Robot Arm Motion Research Articles

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example.

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people’s daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.

Adverse Events Associated With Robotic-Assisted Joint Arthroplasty: An Analysis of the US Food and Drug Administration MAUDE Database

BackgroundThe use of robotic assistance in arthroplasty is increasing; however, the spectrum of adverse events potentially associated with this technology is unclear. Improved understanding of the causes of adverse events in robotic-assisted arthroplasty can prevent future incidents and enhance patient outcomes. MethodsAdverse event reports to the US Food and Drug Administration Manufacturer and User Facility Device Experience database involving robotic-assisted total hip arthroplasty (THA), total knee arthroplasty (TKA), and partial knee arthroplasty were reviewed to determine causes of malfunction and related patient impact. ResultsOverall, 263 adverse event reports were included. The most frequently reported adverse events were unexpected robotic arm movement for TKA (59/204, 28.9%) and retained registration checkpoint for THA (19/44, 43.2%). There were 99 reports of surgical delay with an average delay of 20 minutes (range 1-120). Thirty-one cases reported conversion to manual surgery. In total, 68 patient injuries were reported, 7 of which required surgical reintervention. Femoral notching (12/36, 33.3%) was the most common for TKA and retained registration checkpoint (19/28, 67.9%) was the most common for THA. Although rare, additional reported injuries included femoral, tibial, and acetabular fractures, MCL laceration, additional retained foreign bodies, and an electrical burn. ConclusionDespite the increasing utilization of robotic-assisted arthroplasty in the United States, numerous adverse events are possible and technical difficulties experienced intraoperatively can result in prolonged surgical delays. The events reported herein seem to indicate that robotic-assisted arthroplasty is generally safe with only a few reported instances of serious complications, the nature of which seems more related to suboptimal surgical technique than technology. Based on our data, the practice of adding registration checkpoints and bone pins to the instrument count of all robotic-assisted TJA cases should be widely implemented to avoid unintended retained foreign objects.

User-oriented Natural Human-Robot Control with Thin-Plate Splines and LRCN

We propose a real-time vision-based teleoperation approach for robotic arms that employs a single depth-based camera, exempting the user from the need for any wearable devices. By employing a natural user interface, this novel approach leverages the conventional fine-tuning control, turning it into a direct body pose capture process. The proposed approach is comprised of two main parts. The first is a nonlinear customizable pose mapping based on Thin-Plate Splines (TPS), to directly transfer human body motion to robotic arm motion in a nonlinear fashion, thus allowing matching dissimilar bodies with different workspace shapes and kinematic constraints. The second is a Deep Neural Network hand-state classifier based on Long-term Recurrent Convolutional Networks (LRCN) that exploits the temporal coherence of the acquired depth data. We validate, evaluate and compare our approach through both classical cross-validation experiments of the proposed hand state classifier; and user studies over a set of practical experiments involving variants of pick-and-place and manufacturing tasks. Results revealed that LRCN networks outperform single image Convolutional Neural Networks; and that users' learning curves were steep, thus allowing the successful completion of the proposed tasks. When compared to a previous approach, the TPS approach revealed no increase in task complexity and similar times of completion, while providing more precise operation in regions closer to workspace boundaries.

Novel technique for control of industrial robots with wearable and contactless technologies

Wearable and non-contact sensor technologies have spread to many diverse areas with their ease of use and application, and the variety of applications has been increasing rapidly thanks to technological developments in the recent years. This work ensures the usability of wearable and non-contact sensor technologies in various industrial fields. In this study, it is aimed that operators can easily control industrial robots in real-time without the need for a specialized programming environment. For this purpose, a new human–robot interface, in which industrial robots can be controlled by hand movements, has been created. In this interface, Leap Motion Controller (LMC), which uses an IR Camera for the control of robot arm positioning and motion trajectories, and Myo armband, which detects wrist and hand movements with EMG sensors, is used to control of robot end effector (tool) actions. By means of both devices, robot movements are controlled by industrial communication after hand movements, classification and data processing stages. The data processing, control and positioning results of the platform developed in the article are presented in comparison with similar studies in the literature. As a result, it has been observed that the developed platform works with high accuracy. EMG classification success was determined as 96.9% and hand tracking error between 2.45–2.85 mm. Thanks to this new platform, various robotic processes such as pick-place, object movement, painting applications, etc. will be carried out easily and flexibly without the need for programming.

