DOF Robotic Arm Research Articles

Robotic Arm controlled using IoT application

Work-from-home policies have been the standard since the worldwide pandemic breakout, and this has spurred the fast development of applications in the area of IoT for remotely monitoring and managing applications. This has encouraged us to design and develop a remotely controlled robotic arm that can be used in applications where the engaging human hazardous environment (such as quarantined rooms of COVID affected patients) is dangerous. This has led to the development of a B-rover called a robotic arm, which the technicians remotely control to reduce the direct contact between the technician and the hazardous environment. It has various applications, such as a health monitoring system for monitoring the patient's health conditions, sample collection from the patients and the capability of the Robotic Arm to deliver medications to the COVID affected patients without engaging humans. It is proposed to design a 3DOF(degrees of freedom) robotic arm with stepper motor which is controlled through Wi-Fi using the BlynkIoT App with widgets like Joystick and Sliders. This will pick and drop the objects from one place to another. The results show that the designed robotic arm shows a 3% variation from the simulated and actual results when the slider is adjusted.

Prioritized Hindsight with Dual Buffer for Meta-Reinforcement Learning

Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve multi-task manipulation problems. This is mostly due to the problems associated with efficient exploration in high-dimensional state and continuous action spaces. Furthermore, in multi-task scenarios, the problem of sparse reward and sample inefficiency of DRL algorithms is exacerbated. Therefore, we propose a method to increase the sample efficiency of the soft actor-critic (SAC) algorithm and extend it to a multi-task setting. The agent learns a prior policy from two structurally similar tasks and adapts the policy to a target task. We propose a prioritized hindsight with dual experience replay to improve the data storage and sampling technique, which, in turn, assists the agent in performing structured exploration that leads to sample efficiency. The proposed method separates the experience replay buffer into two buffers to contain real trajectories and hindsight trajectories to reduce the bias introduced by the hindsight trajectories in the buffer. Moreover, we utilize high-reward transitions from previous tasks to assist the network in easily adapting to the new task. We demonstrate the proposed method based on several manipulation tasks using a 7-DoF robotic arm in RLBench. The experimental results show that the proposed method outperforms vanilla SAC in both a single-task setting and multi-task setting.

Hybrid additive robotic workcell for autonomous fabrication of mechatronic systems - A case study of drone fabrication

• A hybrid additive manufacturing / pick-n-place robotic workcell enables autonomous, flexible fabrication of full-scale functional mechatronic systems. • A modular electronics concept is demonstrated to enable error-free autonomous, robotic construction of full-scale mechatronic sub-systems. • The workcell's functionality is verified through the back-to-back manufacture of different drone designs that are able to fly directly from the build platform. • Functional drones are rapidly manufactured at the point of need in under four hours This paper presents the realization of a robotic workcell capable of autonomous, flexible, fabrication of complete, full-scale, functional mechatronic systems. Using a 6-DOF robotic arm with a hybrid additive manufacturing (AM)/pick-and-place (PnP) end-effector, the workcell can rapidly print enclosures, insert and wire functional electronics, and embed them into the printed structure. The workcell's scalable control system, digital workflow for creating assembly instructions, hybrid AM/PnP toolhead design, and autonomous part tending mechanism are detailed. In addition, a modular electronics infrastructure is presented to enable facile, error-free robotic wiring of large-gauge copper wire. The workcell's ability to manufacture is verified in a case study wherein it is used to autonomously fabricate a variety of Unmanned Aerial Systems (UAS). In the study, the workcell produces a functional quadrotor drone, releases the drone from the workcell, and allows the drone to record the workcell constructing a second, different drone - all without human involvement. The resulting workcell, which combines digitally-integrated manufacturing, robotics and automation, and hybrid process manufacturing, enables autonomous manufacture of complete, functional mechatronic systems that are tailored to custom design needs in a just-in-time manner.

Bayesian Disturbance Injection: Robust imitation learning of flexible policies for robot manipulation

Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterization of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.

Design A Precision Motion Control of an Upper Limb Robotic Arm

Trajectory tracking is utilized in medical rehabilitation programs at the early stage of rehabilitation in order to track the performance of the patient in performing the prescribed task. The robotic arm has been utilized to accomplish this due to it precision and provide repetitive motion. The goal of this study is to design and simulate a two-degree-of-freedom robotic arm that can effectively track a trajectory. As a result, this study discusses the modelling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm to attain that goal. First, the robot specifications are provided, as well as the forward and inverse kinematics of a 2-DOF robot arm. The dynamics of the 2-DOF robot arm were then defined using the Euler- Lagrange Equation to obtain motion equations. A PID controller was used to construct a control design for the robot's controller. MATLAB is used to record all the data, including the margin of error, overshoot, and peak settling time. The data is identified using the PI and PID controllers, in which the error is smaller than 7 and 1.5, respectively. The controller was then used to create a prototype model by using MATLAB Sim Mechanics. The data obtained indicates that a PID controller is the best fit for this rehabilitation robot.

