Robot Control Commands Research Articles

Design and application of VR-based college English game teaching

With the deepening of international exchanges, colleges and universities have higher and higher requirements for the quality of English teaching. In order to improve the quality of English teaching in colleges and universities, the research proposes to design and construct an English game education and teaching system based on virtual reality technology. First, the modern VR technology is used to build the initial English game teaching system, and then the HMM-DNN algorithm is introduced to improve the virtual system. In the process, the CMUShinx speech recognition platform is used to build a robot control command speech recognition system, in order to obtain a good voice acoustic model and achieve correct speech recognition in the virtual environment. Finally, the performance analysis of the built system is carried out to verify the feasibility of the system model. The results show that the step response coefficient of the model built by the research institute can be kept within 1 M for a long time; When the speech type is White and Cafe1, the correct recognition rate of the constructed model for English speech is as high as 98 % and 95 %, and is always higher than other algorithms; In the comparison of the accuracy of students' oral speech recognition scores by different models, the lowest error of the research model is 2.23 %, which is significantly smaller than the other models. The above results indicate that the overall teaching effect of the system built by the research institute is stable, and it has high feasibility for college students' English teaching, which helps to further improve students' interest and ability in English learning, and is of great significance for college English talent training.

SSVEP-based Brain-Computer Interface Controlled Robotic Platform with Velocity Modulation.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

Planning Collision-Free Robot Motions in a Human–Robot Shared Workspace via Mixed Reality and Sensor-Fusion Skeleton Tracking

The paper describes a method for planning collision-free motions of an industrial manipulator that shares the workspace with human operators during a human–robot collaborative application with strict safety requirements. The proposed workflow exploits the advantages of mixed reality to insert real entities into a virtual scene, wherein the robot control command is computed and validated by simulating robot motions without risks for the human. The proposed motion planner relies on a sensor-fusion algorithm that improves the 3D perception of the humans inside the robot workspace. Such an algorithm merges the estimations of the pose of the human bones reconstructed by means of a pointcloud-based skeleton tracking algorithm with the orientation data acquired from wearable inertial measurement units (IMUs) supposed to be fixed to the human bones. The algorithm provides a final reconstruction of the position and of the orientation of the human bones that can be used to include the human in the virtual simulation of the robotic workcell. A dynamic motion-planning algorithm can be processed within such a mixed-reality environment, allowing the computation of a collision-free joint velocity command for the real robot.

Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning.

It is challenging to avoid obstacles safely and efficiently for multiple robots of different shapes in distributed and communication-free scenarios, where robots do not communicate with each other and only sense other robots’ positions and obstacles around them. Most existing multi-robot collision avoidance systems either require communication between robots or require expensive movement data of other robots, like velocities, accelerations and paths. In this paper, we propose a map-based deep reinforcement learning approach for multi-robot collision avoidance in a distributed and communication-free environment. We use the egocentric local grid map of a robot to represent the environmental information around it including its shape and observable appearances of other robots and obstacles, which can be easily generated by using multiple sensors or sensor fusion. Then we apply the distributed proximal policy optimization (DPPO) algorithm to train a convolutional neural network that directly maps three frames of egocentric local grid maps and the robot’s relative local goal positions into low-level robot control commands. Compared to other methods, the map-based approach is more robust to noisy sensor data, does not require robots’ movement data and considers sizes and shapes of related robots, which make it to be more efficient and easier to be deployed to real robots. We first train the neural network in a specified simulator of multiple mobile robots using DPPO, where a multi-stage curriculum learning strategy for multiple scenarios is used to improve the performance. Then we deploy the trained model to real robots to perform collision avoidance in their navigation without tedious parameter tuning. We evaluate the approach with multiple scenarios both in the simulator and on four differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient and outperforms existing DRL-based approaches in many indicators. We also conduct ablation studies showing the positive effects of using egocentric grid maps and multi-stage curriculum learning.

Adaptive Model-Based Myoelectric Control for a Soft Wearable Arm Exosuit: A New Generation of Wearable Robot Control

Despite advances in mechatronic design, the widespread adoption of wearable robots for supporting human mobility has been hampered by 1) ergonomic limitations in rigid exoskeletal structures and 2) the lack of human-machine interfaces (HMIs) capable of sensing musculoskeletal states and translating them into robot-control commands. We have developed a framework that combines, for the first time, a model-based HMI with a soft wearable arm exosuit that has the potential to address key limitations in current HMIs and wearable robots. The proposed framework was tested on six healthy subjects who performed elbow rotations across different joint velocities and lifting weights. The results showed that the model-controlled exosuit operated synchronously with biological muscle contraction. Remarkably, the exosuit dynamically modulated mechanical assistance across all investigated loads, thereby displaying adaptive behavior.

