Robotic Arm In Order Research Articles

Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production

Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.

Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic-Swarm Optimization for the Inverse Kinematic Solution.

It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic–Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional–Integral–Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic–Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.

A fast intracortical brain–machine interface with patterned optogenetic feedback

Objective. The development of brain–machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback. Approach. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1) and delivers feedback simultaneously via a low-latency, high-refresh-rate, spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging. Main results. We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying solely on the artificial inputs and outputs of the closed-loop BMI. Significance. This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications.

Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly $98$ % given different tests with different types of residuals and duct structures.

Interactive Plan Execution during Human-Robot Cooperative Manipulation

Abstract Collaborative robots (Cobots) are robotic systems designed to physically interact with humans during task execution in a shared industrial workspace. In this paper, we consider a scenario in which a human operator can be supported by a lightweight robotic arm in order to accomplish complex manipulation tasks. Specifically, we assume that manipulation tasks are explicitly represented as hierarchical task networks to be interactively executed exploiting the human physical guidance. In this setting, the human interventions are continuously interpreted by the robotic system in order to infer whether the human guidance is aligned or not with respect to the planned activities. The interpreted human interventions are then exploited by the robotic system to adapt its cooperative behavior during the execution of the shared plan. Depending on the estimated operator intentions, the robotic system can adjust tasks or motions, while regulating the robot compliance in order to follow or lead the human co-worker. The proposed approach is demonstrated in a testing scenario consisting of a human operator that interacts with a Kuka LBR iiwa arm in order to perform a cooperative manipulation task. The collected experimental results show the feasibility and the effectiveness of the approach.

EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine (SVM) classifier to control a robot arm in order to help a person to find an object in the environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform (CWT) and the empirical modal decomposition (EMD), combined with the principal component analysis (PCA) that were fed through a linear and radial basis function (RBF) kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction.

A dynamic model for GPS based attitude determination and testing using a serial robotic manipulator

A computational algorithm is developed for estimating accurately the attitude of a robotic arm which moves along a predetermined path. This algorithm requires preliminary input data obtained in the static mode to yield phase observables for the precise, 3-axis attitude determination of a swinging manipulator in the dynamic mode. Measurements are recorded simultaneously by three GPS L1 receivers and then processed in several steps to accomplish this task. First, artkconv batch executable converts GPS receiver readings into RINEX format to generate GPS observables and ephemeris for multiple satellites. Then baseline vectors determination is carried out by baseline constrained Least-Squares Ambiguity Decorrelation (LAMBDA) method that uses double difference carrier phase estimates as input to calculate integer solution for each baseline. Finally, attitude determination is made by employing alternatively Least-squares attitude determination (LSAD) in the static mode and extended Kalman filter in the dynamic mode. The algorithm presented in this paper is applied to recorded data on Mitsubishi RV-M1 robotic arm in order to produce attitude estimates. These results are confirmed by another set of Euler angles independently evaluated from robotic arm postures obtained along the predefined trajectory.

Design and Analysis of Electronic Controller Based Robot Arm

Modern industrial robots are designed according to the principal design rules to construct light and stiff. When the controllers are used in industrial robots, the problem arises due to the repetitive motion and technical aspects of the controller. In this work, we proposed an alternative electronics controller design for the robotic arm in order to withstand various load conditions. The PIC controller is used to control the robot motion and to reduce time delay of motion and to analyze the performance of the prototype on various load conditions. In this paper, a robotic arm that serves as a tool to lift an object from one place to another where it can be widely used in factory is developed. The main focus of this work is to design, develop and to implement robot arm with enhanced control with stumpy cost. The proposed system is actuated by 3 DC motors and the motion of the robot arm is controlled by PIC micro controller. The robot arm is designed with two degree of freedom and talented to accomplish accurately simple tasks, such as light material handling, which will be integrated into a mobile platform that serves as an assistant for industrial work force. The designed arm lifts up to 2 kg object.

A Proposal for Automatic Fruit Harvesting by Combining a Low Cost Stereovision Camera and a Robotic Arm

This paper proposes the development of an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm placed in the gripper tool. The stereovision camera is used to estimate the size, distance and position of the fruits whereas the robotic arm is used to mechanically pickup the fruits. The low cost stereovision system has been tested in laboratory conditions with a reference small object, an apple and a pear at 10 different intermediate distances from the camera. The average distance error was from 4% to 5%, and the average diameter error was up to 30% in the case of a small object and in a range from 2% to 6% in the case of a pear and an apple. The stereovision system has been attached to the gripper tool in order to obtain relative distance, orientation and size of the fruit. The harvesting stage requires the initial fruit location, the computation of the inverse kinematics of the robotic arm in order to place the gripper tool in front of the fruit, and a final pickup approach by iteratively adjusting the vertical and horizontal position of the gripper tool in a closed visual loop. The complete system has been tested in controlled laboratory conditions with uniform illumination applied to the fruits. As a future work, this system will be tested and improved in conventional outdoor farming conditions.