Sensorimotor Control Sharing With Vibrotactile Feedback for Body Integration Through Avatar Robot

An avatar robot can be operated by multiple users, augmenting the operation of a single user. In this study, we assembled a collaborative operation system for a 7-DoF robotic arm with a gripper controlled by two users. The users share the role of controlling the robot with one user controlling the arm and the other controlling the gripper. The actions of the two users must be seamlessly coordinated to ensure smooth and precise operations. Therefore, this study aimed to investigate vibrotactile feedback to promote recognition of the actions of a partner. A pick-and-place task was considered as a basic operation. First, an intensity adjustment was developed for vibrotactile feedback to ensure linear sensitivity and its usefulness was verified. An estimation test for the reaching motion showed that the target position was estimated accurately based on velocity feedback, indicating that the participants imaged the arm motion. Subsequently, collaboration tests with and without vibrotactile velocity feedback revealed a potential effect of vibrotactile feedback in terms of reducing the time required for completing the target task. Our experimental results indicate that vibrotactile feedback for partner's motion can be applied to achieve smooth collaborative operations using an avatar robot.

Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

The presence of robots that can assist humans with heavy or dangerous work makes the need for robots more pressing at the moment. One type of robot needed is a robot arm, which is widely used in the manufacturing industry, such as in the assembly process and pick and place. The types of robotic arms used vary both in terms of configuration and the number of degrees of freedom. However, with different types of robotic arms, different models of movement are used. Therefore, research related to the modeling of the robotic arm continues to be carried out to obtain the appropriate movement of the robotic arm. One of the methods used as a first step in designing a robotic arm movement model is kinematics analysis. Kinematics analysis aims to analyze the movement of the robot arm without knowing what force causes the movement. This paper aims to produce an ideal movement model for the AX-12A 3-DoF robotic arm using forward kinematic and inverse kinematic analysis using two methods, the Denavit-Haterberg method and the geometric approach method. The difference from other papers is that this paper makes the kinematics model using Robotic, Vision, and Control (RVC) tools based on the Peter I. Corke model on MATLAB software first before implementing it on hardware. The results show that the error percentage for the forward kinematic model is 1.04% and the inverse kinematic is 0.76%, which means the two models achieved the target that the model’s error maximum must be less than 2%.

Energy efficient reactionless design of multi-arm space robot for a cooperative handshake maneuver

Space robots play a significant role in on-orbit capture, space structure construction, and assembly tasks. Since the robotic arms are attached to a free-floating satellite, the motion of the manipulator in such tasks and the satellite are coupled. Multiple-arm space robots can perform complex cooperative tasks and are superior to single-arm space robots. Current work proposes a reactionless manipulation algorithm for a multi-robotic arm based on the iterative Newton–Euler method for space robots with many task and balance arms. The present work demonstrates two tasks and one balance arm to perform a reactionless handshake maneuver in space. This maneuver is presented in detail for a planar and spatial case. The planar case uses 3 DoF robotic arms, while the spatial case uses 6 DoF robotic arms. In addition, the balance arm has been designed considering the efficient usage of energy satisfying reactionless manipulation concept. The design procedure focuses on minimizing energy used during the motion of the balance arm for a known motion of task arms using a genetic algorithm. Moreover, computational experiments are conducted to validate the use of the genetic algorithm for optimization. The results of proposed reactionless manipulation algorithm have been validated with the results available in the literature for the spatial case that uses a different method. In the future, an energy-efficient balance arm will be designed to handle tumbling objects.

Forward kinematic analysis of 5-DOF LYNX6 robotic arm used in robot-assisted surgery

Robotic surgery or robot-assisted surgery, allows doctors to conduct a wide range of complex procedures. It’s precision, flexibility, and control are more efficient than traditional approaches. A robotic arm is the most commonly used clinical robotic surgical system worldwide. A typical robotic arm includes an arm for camera and/ or mechanical arm for variety surgical instruments attached to it. They are like human arms but with improved efficiency of payload handling. The mapping of a kinematic model with accuracy is the most critical part of a robotic manipulator in order to achieve these demanding expectations. Various researchers in the literature have made substantial contributions in this direction. In 1995, Denavit and Hartenberg proposed the famous D-H parameters technique. This approach is used in the kinematic analysis of a five-DOF spatial manipulator called the 5-DOF Lynx6 Robotic Arm, in the current study. The proposed study includes a comparative analysis of forward kinematics of the 5-DOF Lynx6 Robotic Arm using analytical and computational approaches. The analytical method will include the representation of the end-effector using a transformation matrix and the same is compared computationally by using MATLAB. This study derives a comprehensive analytical solution using matrix algebra to the forward and inverse kinematics of a 5-DOF robotic arm. The proposed strategy is simple yet effective to implement in practice. A number of numerical simulations were performed to verify this strategy. A simulation and visualization of the workspace of the robotic arm is also done using MATLAB. This workspace shows us the set of points reached by the end-effector giving us an understanding of the scope of the robot and its reach and mobility for different types of surgery. As an outcome of this study, we found that the Lynx-6 robotic arm can be used in robot assisted laparoscopy and its effectiveness can be further increased with few modifications.

