Internet enhanced teleoperation toward a remote supervised delay regulator

One of the main challenges in networked closed-loop systems is that communication delays can significantly deteriorate system performance and even cause instability. A predictor-based framework was proposed previously to improve the networked closed-loop system performance by compensating constant network delays. The key feature of this prediction scheme is that it does not require a dynamic model of the remote subsystem and has only one design parameter to tune for each predictor to mitigate the communication delays. In this paper, the predictor-based framework is developed further. Specifically, previously the predictor was developed and its stability was analyzed for constant delays only. In this paper, the predictor structure is extended to the varying delay case and its stability is established for the first time for varying delays in a sufficient but not very conservative range by leveraging a Markov chain based approach from the literature. To that end, the key assumption of the approach that varying delays can be modeled using a two-time-scale Markov chain is tested using actual network delay data. Next, to test the performance of the framework on a networked closed-loop system, it is applied to a simulated networked motor-shaft-geartrain system with different constant and varying delay models, as well as a real network. Predictors are shown to be effective in significantly reducing the negative effects caused by delays and greatly improve the fidelity of the networked integration. Finally, stability of the closed-loop system with predictor-based framework is studied with constant delays using a “mixed” small gain and passivity theorem from the literature and it is demonstrated that the predictors can even help stabilize a closed-loop linear time-invariant system when the nonpredictor-based networked system is unstable due to constant delays.

Control of wheeled mobile robot has been studied extensively in recent years. However, control of wheeled mobile robot on shaking environments has not been studied so far. In this situation, this paper proposes a new control system for trajectory control of mobile robot which frequently accelerates and decelerates on shaking environments. To achieve the accurate control under disturbances, we adopt a model reference adaptive control approach which utilizes the internal model principle in order to control reject the effect of the disturbances. The effectiveness of the proposed method is verified by extensive simulations with the actual disturbance.

Research and Developing on Intelligent Mobile Robot Remote Monitoring and Control System

In the current article, we address the problem of constructing radiofrequency identification (RFID)-augmented environments for mobile robots and the issues related to creating user interfaces for efficient remote navigation with a mobile robot in such environments. First, we describe an RFID-based positioning and obstacle identification solution for remotely controlled mobile robots in indoor environments. In the robot system, an architecture specifically developed by the authors for remotely controlled robotic systems was tested in practice. Second, using the developed system, three techniques for displaying information about the position and movements of a remote robot to the user were compared. The experimental visualization techniques displayed the position of the robot on an indoor floor plan augmented with (1) a video view from a camera attached to the robot, (2) display of nearby obstacles (identified using RFID technology) on the floor plan, and (3) both features. In the experiment, test subjects controlled the mobile robot through predetermined routes as quickly as possible avoiding collisions. The results suggest that the developed RFID-based environment and the remote control system can be used for efficient control of mobile robots. The results from the comparison of the visualization techniques showed that the technique without a camera view (2) was the fastest, and the number of steering motions made was smallest using this technique, but it also had the highest need for physical human interventions. The technique with both additional features (3) was subjectively preferred by the users. The similarities and differences between the current results and those found in the literature are discussed.

This paper investigates the use of passive multirate wave communications for distributed haptic cooperation between two users connected over a network with constant delay and low transmission rate. The paper develops the lifted state space model of the haptic cooperation system and uses it to compute the maximum stable gains for the users' feedback loops and for the coordination of the virtual object distributed across the network. The multirate stability analysis predicts that: an order of magnitude larger coordination gains can be used when the two users are connected via passive wave communications than when they are connected via powerdomain communications or via traditional wave-domain communications; the maximum coordination gain stays unaffected by constant and symmetric network delays. A larger maximum coordination gain independent of the network delays provides increased and robust coherency of the shared virtual object. Experiments in which two users manipulate a shared virtual cube together validate the multirate stability analysis.

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

Remote control of an omnidirectional mobile robot with time-varying delay and noise attenuation

According to NLOS(Non-Line-of-Sight) robot's miniaturization and remote control technology requirement, a NLOS mobile robot control system based on embedded system technology, communication technology, imagine processing technology and sensor technology is raised, which puts an ARM9 processing chip S3C2410 as a controller and ARM-LINUX as a real-time operating system. Using H.264 video coding compression algorithm combined with USB camera image acquisition technology and wireless communication technology realizes real-time acquisition of environment image and wireless transmission; using fuzzy control algorithm and ultrasonic technology combined with robot tele-operation method realizes robot automatic obstacle avoiding and artificial remote control; using Internet technology realizes the NLOS real-time transmission of control instruction, scene image and environment variable. The simulation and experiments show that the robot control system can achieve flexible NLOS real-time control, transmit clear and successive image and also can realize automatic obstacle avoiding when encountering roadblock.

