Robot Motion Control System Research Articles

Trajectory Generation Method for Serial Robots in Hybrid Space Operations

The hybrid space of robots is divided into task space and joint space, with task space focused on trajectory-tracking accuracy, while joint space considers dynamic responsiveness and synchronization. Therefore, the robot-motion control systems need to effectively integrate both aspects, ensuring precision in task trajectory while promptly responding to unforeseen environmental events. Hence, this paper proposes an online trajectory-generation method for robots in both joint and task spaces. In task space, a planning approach is presented for high-precision NURBS curves. The global NURBS curve is segmented into several rational Bezier curves, establishing local coordinate systems for control points. This ensures that all local control points meet the chord error constraint, guaranteeing trajectory accuracy. To address the feed rate dynamic planning issue for segmented curves, an improved online S-shape feed-rate scheduling framework is introduced. This framework dynamically adjusts the current execution speed to meet task requirements. In joint space, an offline velocity planning based on a time synchronization scheme and a multi-dimensional synchronization technique based on the principle of spatial-coordinate system projection are proposed. Building upon the offline scheme, it allows for the modification of the target state for any sub-dimension during the motion process, with the remaining dimensions adapting accordingly. Simulation and experimentation demonstrate that the two proposed online trajectory generations for robot motion spaces, while ensuring task trajectory accuracy, effectively handle external unexpected events. They ensure joint synchronization and smoothness, carrying significant practical implications and application value for the stability of robot systems.

Modeling of Control System for Tracked Mobile Robot Taking Into Account Kinematic and Dynamic Parameters

The paper examines the problem of constructing a motion control system for autonomous mobile tracked robots in an informal external environment. Based on the proposed mathematical model of the control system for a tracked mobile robot, which takes into account kinematic and dynamic parameters, simulation modeling of a tracked mobile robot was carried out in the dynamic modeling environments of technical systems MATLAB Simulink and SimInTech, which made it possible to control the coordinates of a tracked mobile robot along a predetermined trajectory with a certain accuracy. To increase the stability of the mobile robot motion control system, a PID controller of the armature current and electromagnetic torque was introduced into it. During the simulation study, graphical dependences on time were obtained: supply voltage; rotation angle of the robot body; track speeds; motor armature current; electromagnetic torque of engines; armature current of motors with PID controller; the path traveled by the caterpillars; electromagnetic torque of motors with a PID controller, and also the center of mass of the robot was set when setting a trajectory with a radius of 10 m for 6.2 s. Models were built in the MATLAB Simulink software package: general simulation, kinematic simulation and dynamic simulation of a tracked mobile robot, simulation subsystem of the electric drive control unit. In the SimInTech software environment, a simulation model of the dynamic part of the right electric drive of a tracked mobile robot was obtained. A comparative analysis of the graphical dependencies of the angular velocity of the roller and armature current of the motor of a tracked mobile robot, obtained in the MATLAB Simulink and SimInTech packages, was carried out, which revealed a number of advantages and disadvantages when testing the operation of the control system of a tracked mobile robot in an unformalized external environment.

Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Research on the design of mechanical robot control system for industrial PC in the context of big data

Abstract In the digitalization of big data information, how to use big data technology to design and study the mechanical robot control system of industrial PC has become a key topic of concern in today’s society. In this paper, robot kinematic modeling is mainly used to analyze and study the robot motion equations and control system operation mechanism, and the motion controller of the industrial robot control system is selected from the CLIPPERPMAC controller of the PMAC motion controller series. The approach to reducing the operation error of the industrial robot system from the control perspective is discussed. The error is reduced by adjusting the PID parameters of the PMAC motion controller in automatic and manual ways, and the error is further reduced by introducing fuzzy control algorithms. By comparing the original curve, the curve after the PID adjustment of PMAC itself and the curve after the introduction of the fuzzy controller, the curve error is kept below 5%, which is determined to be an effective method to reduce the error. This study can precisely realize the design and research of mechanical robot control systems, thus providing a guiding reference value for the research of industrial robot mechanization.

Parking Robot Path-Tracking System Based on Discrete PID Algorithm

To improve the space utilization rates of intelligent stereo garages, their key execution equipment—parking robots—must be highly flexible; therefore, the chassis of parking robots use steering wheel systems. However, this design increases the complexity of robot motion control and path-tracking systems. Because of the large sizes of parking robots, large position and angle deviations can lead to derailment events, posing significant risk to users. Thus, in this study, based on the characteristics required for parking robots, a diagonally arranged parking robot with double steering wheels was developed and its kinematic model was established using the velocity–geometry method. For the positioning of the robot in a garage, a fusion positioning algorithm based on quick response (QR) codes and track calculation was used, considering the requirements of real-time response and accuracy. A discrete proportional-integral-derivative (PID) path-tracking algorithm for the synchronous compensation of the position and angle deviations was also devised. Using the Visual C++ platform, a path-tracking system that uses an industrial computer and motion control card was developed. Experiments on the path-tracking capability of the parking robot showed that, using the discrete PID path-tracking algorithm, it can track a planned path well. On Z- and U-type paths, its maximum tracking deviations were reduced by 84.8% and 64.0%, compared with the unused path-tracking system, reaching 10.4 mm and 10.9 mm, respectively. Thus, the tracking accuracy was significantly improved, proving that the developed robot well satisfies the path-tracking requirements of parking robots.

Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

In order to further optimize the precision and efficiency of intelligent robot navigation system control, an IoT intelligent robot motion control system based on the improved ResNet model is proposed. Based on the deep learning method, using the Faster R‐CNN target detection architecture and the ResNet50 convolutional neural network, the network is trained according to the characteristics of the operation target of the distribution line maintenance robot system. On this basis, combined with the binocular vision ranging principle, the coordinates of the job target in the camera coordinate system are measured, and the coordinates are converted into the robot base coordinate system through hand‐eye calibration, so as to complete the spatial positioning of the job target. The results showed that the errors of the binocular measurement methods adopted by the system are all within 1%. Conclusion. The method can well adapt to the complex background of the operation scene, the change of illumination, and the partial occlusion of the target and can meet the requirements of the distribution line maintenance robot for the measurement and positioning of the target space.

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform.

Developing a Linear Quadratic Regulator for Human Lower Extremity Exoskeleton Robot

During the last two decades, exoskeleton robot-assisted neurorehabilitation has received a lot of attention. The major reason for active research in robot-assisted rehabilitation is its ability to provide various types of physical therapy at different stages of physical and neurological recovery. The performance of the robot-assisted physical therapy is greatly influenced by the robot motion control system. Robot dynamics are nonlinear, but many linear control schemes can adequately handle the nonlinear dynamics with the help of feedback linearization techniques. In this study, the dynamic model of the human lower extremities was developed. A state-space form of the human lower extremity nonlinear dynamic model is presented. LuGre friction model was used to simulate the robot joint friction. A Linear Quadratic Regulator (LQR) was designed to control the human lower extremity dynamics. Dynamic simulations were carried out in the Matlab-Simulink environment. The designed controller's tracking performance was demonstrated in the presence of joint friction. The developed controller’s tracking performance is assessed by comparing the results obtained using LQR with other linear and nonlinear controllers (PID, Computed torque control, and Sliding mode control). For performance verification, the same robot dynamics, friction model, and trajectories were used. The stability of the developed control system is also analyzed.

ЦИФРОВОЕ И ШИРОТНО-ИМПУЛЬСНОЕ УПРАВЛЕНИЕ КОСМИЧЕСКИМ РОБОТОМ ПРИ СБЛИЖЕНИИ С ГЕОСТАЦИОНАРНЫМ СПУТНИКОМ

The control problems on a space robot during its approach to an information geostationary satellite are considered. The robot motion control system uses an electric propulsion system with 8 engines at the pulse-width modulation of their thrust values and a gyroscopic moment cluster based on 4 gyrodines with digital control. Numerical results are presented that demonstrate the effectiveness of the developed discrete guidance and control algorithms.

Coordinated Motion Control for a Wheel-Leg Robot with Speed Consensus Strategy

In this article, a speed consensus strategy that combined distributed active disturbance rejection control (ADRC) framework with a consensus algorithm is proposed for the coordinated motion control of a wheel-leg robot. In addition, combined with the proposed strategy, a new steering system is designed. The proposed strategy can deal with the coordinated motion control problem of the robot with internal modeling uncertainty and external disturbances. To this end, a new ADRC consensus protocol for the robot motion control system is proposed under an undirected graph. By applying this consensus protocol to the robot motion control system, the problem of coordinated motion control can be solved. The proposed new steering system consists of four inverted Stewart platforms that can achieve an angle range of $ - \text{60}^{\circ} - + \text{60}^\circ $ and make the steering precisely and nimbly. To deal with the consensus issue with different leaders when the robot is steering, the proposed ADRC consensus protocol is combined with the new steering system. The application of the proposed method is illustrated in the electric parallel wheel-leg robot system. Experimental results are provided to verify the effectiveness of the proposed approach.

Design and Simulation of Biotechnical Multidimensional Motion Control Systems of a Robot Manipulator

The design of multidimensional velocity, position and force control algorithms for a semi-automatic motion control system of a robot-manipulator has been performed. Mathematical and computer models of a motion control system with reconfigurable structure have been developed for a 3-link robot performing standard operations set by a human operator with a 3-degree-of-freedom handle. Simulation of dynamic processes in a system with different control algorithms has been performed and their precision characteristics have been evaluated. The achieved results may be of interest for designers who develop training simulators for human supervisors of robot motion control systems.

Mathematical modeling of the robot motion control system for monitoring deep sea communications

The aim of the work is to determine the parameters of the mathematical model of the motion control system (MCS), which provides automatic monitoring of deep-sea communications with the minimum number of control devices of the MCS, due to which it is possible to use additional monitoring equipment. Mathematical models of the motion of an underwater robot are presented, block diagrams are constructed and the results of the motion of an underwater robot in the vertical and horizontal planes using the Matlab Simulink graphical simulation environment are obtained.

