Robot Arm Control System Research Articles

Design and Construction of 5-Dof EMG Based Robotic Arm System

Electromyography (EMG) is an alternate method of obtaining muscle signal outputs. As a result, the current development in designing my electric plans has piqued the attention of researchers in this subject. This is because Standard controllers lack essential components, limiting the use of limbs to operate equipment, namely an arm controlled by robotics. EMG signals are subject to noise, including crosstalk, motion artifacts, ambient noise, and intrinsic noise. Electromyography preparation requires careful selection of muscle groups, electrode placement, and environment quality, all of which impact the signal output. The goal of this study is to create an EMG-based robotic arm control system that can be used to help the elderly, individuals with impairments, and those who operate in dangerous places. Initially, a literature review on current human-robot interaction approaches and analysis of the kinematics of a 5 DOF robotic arm has been conducted. Then, utilizing Electromyogram (EMG) data obtained from the muscles of the elbow, away for controlling a 5 DOF robotic arm is provided. Two accelerometers are utilized to record the human arm's gesture and posture, this information is then communicated to the robotic arm as an input. In the experiments, wrist rotation, left, right, up, and down movements were used to establish controlled motion of a 5 DOF robotic arm. The project's goal has been met with the successful development of an EMG-controlled robotic arm. The robotic arm may yet be improved by including Implementing a wireless sensor network with multiple channels.

Development of robotic arm control using Arduino controller

The advance of Arduino-based technology has spurred innovation in the realm of robotic arm control, offering a cost-effective and accessible platform for enthusiasts and professionals alike. This paper presents the development of robot arm control using an Arduino controller. The work involves the integration of Arduino microcontrollers and sensors to enable precise and dynamic control of a robotic arm. The proposed robot is controlled by 4 servo motors, the motors rotate left, right, front, and back. The paper discusses the challenges encountered during the development process and proposes solutions, paving the way for further advancements in this burgeoning field. With Arduino's widespread availability and affordability, the presented robotic arm control system holds promise for expanding the accessibility of robotics education and fostering innovation in automation technologies. This paper provides a glimpse into the promising synergy between Arduino and robotic arm control, highlighting the contributions and implications of this technology in shaping the future of automation.

Design And Develop Robotic Arm For People With Disable Hands Using EMG And Gyroscope Sensor Control

Robotics technology is currently developing rapidly, including in the medical field. In this world there are many people with hand disabilities or patients who have amputations on their wrists, so they experience difficulties in carrying out their daily activities using their hands, such as to grasp objects. Then the thought arose to conduct research, with the aim of creating a Robotic Arm that could recognize 2 movements, namely the opening and clenching of the fingers like a hand in general. The Electromyogram sensor is used as a Robotic Arm control system, while the Gyroscope with Arduino as the control center. Based on the overall test results, the Robotic Arm can be moved with muscle contractions and user gestures to open and clench finger movements.

Hierarchical conducting networks constructed as resistive strain sensors for personal healthcare monitoring and robotic arm control

Flexible strain sensors are strongly demanded in fields of personal healthcare monitoring and human–machine interface, as they can accurately perceive external action to produce electrical output signals. Aiming at the existing problems, such as the balanced sensitivity and sensing range, durability as well as expanded application, this paper proposes a flexible strain sensor prepared based on electrospun thermoplastic polyurethane (TPU) membrane, followed by anchoring mixed conductive materials (carbon black (CB)/carbon nanotubes (CNT)) on its surface via an effective ultrasonic-assisted method. The resulting CB/CNT@TPU strain sensor delivers a desirable integration of sensing performances: good sensitivity, wide work range, fast response speed (<150 ms) and reliable cyclic stability (5000 cycles). Ascribed to the sensing behavior and additional breathability, strain sensor can be applied well to monitor human health, including human joints movements (fingers and wrists) and physiological signals (pulse and laryngeal vibration recognition). Finally, a multiaxial robotic arm control system is constructed, which provides a feasible strategy for intelligent robotic arms.