Robot arm navigation using deep deterministic policy gradient algorithms

ABSTRACT In this paper, the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is employed to enable a double-jointed robot arm to reach continuously changing target locations. The experimentation of the algorithm is carried out by training an agent to control the movement of this double-jointed robot arm. The architectures of the actor and cretic networks are meticulously designed and the DDPG hyperparameters are carefully tuned. An enhanced version of the DDPG is also presented to handle multiple robot arms simultaneously. The trained agents are successfully tested in the Unity Machine Learning Agents environment for controlling both a single robot arm as well as multiple simultaneous robot arms. The testing shows the robust performance of the DDPG algorithm for empowering robot arm manoeuvring in complex environments.

Cable-path optimization method for industrial robot arms

The production line engineer's task of designing the external path for cables feeding electricity, air, and other resources to robot arms is a labor-intensive one. As the motions of robot arms are complex, the manual task of designing their cable path is a time-consuming and continuous trial-and-error process. Herein, we propose an automatic optimization method for planning the cable paths for industrial robot arms. The proposed method applies current physics simulation techniques for reducing the person–hours involved in cable path design. Our method yields an optimal parameter vector (PV) that specifies the cable length and cable-guide configuration via filtering the candidate PV set through a cable-geometry simulation based on the mass–spring model. The proposed method offers two key features: 1) Increased computational efficiency via an optimization procedure that separates the entire cable into the cable segments. In the proposed method, the entire cable is segmented at the positions of the cable guides into several separate cable segments, and the PVs of the cable segments that satisfy the constraints of collision, stretch, and curvature radius are filtered into the local optimal PV set. The global optimal PV is obtained by finding the combination of the local optimal PVs which have the same guide configuration between the adjacent cable segments and have minimal total length of the adjacent cable segments. 2) Robustness to external disturbances, such as fluctuation in the physical properties of the cables and the accuracy of manually attaching the cables. The PVs of the local optimal PV sets are required to satisfy the above constraints, even if the cable length changes in the predefined range, which ensures the robustness of the obtained cable path. To verify the validity of the proposed method, we obtain the global optimal PVs by applying the method to several pick-and-place motions of a six-axis vertical articulated robot arm in our simulations and implement the cable paths on an actual robot arm based on the obtained PVs. Our results indicate that the proposed method can aid line engineers to efficiently design the cable paths along robot arms.

Development of a Comprehensive Design Guideline to Evaluate the User Experiences of Meal-Assistance Robots considering Human-Machine Social Interactions

ABSTRACT Meal-assistance robots (MARs) help feed meals to users with disabilities or who need help consuming their meals. Although such MARs have been introduced in various fields, guidelines for evaluating MARs are limited. This study aims to develop comprehensive guidelines to assess the user experience (UX) of MAR designs considering its social interaction characteristics between humans and robots. Participants from three groups (patients, doctors, and caregivers) with different perspectives on MARs were recruited and a focus group interview was conducted to collect their UXs with MARs. The three groups showed different UXs with MARs in user interface design, robot arm motion, and safety and mobility. In addition, based on the literature review, eight UX features (usability, emotion, value, naturalness, assistance, acceptance, personality, and culture) are proposed to evaluate MAR interfaces. The proposed comprehensive design guideline will be particularly useful in evaluating and designing the UX of MARs.