Unknown System Dynamics Estimator for Motion Control of Nonlinear Robotic Systems

In this paper, we propose an alternative, simple, yet efficient estimation method to handle unknown dynamics and external disturbances for motion control of robotic systems. An unknown system dynamics estimator (USDE) is first proposed by introducing filter operations and simple algebraic calculations, where the external disturbances and unknown Coriolis/gravity dynamics can be estimated simultaneously. In the specific case where only the external disturbance is unknown, a further modified unknown disturbance estimator (MUDE) is introduced. These proposed USDE and MUDE can be easily implemented and their parameter tuning is straightforward compared with the nonlinear disturbance observer that requires calculation of the inverse of the inertia matrix. The acceleration signal of robotic joints is not used in the design of estimators. Moreover, we also show that the proposed estimators can be incorporated into the design of composite controllers to achieve satisfactory motion tracking response. The closed-loop control system stability and convergence of both the tracking error and estimation error are all guaranteed. Finally, the effectiveness of the two proposed methods is validated by using simulations and experiments based on a SCARA robot test-rig.

Simulation design of trajectory planning robot manipulator

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.

A real time self-tuning motion controller for mobile robot systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.

Research on Robot Control Technology Based on Kinect

With the development of robot control technology, the interaction between humans and robots has become more and more common. Using human movements and gestures to control the robot can replace complex and cumbersome program operations, easily and conveniently manipulate the robot, issue commands to the robot, and interact with the robot. Human vision and gesture recognition based on machine vision is an indispensable key technology for implementing a new generation of human-computer interaction systems. As a revolutionary product, Kinect somatosensory devices capture color images, depth images, and human bone images, and provide a new way of human-computer interaction. It captures and tracks human movements, gestures and sounds. In this paper, the Kinect somatosensory device is used for static gesture recognition and dynamic gesture recognition. Based on this, the robot action is used to realize the contactless interactive interaction of the robot through gesture control. This paper introduces a Kinect device-based robot control method, which is related to the field of robot control, and is applied to the two-wheeled self-balancing robot motion control and human-computer interaction system. This method utilizes Kinect for the motion control system of two-wheeled self-balancing robot. It is controlled with the DTW gesture recognition algorithm.

Intelligent mobile robot control system based on PC+ underlying motion controller

Open-structure mobile robot motion control system based on “PC + underlying motion controller” could take advantage of the high degree of good versatility, real-time performance, high processing capacity of PC, and high controlling capacity of underlying motion controller. And the overall design of the open- structure mobile robot motion controller based on PC + stm32 is developed, which contains the system control architecture, the layered architecture of the control system and the human-computer interface. An algorithm also proposed for estimating the robot’s position and orientation, and the experimental system built for the prototype, provided the platform for moving, monitoring and automation. Experimental results of the proposed control system show that the open-structure mobile robot owns the stability, accuracy, rapid response and exact localization.

Analytical Study of Algorithms for Solving Inverse Kinematic Problems in Robot Motion Control

The given article covers the general formulations of inverse kinematic problems for robot motion control systems. We have discussed the difficulties how to solve such problems using analytical and numerical methods. We have also analyzed the convergence of iterative algorithms with the regularization on the trajectory with the points outside of the gripper reachability. The example of an iterative calculation of joint trajectories for a 3-link robot using the recursive algorithm for the Jacobi matrix calculation has been presented.

LOCALIZATION AND MOTION CONTROL IMPLEMENTATION FOR AN AGRICULTURAL MOBILE ROBOT

In robot navigation experiment, a localization and motion control system is required to secure the agricultural robot motion in the environment. However, the high cost of localization system and complex structure of motion controller has limited the low cost agricultural mobile robot development. In this paper, a low-cost localization system and simple motion control system is presented. The localization system has been implemented using a dead reckoning method by accessing an incremental encoder’s reading. A simple cascaded motion control system based on proportional feedback kinematics controller and PI based controller was used to control the mobile robot motion. The performances of different turning methods: U turn and a π turn, were compared for lane changing, based on completion time, controller’s error and distance travelled. Simulation test of robot motion was conducted using a Simulink3d animation in MATLAB software. An experimental test in a real greenhouse environment was conducted to verify the simulation performance in motion control and localization system. The experimental and simulation results have shown that a U turn has the best turning performance with 69.1 % better efficiency in experimental mode and it is recommended to be applied in agricultural field.

An FPGA Implementation of a Robot Control System with an Integrated 3D Vision System

Robot decision making and motion control are commonly based on visual information in various applications. Positionbased visual servo is a technique for vision-based robot control, which operates in the 3D workspace, uses real-time image processing to perform tasks of feature extraction, and returns the pose of the object for positioning control. In order to handle the computational burden at the vision sensor feedback, we design a FPGA-based motion-vision integrated system that employs dedicated hardware circuits for processing vision processing and motion control functions. This research conducts a preliminary study to explore the integration of 3D vision and robot motion control system design based on a single field programmable gate array (FPGA) chip. The implemented motion-vision embedded system performs the following functions: filtering, image statistics, binary morphology, binary object analysis, object 3D position calculation, robot inverse kinematics, velocity profile generation, feedback counting, and multiple-axes position feedback control.