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

A robotic arm is the most often used robot in manufacturing to perform the same task accurately again and over again in a controlled environment. However, direct positioning control of the robotic arm is always inaccurate as it does not consider its position in the external environment. Therefore, this work implements the Mamdani fuzzy logic in the position control of the 6 DOF robotic arm to improve the accuracy and movement of each joint at this manipulator. The implemented fuzzy logic Mamdani inference system is done in MATLAB and finally converted to the C language to accommodate the main microcontroller, Arduino UNO environment. There are 6 servo motors controlled by this approach and the result is compared with the conventional method. It is found that the Mamdani fuzzy logic controller has an average error of 0.67% while the direct control method has an average error of 2.33%. Based on this result, it shows that the use of the Mamdani fuzzy logic increases the accuracy and reduces the swaying movement after stopping the robotic arm movement.

A Fully Geometric Approach for Inverse Kinematics of a Six-Degree-of-Freedom Robot Arm

This paper presents an analytical solution to inverse kinematics problem of a 6-DOF robot arm based on geometric method. The solution only utilizes relations between vectors in frames attached in the robot arm. Experiments with the solution are conducted to test the approach and results show that the correctness and accuracy of the solution are verified.

Utilisation of Initialised Observation Scheme for Multi-Joint Robotic Arm in Lyapunov-Based Adaptive Control Strategy

In this paper, we present a modelling, dynamic analysis, and controller tuning comparison for a five-degree-of-freedom (DoF) multi-joint robotic arm based on the Lyapunov-based Adaptive Controller (LAC). In most pick-and-place applications of robotic arms, it is essential to control the end-effector trajectory to reach a precise target position. The kinematic solution of the 5-DoF robotic arm has been determined by the Lagrangian technique, and the mathematical model of each joint has been obtained in the range of motion condition. The Proportional-Integral-Derivative (PID) control parameters of the LAC have been determined by the Lyapunov stability approach and are initialised by four observation methods based on the obtained transfer function. The effectiveness of the initialised controller’s parameters is compared by a unit step response as the desired input of the controller system. As a result, the average error (AE) for Ziegler–Nichols is 6.6%, 83%, and 53% lower than for Pettit & Carr, Chau, and Bucz. The performance of LAC for the robotic arm model is validated in a virtual 3D model under a robot operating system environment. The results of root mean square error by LAC are 0.021 (rad) and 0.025 (rad) for joint 1 and joint 2, respectively, which indicate the efficiency of the proposed LAC strategy in reaching the predetermined trajectory and the potential of minimizing the controller tuning complexity.

Experiment assisting system with local augmented body (EASY-LAB) In dual presence environment

<p class="Abstract">This paper introduces a system designed to support conducting experiments of subjects when the situation does not allow experimenter and subject to be in the same place such as the COVID19 pandemic where everyone relied on video conference applications which has its limitation. Due to the difficulty of directing with a video conferencing system using solely video and voice.</p><p class="Abstract">The system we developed allows an experimenter to actively watch and interact with the subject. Even if you're operating from a distant area, it is still possible to conduct experiments. Another important aspect this study will focus on is the case of when there are several subjects required and the experimenter must be able to guide both subjects equally well. The system proposed uses a 6 DoF robotic arm with a camera and a laser pointer attached to it on the subject side. The experimenter uses a head-mounted display to control it and it moves corresponding to the head movement allowing for easy instruction and intervention to the subject side. Comparison with other similar research is also covered. The study will focus mainly on which viewing method is the easiest for the experimenter to use, and if teaching one subject at the time gives better results than teaching two subjects simultaneously.</p>

Servo Motor Controller using PID and Graphical User Interface on Raspberry Pi for Robotic Arm

The usage of the robotic arm in industries has been growing as the robotic arm can provide many advantages to the industries. However, the usage of the robotic arm has many challenges faced by the industries. One of the problems is the error of the angle position caused by the movement of the robotic arm’s joint which affects the precision of the desired position of the end-effector. Therefore, this paper aims to study the solution of the PID controller to increase the accuracy of the position of the end-effector in the X-Y plane using forward kinematic. It describes the mechanical system, design concept, and the prototype of a 6 DOF robotic arm. Initially, the angle error of the joint for 2D plane movement is captured manually using the trigonometric calculation. Then, to reduce this error, the coefficient of the PID controller, k p , k i , and k d are tuned automatically using Simulink and MATLAB. Then, the controller is programmed on a Raspberry Pi with a servo driver, PCA9685. There are six servo motors used as the joints of the robotic arm, namely MG996R. Each servo motor can be controlled by using a Graphical User Interface (GUI) scripted in Python Programming Language. The end position of the end-effector will be displayed on this GUI. It is found that the angle position error of the 6 DOF robotic arm for X-Y plane movement is reduced from 4.7° to 0° by using the PID controller.