Controlling by Showing: i-Mimic: A Video-Based Method to Control Robotic Arms

A novel concept of vision-based intelligent control of robotic arms is developed here in this work. This work enables the controlling of robotic arm motion only with visual input, that is, controlling by showing the videos of correct movements. This work can broadly be sub-divided into two segments. The first part of this work is to develop an unsupervised vision-based method to control robotic arms in 2-D plane, and the second one is with deep convolutional neural network (CNN) in the same task in 3-D plane. The first method is unsupervised, where our aim is to perform mimicking of human arm motion in real-time by a manipulator. We developed a network, namely the vision-to-motion optical network (DON). Given the input of a video stream containing the hand movements of human on the DON, the velocity and torque information of the hand movements shown in the video would be generated as the output. The output information of the DON is then fed to the robotic arm by enabling it to generate motion according to the real hand videos. The method has been tested on both live-stream video feeds as well as on recorded video obtained from a monocular camera even by intelligently predicting the trajectory of the human hand when it gets occluded. This is why the mimicry of the arm incorporates some intelligence to it and becomes an intelligent mimic (i- mimic). Furthermore, to enhance the performance of DON and make it applicable to mimic multi-joint movements with n-link manipulator, a deep network, namely, CNN has been used along with a refiner network as the predecessor of DON. Refiner network has been used to overcome the limitations of inadequate labelled data. Both the proposed methods are validated with off-line as well as with on-line video datasets in real-time. The entire methodology is validated with real-time 1-link and simulated n-link manipulators (an arm with n number of different joints) along with suitable comparisons.

Simplifying Robotic Pancreato-Jejunostomy

Robotic surgical system has brought about great advantages in minimally invasive pancreatic surgery. Especially, fine and articulated movements of robotic arms make it much easier to perform pancreato-jejunostomy (PJ) during pancreatoduodenectomy (PD), compared to a laparoscopic approach.

Automatic interpolation algorithm for NURBS trajectory of shoe sole spraying based on 7-DOF robot

PurposeThe purpose of this paper is to propose an automatic interpolation algorithm for robot spraying trajectories based on cubic Non-Uniform Rational B-Splines (NURBS) curves, to solve the problem of sparse and incomplete trajectory points of the head and heel of the shoe sole when extracting robot motion trajectories using structured-light 3D cameras and to ensure the robot joints move smoothly, so as to achieve a good effect of automatic spraying of the shoe sole with a 7-degree-of-freedom (DOF) robot.Design/methodology/approachFirstly, the original shoe sole edge trajectory position points acquired by the 3D camera are fitted with NURBS curves. Then, the velocity constraint at the local maximum of the trajectory curvature is used as the reference for curve segmentation and S-shaped acceleration and deceleration planning. Immediately, real-time interpolation is performed in the time domain to obtain the position and orientation of each point of the robot motion trajectory. Finally, the inverse kinematics of the anthropomorphic motion of the 7-DOF robot arm is used to obtain the joint motion trajectory.FindingsThe simulation and experiment prove that the shoe sole spraying trajectory is complete, the spraying effect is good and the robot joint movement is smooth, which show that the algorithm is feasible.Originality/valueThis study is of good practical value for improving the quality of automated shoe sole spraying, and it has wide applicability for different shoe sole shapes.

A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

Evaluation of a custom-designed human-robot collaboration control system for dental implant robot.

The purpose of this study is to develop a methodology to better control a human-robot collaboration for robotic dental implant placement. We have designed a human-robot collaborative implant system (HRCDIS) which is based on a zero-force hand-guiding concept and a operational task management workflow that can achieve highly accurate and stable osteotomy drilling based on a surgeon's decision and robotic arm movements during implant surgery. The HRCDIS brings forth the robot arm positions, exact drilling location, direction and performs automatic drilling. The HRCDIS can also avoid complex programing in the robot. The purpose of the study is to evaluate the accuracy of drilling resulting from our developed operational task management method (OTMM). The OTMM can enable the robot to switch, pause, and resume drilling tasks. The force required for hand-guiding in a zero-force control mode of the robot was detected by a 6D force sensor. We compared our force data to those provided by the manufacturer's manual. The study was conducted on a phantom head with a 3D-printed jaw bone to verify the validity of our HRCDIS. We appraised the discrepancies between free-hand drillings and the HRCDIS controlled drillings at apical centre and head centre of the implant and implant angulation to the baseline data from a virtual surgical planning model. The average required force used by hand-guiding to operate the robot with HRCDIS was near 7 Newton which is much less than the manufacturer's specification (30 Newton). The results from our study showed that the average error at implant head was 1.04±0.37mm, 1.56±0.52mm at the implant apex, and deviation of implant angle was 3.74±0.67°. The results from this study validate the merit of the human-robot collaboration control by the HRCDIS. Based on the improved navigation system using HRCDIS, a robotic implant placement can provide seamless drilling with ease, efficiency and accuracy.

Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.

Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning

The use and application of robotic arms in helping the aged and vulnerable persons are increasing gradually. In order to achieve safer and reliable human-robot interaction and its wider adoption, the requirements for the humanoid motion of robotic arms are becoming more stringent. This paper presents a humanoid motion planning method for a robotic arm based on the physics of human arm and reinforcement learning. Firstly, the humanoid motion rules are extracted by analyzing and learning the action data of human arm, which is collected using the VICON optical motion capture system. Then, according to the acquired features and rules, the corresponding reward functions are proposed and the humanoid motion training of the robotic arm is carried out by using the reinforcement learning based on Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) algorithm. Finally, the experiments are carried out to verify whether the robotic arm motions planned by the proposed approach are humanoid, and the observed results show its feasibility and effectiveness in planning the humanoid motion of the robotic arm.

Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility.

In 2019, the University of Warmia and Mazury in Olsztyn, in cooperation with Astri Polska, started a European Space Agency (ESA) project. The purpose of the project is the development and implementation of a field calibration procedure for a multi-frequency and multi-system global navigation satellite system (GNSS). The methodology and algorithms proposed in the project are inspired by the “Hannover” concept of absolute field receiver antenna calibration; however, some innovations are introduced. In our approach, the antenna rotation point is close to the nominal mean phase center (MPC) of the antenna, although it does not coincide with it. Additionally, a National Marine Electronics Association local time zone (NMEA ZDA) message is used to synchronize the robot with the GNSS time. We also propose some modifications in robot arm movement scenarios. Our first test results demonstrate consistent performance for the calibration strategy and calibration procedure. For the global positioning system (GPS) L1 frequency, the calibration results show good agreement with the IGS-type mean values. For high satellite elevations (20°–90°), the differences do not exceed 1.5 mm. For low elevation angles (0°–20°), the consistency of the results is worse and the differences exceed a 3 mm level in some cases.

Multi-degree-of-freedom robot arm motion simulation based on MATLAB

The design is divided into three main parts. The first part is an analysis of the basis robotic arm components and principles to understand how the robotic arm is precisely and automatically controlled to achieve the desired task. The kinematic basis of the robotic arm is then investigated by establishing a co-ordinate system for the arm based on the modified D-H method. A preliminary kinematic model of the 6 degree of freedom robotic arm is established through a structural study of ABB's multi-degree-of-freedom robot. The basic parameters of the robotic arm are brought into the equation to obtain its equations of motion, and then a simulation study is carried out using MATLB to find the forward and inverse solutions, and the results are compared with the previous study to verify their reasonableness. The second part is based on the description and analysis of the work space in the first part, and the methods for solving the work space are investigated. These methods are also compared to analyse and understand their applicability and reasonableness. Finally, the path description and generation of the robotic arm is studied to complete the planning of the robotic arm trajectory and the results of the simulation are analysed. The study of the 6-degree-of-freedom robot arm is used to improve the theoretical basis of the robot. The study of the 6-degree-of-freedom robotic arm provides a deeper understanding of the structural parameters of the robot arm and adds to the missing knowledge for the next study of intelligent robotics, as well as to the research and application of the robotic arm or to further research based on it.

Automatic Control of Color Sorting and Pick/Place of a 6- DOF Robot Arm

This work focuses on the implementation and design of a six degree of freedom, 6-DOF control of automatic color sorting and pick and place tasks for a robot arm using wireless controlling interface – Blynk apps. Based on the collaboration between the servo motor and input color sensor, this wireless control of automatic color sorting robot arm provides a powerful wireless control GUI (Graphics User Interface) and to enable the method for manual color sorting mode. The color sorting mode is performed once the mode is turned on by the user. The robot arm able to differentiate the colors of the object (input) and categorize or classify the object to the correct places automatically. It provides a stable, efficient, and precision results without any vibration of control as the main target for this project. In this work, six servo motors were used to realize the real robotic arm for industrial use. Five servos were operated to control the entire robot arm motion including the base, shoulder, and elbow as well as one servo is reserved for the positioning of the end effector. Two input variables namely TSC3200 Color Sensors & HC-SR04 Ultrasonic Sensors were employed as the input for the robot arm. The output variable mainly focused on the servo motor as the links for the robot arm to reposition and change the motion for the entire system.

