For pose stabilization task of nonholonomic mobile robots, this article proposes a novel integrated interactive framework, bridging the gap between visual servoing and simultaneous localization and mapping (SLAM). The framework consists of two cooperative components, control module for servoing task and SLAM module for feedback signals estimation. In most visual servoing methods, feedback signals for the servoing controller are estimated by means of multiple-view geometry assuming the target scene being always within the camera field of view (FOV). To handle the challenge that the target scene gets out of view during servoing process, the desired image is associated with the initial map by a two-step strategy, and an incremental map is constructed to guarantee available feedback signals estimation. In addition, on the basis of the kinematic model of the mobile robot and velocities designed by the servo controller, the predicted pose is exploited to discard moving objects in the camera FOV, thus making the proposed framework effective in dynamic scenes. Experimental results operated in different scenes without prior information demonstrate the effectiveness of the proposed approach to handle the FOV problem and dynamic scenes. Note to Practitioners —Traditional visual servoing stabilization approaches usually require that the feature points in the target scene remain within the FOV of the camera for feedback signals calculation, which is often neglected. Motivated by the requirement of continuous feedback signals to the servo controller, the SLAM technique is introduced to relax the FOV constraint during the servoing process. A novel integrated interactive framework is proposed in this article to further increase the applicability of the servoing system in practice, in which the SLAM module is also redesigned for the flexibility in dynamic scenes. The SLAM module provides feedback signals for the servo controller; meanwhile, velocities designed by the servo controller are utilized for the prediction mechanism in the SLAM module to discard features on moving objects. Experiments validate the applicability of the proposed framework in different scenarios.

Owing to advantages of large workspace and flexible movement, wheeled mobile robots are widely applied in industry. With vision module for environment perception, visual servoing of wheeled mobile robots has been one of the hottest research topics in robotic fields. Due to different demands for various applications and specific properties of the system, many challenges are faced for designing visual servoing control strategies. In this paper, development of visual servoing for mobile robots is elaborated in visual and control modules, respectively. The vision module along with various uncertainties are analyzed, which provides feedback signals for the servo controller, thus associating the image space with the motion space for further control. Moreover, motion controllers are devised under various constraints for different objectives, such as pose regulation and trajectory tracking. Research trends are discussed to show further focus of visual servoing of mobile robots.

This paper proposes an enhanced Extended Kalman Filter (EKF)-based Simultaneous Localization and Mapping (SLAM) algorithm based on `directional endpoint' features extracted from two-dimensional (2D) laser data for indoor environments. The proposed approach is composed of calculating the covariances of the extracted line segments, calculating the covariances of the directional endpoints, and enhanced EKF-SLAM. Different from the classical SLAM based on point and line features, this work uses the directional endpoint feature, which has 3 degrees of freedom. To facilitate the enhanced EKF-SLAM, the implicit function theorem and the geometrical method are used to obtain the uncertainty of the directional endpoint. Comparative experimental results show superior performance of our proposed algorithm. In addition, the enhanced EKF-SLAM achieves the similar performance compared with Karto-SLAM in terms of pose estimation, but at the same time, the feature map composed of a set of directional endpoints is obtained, which is robust in dynamic environments.

Quadrotor transportation systems are capable of transferring necessary relief supplies in emergency tasks. In practice, the nonnegligible hook and the payload's scale make the system exhibits double-pendulum swing dynamics, which bring great challenges to controller design and stability analysis. To realize rapid swing suppression and efficient quadrotor positioning, a nonlinear controller is designed. Specifically, a composite signal is constructed. The closed-loop asymptotic stability analysis and simulation results are provided to verify the effectiveness of the control scheme.

Due to the double-pendulum phenomenon in practical applications of the quadrotor transportation systems, most existing control methods based on simplified single-pendulums are no longer applicable. To achieve simultaneous quadrotor positioning control and swing elimination for the hook and the payload, an accurate system model is of great significance. Specifically, the dynamic equation of the plant is obtained by Lagrange's modeling method. The main characteristics of the system is then analyzed based on the derived model. Numerical simulations for the two control schemes are presented to reveal the characteristics of the unmanned quadrotor transportation systems.

In this paper, a general visual servoing structure for mobile robots is proposed to handle the situation that the target scene gets out of the camera view. Most existing visual servoing strategies are based on the assumption that images always share common feature points with the desired one during the servoing procedure, which actually cannot be guaranteed by the controller. To avoid such problems, simultaneous localization and mapping (SLAM) is introduced to visual servoing system, which contains the front-end for estimating the current pose and the back-end for optimizing the desired pose of the mobile robot. Meanwhile, compared with the traditional servoing system with artificial feature points, the scale of robot poses can be fixed by the map in the proposed scheme, which makes it applicable in natural scene. In addition, all position-based visual servoing controllers are implementable in the proposed servoing architecture. The servoing structure has been implemented on a nonholonomic mobile robot and experimental results are exhibited to illustrate the effectiveness and feasibility of the proposed approach.