The tracking control of wheeled mobile robot is a complex problem that is encountered in robotic science. In real applications, many serious difficulties affect the control of the robot. Nonlinear model, parameters uncertainties, and external disturbances limit the study of mobile robot tracking control. To reduce the mobile robot tracking error, we propose an adaptive law based on sliding mode control applied to a nonlinear model and taking into account uncertainties. Using the Lyapunov theory, the stability and the convergence of the tracking errors are proved. Simulations are used to illustrate the efficiency of the proposed controller.

The article describes the process of developing a research stand for exploration of models and algorithms for group control of ground-based mobile robots at the Department of Intelligent Systems in Control and Automation. The role of robotics in science, technology and education is considered. The tasks most effectively solved by means of group robotics are defined. The classification of centralized and decentralized group control strategies is given. A formal statement of the problem of group control of robots is given. The structure of the robot control system for distributed collective and pack control strategies is presented. The task is to develop a research stand for exploration of models and algorithms for controlling a group of mobile robots. The prospects of using the research stand in the research work of the department are described. The functional and electrical diagram of the ground-based mobile robot is given.

A novel approach for formation control of differential-drive mobile robots is presented in this paper. The control design is done in the framework of functionally related systems. Functionally related systems are the systems that are ‘virtually’ interconnected. The term ‘virtually’ denotes the situation in which the states or outputs of otherwise separated systems are functionally related to each other. If a formation of mobile robots is analyzed, it can be considered as a group of functionally related systems. Therefore, formation control of the robots can be synthesized in the mentioned framework.

The paper concerns the problem of trajectory tracking control of a four-wheeled PIAP SCOUT mobile robot with non-steered wheels. For this kind of wheeled robots, it is impossible to find kinematic relationship between robot’s body motion and motion of driven wheels, because of inherent sliding of wheels on the ground during turning. This is an important problem from the point of view of control of the robot. The approach followed in the present work relies on introducing a simple linear controller with feedback of actual yaw rate of robot’s body. The yaw velocity is measured by inexpensive MEMS gyroscope. Experiments were conducted on two kinds of floor typical for office buildings: PVC flooring and carpet flooring. Measurements of motion parameters were possible with INS technique. It was found that the proposed yaw rate controller significantly reduces the angular error of path tracking for 90 degrees turn maneuver.

<abstract><p>In this paper, we introduce a shifted Legendre neural network method based on an extreme learning machine algorithm (SLeNN-ELM) to solve fractional differential equations with constant and proportional delays. Based on the properties of Caputo fractional derivatives and shifted Legendre polynomials, the fractional derivatives of SLeNN can be represented analytically without other numerical techniques. SLeNN, in terms of neural network architecture, uses a function expansion block to replace the hidden layer, and thus improving the computational efficiency by reducing parameters. In terms of solving technology of neural networks, the extreme learning machine algorithm is used to replace the traditional gradient-based training algorithm. It dramatically improves our solution efficiency. In addition, the proposed method does not require parameter initialization randomly, making the neural network solution stable. Finally, three examples with constant delays and three examples with proportional delays are given, and the effectiveness and superiority of the proposed method are verified by comparison with other numerical methods.</p></abstract>

This article presents the development of a Computed Torque Control (CTC) scheme for Cartesian velocity control of Wheeled Mobile Robots (WMRs). The literature presents extensive study on the need and suitability of the CTC scheme for robot arms. Many researchers have identified the benefits of using a CTC scheme for mobile robots. There is however very little information on CTC schemes for controlling mobile robots. The need for the CTC scheme stems from the fact that mobile robots (industrial AGVs) employing conventional velocity control schemes experience side slip due to suspended loads while negotiating curves, and the controller gains and accelerations need to be modified for changes in the dynamics. The structure of the proposed control scheme can be employed to control any mobile robot for which an inverse dynamic model exists, as a CTC scheme requires an inverse dynamic model to compute unique values for the motor current for a given trajectory. It is demonstrated that the existence of the inverse dynamic model is guaranteed for all differentially driven WMRs for all operating conditions, and is not affected by the number of castor wheels in the WMR. In the proposed CTC scheme, the linear and angular velocities of the WMR are controlled by adjusting the WMR accelerations, which are computed based on the motor torques required to follow a given trajectory. The motor torque is pre-computed based on a dynamic model of the mobile robotic system. The simulation and experimental results presented for a differentially driven WMR demonstrate that a computed-torque control scheme provides adaptive cruising and steering control for nominally tuned controller gains compared to a conventional velocity controller to achieve proper road following in the presence of changes in the dynamics. © 1997 John Wiley & Sons, Inc.

Wheeled Mobile robots (WMRs) are very popular because they are appropriate for applications with relatively low mechanical complexity and energy consumption. WMRs are used in many military and civilian applications. These applications include surveillance, transportation, inspection, exploration, and many other applications either in structured or unstructured environments. This paper provides a review on the developments of the wheeled mobile robots (WMRs) control. It reviews the kinematic and dynamic modeling of the robots, the path following and tracking, the avoiding obstacles control algorithms, and finally a review on robot vision and visual servoing control system is presented including the main challenges in each field of research.