In this paper, a simultaneous localization and mapping algorithm based on the weighted asynchronous fusion of laser and vision sensors is proposed for an assistant robot. When compared to the synchronous fusion algorithm, this method can effectively use the redundant data in the vision sensor and improve the tracking accuracy of the algorithm. At the same time, the attitude estimation of the visual sensor is taken as a prior of the attitude estimation of the laser sensor to reduce the number of iterations and improve the efficiency of the algorithm. Further, according to the running state of the robot, a weighting coefficient based on angle is introduced to improve the confidence of the measurement. Experimental results show that the algorithm is robust and can work in a degraded environment. When compared to the synchronous fusion method, the asynchronous fusion algorithm has a more accurate prior, faster operation speed, higher pose estimation frequency, and more accurate positioning accuracy.

This study considers the visual stabilization problem of nonholonomic mobile robots and proposes a novel optimization stabilization method for visual servo control of nonholonomic mobile robots with monocular cameras fixed onboard. The main idea of the method is to utilize control Lyapunov functions of discrete-time nonlinear systems to design a family of explicit stabilization control laws of the visual servo error system. The parameters of the control laws can indirectly reflect the performance of the visual servo controllers. Then taking account of visibility constraints and actuator limitations, a set of optimal parameters of the control laws is calculated by offline solving a constrained finite horizon optimal control problem. Moreover, the stabilization results on the optimal visual servo controller are established based on the properties of control Lyapunov functions. Finally, some simulation experiments are used to illustrate and evaluate the performance of the visual servo control scheme proposed here.

This study studies a critical issue that linear velocity and steering angular velocity of the wheels of omni-directional mobile robot are easily affected by the unknown external disturbances such as wind direction and road flatness during the visual servoing stabilization task. To overcome such challenging problems, this study proposes a disturbance observer based visual servoing control strategy. In configuration, the controller is divided into two parts: the quasi-min-max model predictive controller is designed to accommodate the state constraint and input constraint of the system, and complete the visual stabilization task without disturbance. A nonlinear disturbance observer is designed to estimate the external disturbance and feedback compensate the effect of the actual disturbance on the wheel speed. The simulation results and real experiments show that the proposed control strategy can stably accomplish the visual servoing stabilization task even with a large external disturbance.