. Compared with other formation control schemes, our solution relies only on onboard visual sensors without communication, since it does not need the relative angle/distance between the robots, or the velocity of the leader. Moreover, it guarantees prescribed transient and steady state performance besides the visibility constraints.

Defects on rail surfaces, which have become critical problems, need to be detected and removed as quickly as possible to ensure the fast, safe, and stable operation of trains. At present, although many solutions have been proposed to address these problems, the comprehensiveness, rapidity, and accuracy of defect detection remain unsatisfactory. This study aims to resolve these existing problems and accordingly proposes a multi-model rail surface defect detection system based on convolutional neural networks (MRSDI-CNN) from the standpoint of studying the squat on the rail surface. The convolutional neural networks utilized include the improved Single Shot MultiBox Detector (SSD) and You Only Look Once version 3(YOLOv3)—two types of one-stage networks. We expounded and analyzed the performance of the convolutional neural networks as well as their applicability to rail surface defect detection. We used a diverse range of rail defect sizes to improve the detection performance of the two deep learning networks, following which they could identify three types of squats in parallel with improved accuracy and without reduction of the detection speed. The experimental results confirm the effectiveness and superiority of the proposed method over those of previous studies.

This article addresses the vision-based landing problem of a low-cost quadrotor on an unknown moving platform. A robust landing controller is developed, which consists of the design of the low-complexity outer-loop controller and the geometric inner-loop attitude controller. First, an error transformation based on prescribed performance is designed to guarantee the landing behaviors and deal with the intermediate control signal of backstepping approaches, resulting in a low-complexity position-based visual servoing (PBVS) design. In addition, the proposed PBVS controller exhibits strong robustness against an uncertain relative dynamic system due to no incorporation of any prior knowledge of the moving platform. Next, a modified geometric attitude controller is presented by characterizing the geometric properties of rotation matrices intrinsically. Finally, the stability analysis is presented using Lyapunov stability theory, and the effectiveness of the proposed control strategy is demonstrated through numerical simulations and experiments.

In this the problem of leader-following tracking control of wheeled mobile robots with implicit communication through onboard visual sensing is addressed. New image-based visual servoing (IBVS) control schemes are proposed to ensure prescribed performance and the field-of-view (FOV) constraints in the absence of the feature depth information. First, the error transformation with predefined performance specifications is introduced for handling the visibility constraints. Then, in the setting of control of the uncertain system with unknown control coefficients, a Nussbaum gain adaptive controller and a static nonlinear gain controller are proposed without the need of feature depth information. Finally, both numerical simulations and physical experiments are presented to demonstrate the applicability and performance of the proposed control schemes.

This paper addresses the problem of autonomous servoing control an unmanned aerial manipulator with the capability of grasping target objects by using computer vision. Specifically, a practical visual servo control using spherical projection model is proposed. The aerial manipulator is an unmanned aerial vehicle (UAV) equipped with a robotic arm that greatly increases the freedom and operational flexibility of the end gripper. However, such a system produces complexity in its kinematics, dynamics and control duo to the coupling characteristics of the two parts. A novel passivity-like visual error equation of the image features is established by using the spherical camera geometry dynamics with an eye-in-hand configuration. To further improve the grasping performance, a task-priority control scheme is utilized with one main task and several sub-tasks, i.e., controlling the gripper position and orientation, vertically aligning the CoG (center of gravity) and avoiding the joint limitation. Simulation results are provided to illustrate and assess the performance of the proposed visual servo control. Experimental results performed in outdoor environments support well the practical analysis and effectiveness of autonomous aerial manipulation.