This paper focuses on the problem of vision-based leader-follower formation control of mobile robots. The proposed adaptive controller only requires the image information from an uncalibrated perspective camera mounted at any position and orientation (attitude) on the follower robot. Furthermore, the approach does not depend on the relative position measurement and communication between the leader and follower. First, a new real-time observer is developed to estimate the unknown intrinsic and extrinsic camera parameters as well as the unknown coefficients of the plane where the feature point moves relative to the camera frame. Second, the Lyapunov method is employed to prove the stability of the closed-loop system, where it is shown that convergence of the image error is guaranteed. Finally, the performance of the approach is demonstrated through physical experiments and experimental results.

Most existing formation control approaches are based on the assumption that the global/relative position and/or velocity measurements of mobile robots are directly available. To extend the application domain and to improve the formation control performance, it is extremely necessary to avoid the use of position and velocity measurements in the design of formation controllers. In this paper, we propose new leader-following formation tracking control schemes for nonholonomic mobile robots with onboard perspective cameras, without using both position and velocity measurements. To address the unavailability issue of position measurements, the leader–follower kinematics model in the image space is developed, which can facilitate the complete elimination of measurement/estimation of the position information. Furthermore, feedback information from the perspective camera of the follower robot is used to design adaptive observers to estimate the leader linear velocity for feedforward compensation, which can handle the absence of velocity measurements such that the proposed schemes can be applied to control formations of mobile robots without mutual communication abilities. By using the Lyapunov stability theory, a rigorous stability analysis based on the nonlinear formation dynamics is provided to show that the global stability of the combined observer–controller closed-loop system can be guaranteed. Both simulation and experimental results are also given to demonstrate the performance of the proposed formation tracking control schemes.

This Review explores the challenges and the opportunities for robotics to tackle infectious diseases such as COVID-19.

4D imaging radars (4D radars) provide point clouds with range, azimuth, elevation as well as Doppler velocity. They are much cheaper sensors than LiDARs and can operate under extreme weather conditions. However, its drawbacks of high noise and sparsity would pose great challenges for SLAM. In this paper, we present a 4D radar-based SLAM framework based on pose graph optimization. In order to get a cleaner radar point cloud for registration, the raw 4D radar data is first filtered to reduce ghost and random noise. Next, we estimate the linear and angular ego-velocity using the Doppler velocity. Based on this, we design a new ego-velocity pre-integration factor for pose graph optimization to achieve more accurate and robust pose estimation. Finally, a real-world dataset is collected in different challenging environments. The experimental results demonstrate the precision and robustness of our proposed framework.