Forward and Inverse Kinematics Demonstration using RoboDK and C#

Purpose: Robot researchers need a simulator to understand better the algorithm on path planning, arm movement, and many more. They need a good simulator. RoboDK is an excellent simulator to fulfill the research work. It has calibration facilities, so it is industrial-grade software. Its forward and inverse kinematics accuracy is better than any competing software. The main advantage is all robots under one IDE. When we use an industrial robot, and we must use their software environment to operate the robot. But the RoboDK covers most of the robots and runs under one roof. And we need to learn only one IDE. The RoboDK online library is full of the standard robot. And all robot’s operation procedure is the same. So, the learning curve of new robots is easy. It is easy to simulate, and it can connect with a practical robot to execute the task. Using this software, we can quickly create digital twins for the industry. Now we think about control the robot from our application. When we use to control the robot from an external environment or remote software, we need the use the API to control the robot. Here we will see how easily we can operate the robot from our custom application. We adopted RoboDK C# API and integrated it into Visual studio using a User interface to control the robot movement. Keeping this research as a reference, the robotic arm researcher can add value to their research. Our primary purpose is to shorten the learning curve to integrate the RoboDK with their custom application. Design/Methodology/Approach: Taking the RoboDK C# API they provided, we customized it according to our purpose with minimal components. After developing a graphical user interface, we interact through API. Then, opening both RoboDK IDE and C# application, we can send the End effector position using the sliding movement. Findings/Result: After our research, we found that RoboDK is a good IDE for our research on the robotics arm. We can easily integrate the C# API they provided with our custom application for research purposes. Originality/Value: If we want to test robotic arm movement in the simulator, we need an excellent simulator like RoboDK. Integrating the RoboDK C# API is a little bit time-consuming. Using our approach, the researcher can continue their research in a minimal period. And find adequate information here to integrate easily into their project. Paper Type: Simulation-based Research.

Control of Satellites with Onboard Robotic Manipulators

Free-floating satellites with onboard robotic manipulators are subjected to widely varying loads resulting from the motion of the robotic manipula-tors. As there are no fixed supports in space, these loads will cause the satellite to move. By modelling the motion of the onboard robotic arms, determin-ing the necessary reaction loads (which must be sup-plied by the satellite to keep the arm fixed), and sim-ulating the resulting satellite dynamics, we designed a model of a satellite-arm system. We found that a Proportional-Integral-Derivative (PID) control scheme, with disturbance-estimating capabilities, was effective in maintaining satellite position and ori-entation during the operation of the onboard ro-botic manipulator. The MATLAB-based Simulink modeling environment was used to perform the sim-ulations of satellite dynamics and control.

Application of image recognition in workpiece classification

With the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion to achieve automatic classification. First, we upload captured images to a PC for classification process and use chess patterns to conduct a sampling test. Next, when the system identifies these patterns as proper chess patterns, the robotic arm grabs the objects and moves them to designated locations. The project is divided into two main sections: image recognition and robotic arm motion. In the image recognition section, we use Keras and the Tensorflow open source learning machine to build a convolutional neural network model. Then, we use a learning model network that is a considerably more compact variant of the VGGNet network in the image recognition system. With this model, we achieve a recognition accuracy of 95%. In the robotic arm section, we use a five-axis robotic arm and an Arduino Uno board as the controller. We design the Denavit–Hartenberg parameters of the arm and calculate the direct (inverse) kinematics parameters to plan its trajectory. Thereafter, we use MATLAB software to simulate prototype processes, such as grabbing, moving, and placing. Finally, we import the program into the controller so that the robotic arm can execute classification based on the chess pattern.