Design of control system for six-degree-of-freedom robotic arm

To study the robotic arm trajectory planning and motion, the six-degree-of-freedom robotic arm control system design is proposed. In this paper, we choose a multi-joint type of robotic arm. We analyze the design of the robotic arm at home and abroad and determine the specific servomotor model and the specific robotic arm mechanism. We analyze the torque relationship of each joint and the size of the servomotor. Using SolidWorks and Robot Studios, we carry out the three-dimensional design modeling and simulation of the robotic arm, design the whole set of robotic arms, and use the Arduino Uno development board for precise control of the servo motor. The control program was written using C language, and various control tests were carried out on the robotic arm to make sure that the system could satisfy the debugging and research of trajectory planning type.

Operation Control System for Movable Robotic Arm Based on Mixed Reality

Operation Control System for Movable Robotic Arm Based on Mixed Reality

Robotic arm control system based on triple closed-loop BLDC

Robotic arm control system based on triple closed-loop BLDC

Six-DOF modular robotic arm bearing chatter suppression algorithm

Robotic arms are frequently utilized in contemporary industrial production since they offer great qualities like high precision and low mass. How to minimize robotic arm tremors in order to maximize their control effect has emerged as one of the most critical issues to be resolved with the continued development of industrial intelligence. The study uses a combination of PID control and an artificial fish swarm technique to optimize the parameters and confirm the simulation control effect based on the kinematic analysis of a six-degree-of-freedom (Six-DOF) modular robotic arm. The findings demonstrated that the suggested fusion approach converges to zero in 80 iterations and has a recall of 0.893 and 0.785 at an accuracy of 0.8 and 0.9, respectively. The robotic arm control system’s average control effect is 42.96 %, which is a respectable control performance. In the second and third studies, the fusion approach stabilized actuator end tremor suppression after 0.01 s and 0.0001 s, respectively. It shows that the technique can effectively suppress robotic arm bearing tremor and has high flexibility for robotic arm tremor suppression, which offers trustworthy technological support for improving the motion control system of industrial robots.

Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

The Five-DOF Explosion-Removal Manipulator Based on Position and Velocity Feedforward Compensation Control.

The main problem with a robotic system arm is its sensitivity to time delays in the control process. Due to this problem, it is necessary to further optimize the control process of the system. One solution is to deal with the control accuracy and response speed issues of robotic arm joints, to improve the system's response performance and enhance the system's anti-interference ability. This paper proposes a speed feedforward and position control scheme for robotic arm joint control. The conclusion section shows that compared to traditional five-degree-of-freedom robotic arm systems, the addressed robotic arm control system has a lower tracking delay and better dynamic response performance. It can improve the system's response performance while also enhancing its anti-interference ability.

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

A robotic arm control system with simultaneous and sequential modes combining eye-tracking with steady-state visual evoked potential in virtual reality environment.

At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects' gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system.

Design of dynamic gripping control system for robotic arm based on the NJ motion controller

Due to the increasing improvement of people’s material living conditions, the requirements and needs for mechanical products are rising, thus indirectly promoting the development of mechanical devices. The study aims to provide an in-depth discussion on the power capture of NJ-type robots, the working principle, and arithmetic methods of NJ-type controllers, and to conduct simulation tests of NJ-type robots. The experiments showed that 99.775% of the test data were distributed between 30 ms and 50 ms, and the actual rotational speed was only about 250 ms. The missed and wrong capture rates were 3.58% and 0%, respectively, which indicated that the actual rotational speed was almost the same as the specified one. The results show that the manipulator designed with the NJ-based motion controller achieves a high level of accuracy and stability, and can achieve accurate grasping, which provides convenient conditions for people to perform self-service table tennis entertainment in their daily lives.

Upgrading Gryphon PUMA Robot Arm Using ROS-MoveIt

Industrial and old lab robotic equipment can be upgraded and reused invery sophisticated applications such as robot arms if provided with an efficientmodular program or framework such as ROS (Robotics Operating system). In thispaper, we replace the old control system of the 5-DOF robot arm (completeGryphon Package 35-002-c) with the help of ROS MoveIt Motion PlanningFramework package to control motion planning and generate joints angles, URDF(Unified Robot Description format) used to create the robot model for ROS. Themicrocontroller is used to move the five stepping motors to move the end effectorin the desired pose: position and orientation using ROS and MoveIt capability.Thus, the robot should be upgradeable, and anyone who has ROS basic skills canimprove the arm functionality to use it in any desired application.