Robot-Enabled Uterus Manipulator for Laparoscopic Hysterectomy With Soft RCM Constraints: Design, Control, and Evaluation

Laparoscopic hysterectomy is a common minimally invasive gynecologic surgery in which an assistant is required to manipulate the uterus during the procedure according to the verbal commands of the primary surgeon. However, uterus manipulation is a lengthy and laborious task, where human fatigue may lower operating safety. This paper presents a novel robot-enabled uterus manipulation system that provides accurate, tireless, and direct uterus manipulation. The system consists primarily of a 7-DoF robotic arm tailored for uterus manipulation in laparoscopic hysterectomy. The primary surgeon can directly control the robot using a footswitch pedal to move within the constraint of the Remote Center of Motion (RCM) via software and achieve the desired range of pitch and yaw motion. An additional rotational motion around the instrument axis is provided for twisting the uterus to enhance the exposure of the ligaments and peritoneum for resection. In addition, the robot arm consists of 7 compact modular actuators that provide the target payload for holding the uterus while maintaining a small footprint in a constrained operating environment. To facilitate operation, a passive mobile base is provided for supporting and positioning the robot. A 6-D force sensor is equipped that allows the assistant to quickly guide the robot into the work area and move the robot under RCM constraint using an admittance control approach. The uterus manipulation rod can be easily detached from the robot body by a quick pluggable mechanism for preoperative sterilization. A quick pluggable mechanism was proposed for easy separation and mounting of the uterus manipulation rod from the robot body for preoperative sterilization. Experiments were performed to measure the performance of the modular joints, RCM repeatability, and evaluate the feasibility of the robot in the simulated laparoscopic hysterectomy.

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller.

A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm

In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model.

Inverse Kinematics Analysis of Novel 6-Dof Robotic Arm Manipulator for Oil and Gas Welding Using Grey Wolf Algorithm

his paper presents a grey-wolf algorithm for solving the inverse kinematics of a newly designed 6-degree-of-freedom robotic arm for oil and gas pipeline welding which has not been used in the literature. Consequently, due to the robot’s multiple joints with compounding combinatory possibilities of joint angles, analysis of the inverse kinematics is relatively complex. In this research, grey-wolf algorithm, a swarm-based meta-heuristic algorithm, has been used to solve for the inverse kinematics of the robotic arm with respect to tracking a rectangular trajectory with six sets of waypoints in the 3D [X, Y, Z] space. The results were further analyzed in terms of the accuracy of the position of end effector from the accurate position of the rectangular target trajectory via a mean squared error cost function. Furthermore, results of comparison between the grey-wolf algorithm and the particle swarm optimisation, an alternate swarm algorithm with respect to position error from the inverse kinematics task is also presented. The results obtained via simulation clearly demonstrates the superior performance of the grey-wolf algorithm compared to particle swarm optimisation with respect to the solving an inverse kinematics task

Simulation of robotic space operations with minimum base reaction manipulator

Autonomous robotic capture has been identified as a key technology for On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions. However, manoeuvring spacecraft-mounted manipulators is a challenging task since it generates disturbance torques on the satellite. To mitigate this problem several minimum reaction control strategies have been developed to reach the desired End-Effector (EE) pose while minimizing the dynamic disturbances transferred to the spacecraft by the robotic arm.This paper presents the development of a Simulation Tool in the MATLAB/Simulink environment capable of simulating the dynamics of a satellite equipped with a 7-DoF robotic arm during the target capture phase. The manipulator, as well as the spacecraft, is implemented by using Simscape Multibody and joint actuators are modelled as Brushless DC motors controlled by PID controllers. The spacecraft attitude is Nadir-Pointing, it is controlled by means of quaternion feedback and Linear-Quadratic-Regulator (LQR) and it is actuated by three Reaction Wheels (RW). Orbital perturbations such as non-spherical gravity potential (EGM2008 model) and atmospheric drag are considered. In addition, the minimum reaction control strategy called Kinetic Energy Minimization (MKE) is employed during the robotic arm manoeuvres.The goal of this study is to compare the performances obtained with the MKE method with those achieved by using the classic Inverse Kinematics (IK) in the free-flying case. The numerical results confirmed that MKE method is to be preferred since it minimizes the control torque that the Attitude Control Subsystem (ACS) must provide and reduces the EE orientation and position errors.

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.