In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

Large workspace is one of the promising advantages possessed by the cable-driven parallel robots (CDPR) over the conventional rigid-link robots. This paper focuses on the dynamic analysis and workspace classification based on the general motion equation of cable robot and the unilateral property of cables. The combinations of different types of two conditions lead to several different types of workspace, including static equilibrium workspace, wrench closure workspace, wrench feasible workspace, dynamic workspace, and collision-free workspace. A qualitative comparison of different types of workspaces is performed. The simulation results verify the relationship between the several types of workspaces.

Loop closure detection is a key module for visual simultaneous localization and mapping (SLAM). Most previous methods for this module have not made full use of the information provided by images, i.e., they have only used the visual appearance or have only considered the spatial relationships of landmarks; the visual, spatial and semantic information have not been fully integrated. In this paper, a robust loop closure detection approach integrating visual–spatial–semantic information is proposed by employing topological graphs and convolutional neural network (CNN) features. Firstly, to reduce mismatches under different viewpoints, semantic topological graphs are introduced to encode the spatial relationships of landmarks, and random walk descriptors are employed to characterize the topological graphs for graph matching. Secondly, dynamic landmarks are eliminated by using semantic information, and distinctive landmarks are selected for loop closure detection, thus alleviating the impact of dynamic scenes. Finally, to ease the effect of appearance changes, the appearance-invariant descriptor of the landmark region is extracted by a pre-trained CNN without the specially designed manual features. The proposed approach weakens the influence of viewpoint changes and dynamic scenes, and extensive experiments conducted on open datasets and a mobile robot demonstrated that the proposed method has more satisfactory performance compared to state-of-the-art methods.

The kinematics of a two rotational degrees-of-freedom (DOF) spherical parallel manipulator (SPM) is developed based on the coordinate transformation approach and the cosine rule of a trihedral angle. The angular displacement, angular velocity, and angular acceleration between the actuators and end-effector are thus determined. Moreover, the dynamic model of the 2-DOF SPM is established by using the virtual work principle and the first-order influence coefficient matrix of the manipulator. Eventually, a typical motion plan and simulations are carried out, and the actuating torque needed for these motions are worked out by employing the derived inverse dynamic equations. In addition, an analysis of the mechanical characteristics of the parallel manipulator is made. This study lays a solid base for the control of the 2-DOF SPM, and also provides the possibility of using this kind of spherical manipulator as a 2-DOF orientation, angular velocity, or even torque sensor.

This paper deals with the kinematic calibration and motion control of a triple-level spatial positioner consisting of the cable-driven parallel manipulator (CDPM), active gyro stabilizer (AGS), and the Stewart platform. A six-degree-of-freedom laser tracker is employed when calibrating the benchmark positions and measuring the real-time position and orientation in motion control, which makes it a straightforward solution to tackle with hierarchical mechatronic system actuated by servomotors with incremental encoders. Then the trajectory planning and motion control of the triple-level robotic spatial positioner are explored to verify the correctness and to what extent the calibration improves the system. This CDPM based spatial positioner has an accuracy of several millimeters though it has a ten-meter workspace.