Simulation Analysis of Hydraulic Control System of Engineering Robot Arm Based on ADAMS

Simulation Analysis of Hydraulic Control System of Engineering Robot Arm Based on ADAMS

Retracted: Robotic Arm Control System Based on AI Wearable Acceleration Sensor

Retracted: Robotic Arm Control System Based on AI Wearable Acceleration Sensor

Motion control of photovoltaic module dust cleaning robotic arm based on model predictive control

Carbon neutralization has become a global consensus for green development, and solar photovoltaic power generation has become one of the key technologies for carbon reduction. The presence of dust on a photovoltaic module affects power generation, so the trajectory tracking control of dust removal robotic arm for photovoltaic modules is of great significance for improving power generation efficiency. In this study, a composite trajectory tracking strategy based on model predictive control is designed to track the desired angle of each joint, which is the control objective for the trajectory tracking of the photovoltaic module's dust cleaning robotic arm. The control strategy consists of a model predictive controller and a disturbance observer. Firstly, when there is no external disturbance acting on the system, and the robotic arm model is accurate, the trajectory tracking prediction optimization problem is constructed, and an error feedback correction mechanism is introduced so that the dust cleaning robotic arm tracks the desired trajectory asymptotically. Secondly, when there are model parameter deviations, system time variation, external disturbances, or other uncertain factors, a composite control strategy is established by combining the disturbance observer and model predictive control to compensate for the effects of disturbances through feedback, thus improving the stability and accuracy of the robotic arm control system. Finally, the feasibility of the composite control tracking strategy is verified by numerical simulation. The results show that the designed predictive controller has high error control accuracy and fast solution speed, and it can realize real-time and robust trajectory tracking of the robotic arm with a constrained dust cleaning assembly.

Model verification of fallback control system under cyberattacks via UPPAAL

ABSTRACT Industrial control systems (ICS) are required to be operated safely under cyberattacks. Fallback control is necessary for the safe operation of ICS. As one of fallback control systems, we develop a resilient third-party monitoring system. This system consists of Programmable Logic Controllers (PLCs) for normal control and for fallback control. The normal PLC controls field devices, and the fallback PLC takes over the control after the normal PLC is attacked. The fallback control of this paper is the control takeover of a robot arm control system. To quickly transition to this fallback control, it is necessary to incorporate a supervisor function to manage each PLC function in an integrated manner. This paper aims to propose a modeling method of ICS functions and its analysis method to ensure that the supervisor can work properly under cyberattacks. For modeling, we use UPPAAL, specializing in formal verification by timed automata. We implement the models of each PLC and supervisor on UPPAAL. To quantitatively analyze whether the supervisor can really realize the incident response during cyberattacks, we give the specifications necessary for fallback control to the supervisor model using Timed Computation Tree Logic (TCTL) and verify its feasibility.

Robot Arm Control System for Assisted Feeding of People With Disabilities in Their Upper Limbs

This work proposes a robot arm control system for assisted feeding of people with reduced functionality in their upper limbs. It aims at improving their quality of life by helping users recover their independence when feeding, aided by the proposed system. Previous research presents solutions that often lack functionality to meet the user's needs, such as a lack of emergency functions or the use of passive feeding techniques, due to the absence of adequate human-robot interaction. The proposed solution involves the design of an interface adapter between the robot arm and the spoon, for the correct transport and positioning of the food. Moreover, a PD-type electronic controller is implemented for the robot arm; it includes gravity compensation and trajectories defined from the detection of the user's position. Additionally, the system has two safety features: an emergency button and a proximity warning that triggers when undesired objects are too close to the robot arm. The proposed system was validated through position tests and interaction with people using rice and oatmeal. When carrying out the tests with rice, 80% success was obtained, while in the case of oatmeal, 98.9% success was achieved.

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.