Manipulator Trajectory Control Combined with Moving Average Filtering Algorithm

To solve the problem of abnormal angular velocity and angular acceleration in manipulator trajectory motion controlled by quintic spline interpolation algorithm, a manipulator trajectory control algorithm combined with moving average filtering algorithm was proposed. Based on the quintic spline interpolation algorithm, the moving average filtering algorithm was used to clean the abnormal data under the quintic spline interpolation. And the recursive forward dynamics model based on joint space motion was used to design the trajectory motion control of the manipulator. The simulation results show that the manipulator trajectory control algorithm combined with the moving average filtering algorithm has strong constraint ability of diagonal velocity and angular acceleration, and 67% of the maximum velocity and maximum acceleration of the joint axis of the designed manipulator trajectory are significantly reduced, and the curve is smoother.

Motion intensity modeling and trajectory control of upper limb rehabilitation exoskeleton robot based on multi-modal information

The motion intensity of patient is significant for the trajectory control of exoskeleton robot during rehabilitation, as it may have important influence on training effect and human–robot interaction. To design rehabilitation training task according to situation of patients, a novel control method of rehabilitation exoskeleton robot is designed based on motion intensity perception model. The motion signal of robot and the heart rate signal of patient are collected and fused into multi-modal information as the input layer vector of deep learning framework, which is used for the human–robot interaction model of control system. A 6-degree of freedom (DOF) upper limb rehabilitation exoskeleton robot is designed previously to implement the test. The parameters of the model are iteratively optimized by grouping the experimental data, and identification effect of the model is analyzed and compared. The average recognition accuracy of the proposed model can reach up to 99.0% in the training data set and 95.7% in the test data set, respectively. The experimental results show that the proposed motion intensity perception model based on deep neural network (DNN) and the trajectory control method can improve the performance of human–robot interaction, and it is possible to further improve the effect of rehabilitation training.

Integrated Mobile Robotic Platform Model

Introduction. The “Smart Agroˮ committee of Research and Education Center “Engineering of the Future” has identified a number of tasks relevant for improving the efficiency of precision, soil-protecting and conservation agriculture. One of these tasks is the development of a digital multi-agent system, which provides a number of services for agricultural enterprises, developers and manufacturers of agricultural machinery. The purpose of the present study is to model an autonomous mobile robotic platform, including the development of software and hardware for trajectory control. Materials and Methods. To solve the problem, there are used modern CAx systems and their applications, the methods of 3D and full-body modeling, and the method of numerical solution of problems in solid mechanics. To expand and improve the standard functionality of CAx-systems (SolidWorks) in the software implementation of trajectory control algorithms, the methods and technologies of programming using API SolidWorks, VisualStudio C++ (MFC, ATL, COM) are used, and to build physical full-scale models ‒ Arduino and fischertechnik platforms. Results. The result of the study is a software and hardware module of trajectory control for an integrated (physical and virtual) model of a mobile robotic platform, which can be provided to the consumer as a service for technology autonomation. For the developed integrated model, control algorithms for various types of trajectories were tested. Discussion and Conclusion. The developed integrated software and hardware model of trajectory control can be used by developers and manufacturers of agricultural machinery, and directly by agro-enterprises for implementing typical technological processes. A feature of the implementation is an open hardware and software interface that provides the integration of mobile robotic platforms based on a digital multi-agent system.