Gesture-based Interface for Real-time Control of a Mitsubishi SCARA Robot Manipulator

A new approach to control robot manipulators using body gestures is presented. It implements the Microsoft Kinect sensor ability to track joint positions. The later has been used in the developed software application for gesture recognition, operating in Matlab programming environment, that maps the gestures into robot control commands. The goal is to control the movements of an industrial manipulator that shares its workspace with humans. Such a working environment is characterized with complexity and dynamism. In this way a more-effective interface with the controlled robot can been created. The experimental tests have been carried out with a Mitsubishi SCARA robot manipulator model RH-6CH7020-1D1-S15.

Research on multimodal human-robot interaction based on speech and gesture

This paper presents a multimodal human-robot interaction based on fusion of speech and gesture. In the interface, a robot control command system is designed, which can transform the speech and gesture of users into commands that the robot can execute. Microsoft speech SDK is used in this system to collect the speech of the operator. Then, a corpus-based algorithm of maximum entropy classification for natural language understanding is employed to generate commands. Leap Motion is employed to capture the gesture of operator in this system. Interval Kalman Filter (IKF) is used to estimate the measured data to reduce the inherent noise of the sensor. The advantage of the proposed method is that the combination of speech and gesture makes the human-robot interaction more convenient and direct. Finally, a series of experiments were carried out to validate our method, and proved that it performed better than the other proposed methods.

Brainwaves driven human-robot collaborative assembly

This paper introduces an approach to controlling an industrial robot using human brainwaves as a means of communication. The developed approach starts by establishing a set of training sessions where an operator is enquired to think about a set of defined commands for the robot and record the brain activities accordingly. The results of the training sessions are then used on the shop floor to translate the brain activities to a set of robot control commands. An industrial case study is carried out to assist the operator in coordinating a collaborative assembly task of a car engine manifold.

Transport Robot Control by Voice Commands

В системах управления современных роботов используется нечеткая логика, поэтому целесообразно подавать команды управления роботом на основе нечеткой логики, которая позволит роботам эффективно выполнять поставленные задачи. В статье рассматривается управление транспортным роботом голосовыми командами и моделирование его движения в программном продукте Microsoft Robotics Developer Studio. Для моделирование движения транспортного робота использовался язык Visual Programming Language (VPL) - среда визуального программирования для создания и отладки приложений. Для распознавания голосовых команд используется планшетный компьютер или мобильный телефон с операционной системы Android с приложением, при помощи которого можно управлять роботом с использованием интерфейса Bluetooth. Разработана программа, которая позволит человеку управлять роботом с помощью речи. Получена визуальная программа управления движением транспортного робота. Приведен пример моделирования робота и его траектория движения с использованием голосового управления. Разработанная система управления позволяет следовать транспортному роботу от одной целевой точки к другой с использованием голосового управления. Задача управления роботом с использованием голосового управления существенно упрощается, поскольку она практически не требует специальных навыков от оператора. В конечном счете, голосовое управление облегчит использование роботов в промышленности, быту и других областях. Управлять можно будет не только роботами, но и другими устройствами, имеющими микропроцессорное управление.

Development of Robotic System for Free-Form Surfaces Polishing Based on CAM Data

Polishing, a kind of finishing process done after machining is necessary to obtain a good surface roughness as well as the form accuracy. In this paper, instead of manual polishing work, a robotic polishing system is introduced. A trajectory generator needed for generating the robot polishing path are taken from the cutter location data generated from the postprocessor of a CAD system. Using the trajectory generator, the present polishing robot doesnt need the conventional complicated teaching process. Position interpolation algorithm between two CL data is proposed, and the robot control commands can also be generated using an industrial robot Motoman sv3.

Imitation Control in Biped Robot by Using Controller to Operator

This study describes a robot that can be remotely imitation controlled by an operator wearing a special suit with sensors to facilitate various movements. Remote controlled robot has quite different body structure and control strategy. Traditionally, robot is controlled by preset commands which are calculated from the requirements to keep balance. However, our robot control command is issued by operator instead of robot itself. Balancing imitation control algorithm is designed and implemented to walk without falling down. All the trajectories such as turn, stop, start, walk, etc, are determined by the operator. Finally, experiment result shows that this kind of new control method can be applied on various areas.

Design and implementation of an electronic architecture for an agricultural mobile robot

A current trend in the agricultural area is the development of mobile robots and autonomous vehicles for remote sensing. One of the major challenges in the design of these robots is the development of the electronic architecture for the integration and control of the devices. Recent applications of mobile robots have used distributed architectures based on communication networks. A technology that has been widely used as an embedded network is the CAN protocol. The implementation of the ISO11783 standard represents the standardization of the CAN for application in agricultural machinery. This work describes the design and implementation of an electronic architecture for a mobile agricultural robot. The discussions are focused on the developed architecture, the wireless communication system for teleoperation and the distributed control based on CAN protocol and ISO11783. The evaluation of the developed system was based on the analysis of performance parameters such as motor response and architectural time delay obtained with the robot operation. The results show that the developed architecture can be applied for teleoperation and distributed control of agricultural robots meeting the requirements for accurate robot movement and an acceptable response time for robot control commands and supervision.

Automation of Deburring by a Material-Handling Robot —Generation of a Deburring Path Based on a Characteristic Model—

This study deals with the automation of deburring in press working using a material-handling robot. The robot simultaneously manipulates and deburrs a workpiece using robot control commands automatically generated. Robot control commands are generated based on environmental information — positioning of carry-in/carry-out table, tool and end effector dimensions — and CAD data using a “characteristic model” to realize robot operation suiting the working environment. The results of experiments confirm the proposal’s effectiveness in automatic deburring and material handling.

Classification of BMI control commands from rat's neural signals using extreme learning machine

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers’ outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

Exoskeleton arm with force feedback for robot bilateral teleoperation

Abstract A novel 6 degrees of freedom (DOFs) wearable exoskeleton arm, ZJUESA, based on man-machine system is presented. It can be used in robot bilateral teleoperation as master arm with force-feedback. With introducing the revolute-prismatic-spherical (RPS) parallel mechanism and planetary gear mechanism, it is designed based on the anatomy of human upper-limb. With the sensors on this external mechanical structure, the human operator motion is detected and converted to the slave robot control command. Additionally the pneumatic system on it generates a force feedback by using hybrid fuzzy control. As a result, the human operator may have a feeling of doing the work directly. *Supported by National Natural Science Foundation of China (Grant No. 50305035)

615 工作物マニピュレーションによるバリ取り作業の自動化 : ロボット動作プログラムの生成(OS11-3 加工計測・加工機械,オーガナイズドセッション:11 加工技術の高度化)

615 工作物マニピュレーションによるバリ取り作業の自動化 : ロボット動作プログラムの生成(OS11-3 加工計測・加工機械,オーガナイズドセッション:11 加工技術の高度化)

DEVELOPMENT OF A SURGICAL NAVIGATION ROBOT USING IMAGE SERVO TRACKING TECHNIQUES

This paper presents the development of surgical navigation robot, which is designed based on the image servo tracking technique. The surgical navigation robot plays an important role to the computer aided surgery (CAS), since it provides the patient's space coordinate during surgical navigation stage in real-time. Especially, the proposed active image servo tracking based surgical navigation robot can track the surgical marker that is mounted on the patient's hard tissue of the surgical position, so that the navigation space can be automatically extended. The proposed surgical navigation robot system consists of the in-lab designed dual-axis eye-in-hand robot (EIHR), digital signal processor (DSP) based EIHR motion controller, image servo tracking and positioning software. The EIHR robot is composed of the dual-axis robot mechanics and two CCD cameras, which acts as the stereoscopic image capture platform. The DSP based EIHR motion controller is designed to control the EIHR robot in a desired velocity, so that the surgical marker can be dynamically tracked. The image servo tracking and positioning software is the kernel of this work, and it is composed of image capture and recognition module, fuzzy logic based surgical marker tracking module and surgical marker positioning module. The image capture and recognition module is developed to abstract the image features of the surgical marker. The intelligent fuzzy logic based surgical marker tracking module is capable of generating robot's control command so that the surgical marker can be tracked within the desired target window. In addition, the surgical marker positioning module is used to calculate the surgical marker's space coordinates in terms of the features of stereoscopic images and the robot's posture. Finally, the prototype had been finished, and the experiment results showed that proposed EIHR achieved 1.7 mm position error within 500 mm operation range.

Automation of chamfering by an industrial robot; for the case of hole on free-curved surface

The study deals with the automatic chamfering for the case of hole on free-curved surface on the basis of CAD data, using an industrial robot. As a chamfering tool, a rotary-bar driven by an electric motor is mounted to the arm of the robot having six degrees-of-freedom in order to give an arbitrary position and attitude to the tool. The robot control command converted from the chamfering path is transmitted directly to the robot. From the experimental results, the system is found effective to remove a burr along the edge of a hole on a workpiece with free-curved surface.

Chamfering Quality Control by an Industrial Robot. For the Case of Chamfering for Safety.

The study deals with the control by an industrial robot. In the study, the chamfering quality means the property of the chamfered edge shape for various functions. Especially, a for safety is discussed in the article. In order to perform by a robot, a width control system is developed to compensate positioning errors and to control the width of part. A tool path is generated on the basis of CAD data followed by conversion to a robot control command. The depth of cut at, each point calculated based on the CAD data is given to width control unit attached to the robot to realize designated shape of chamfering. From the experimental result, the system is found to have an ability of for safety.