RK- Methods for Robot Application problems

The significance, is to introduce a novel way to employ the improved Runge-Kutta fifth order five stage method, here after called as Modified IRK(5,5) method, for system of second order robot arm problem and variations in angles at the joints in which parameters governing with two degrees of freedom which requires lesser number of function evaluations per time step as compared to the existing ones, in order to save time and spaceAn ultimate aim of this present paper is to solve application problem such as robot arm and initial value problems by applying Runge-Kutta fifth order five stage numerical techniques. The calculated output for robot arm coincides with exact solution which is found to be better, suitable and feasible for solving real time problems.

Visual evoked potential-based brain–machine interface applications to assist disabled people

This paper describes a brain–computer interface (BCI) based on electroencephalography (EEG) that has been developed to assist disabled people. The BCI uses the evoked potentials paradigm (through P300 and N2PC waves detection), registering the EEG signals with 16 electrodes over the scalp. Three applications have been developed using this BCI paradigm. The first application is an Internet browser that allows to access to Internet and to control a computer. The second application allows controlling a robotic arm in order to manipulate objects. The third application is a basic communication tool that allows severe disabled people to interact with other people using basic commands related to emotions and needs. All the applications are composed by visual interfaces that show different options related to the application. These options are pseudo-randomly flickering in a screen. In order to select a specific command, the user must focus on the desired option. The BCI is able to obtain the desired option by detecting the P300 and N2PC waves produced as an automatic response of the brain to attended visual stimuli and finally classifying these signals. Different experiments with volunteers have been carried out in order to validate the applications. The experimental results obtained as well as the improvement achieved by using both types of evoked potentials are shown in the paper.

Self-Learning for a Humanoid Robotic Ping-Pong Player

Imitating the learning process of a human playing ping-pong is extremely complex. This work proposes a suitable learning strategy. First, an inverse kinematics solution is presented to obtain the smooth joint angles of a redundant anthropomorphic robot arm in order to imitate the paddle motion of a human ping-pong player. As humans instinctively determine which posture is suitable for striking a ball, this work proposes two novel processes: (i) estimating ball states and predicting trajectory using a fuzzy adaptive resonance theory network, and (ii) self-learning the behavior for each strike using a self-organizing map-based reinforcement learning network that imitates human learning behavior. Experimental results demonstrate that the proposed algorithms work effectively when applied to an actual humanoid robot playing ping-pong.

New approach for a Reconfigurable Autonomous Underwater Vehicle for Intervention

This shows an on-going project named RAUVI (i.e., Reconfigurable AUV for Intervention). This project aims to design and develop an Underwater Autonomous Robot, able to perceive the environment by means of acoustic and optic sensors, and equipped with a robotic arm in order to autonomously perform simple intervention tasks. A complete simulation environment, including this new concept of robot, has been developed and is presented as a preliminary result.

System of second order robot arm problem by an efficient numerical integration algorithm

A robot arm problem solved by adapting RK-sixth order algorithm is presented in this paper. Results and comparison show the efficiency of the RK-sixth order algorithm based on the absolute error between the exact and approximate solutions at different time intervals. In the present paper, a corrective measure has been taken to obtain the stability polynomial, the ranges for the real part of λh and graphical representation of the stability regions for the case of RK-Butcher algorithm, obtained by Sekar et al. (2004), Murugesan et al. (2004, 2005), Murugesh and Murugesan (2009) and Park et al. (2004a, 2004b). It is found that the error involved in the numerical solution obtained by RK-sixth order algorithm is less in comparison with that obtained by the RK-fifth order and RK-fourth order algorithms respectively.

An efficient numerical solution for system of second order robot arm problem

The aim of this paper is focused on providing numerical solutions for system of second order robot arm problem using the RK-eight stage seventh order limiting formulas. The parameters governing the arm model of a robot control problem have also been discussed through RK-eight stage seventh order limiting algorithm. The precised solution of the system of equations representing the arm model of a robot has been compared with the corresponding approximate solutions at different time intervals. Results and comparison show the efficiency of the numerical integration algorithm based on the absolute error between the exact and approximate solutions. Based on the numerical results, a thorough comparison is carried out between the numerical algorithms. It is observed that RK-eight stage seventh order limiting algorithm is accurate, efficient compared with RK fifth order algorithm and RK-sixth order algorithms.

Control of a compliant two-axis robotic arm

It is common to make the links between actuators and robotic limbs as stiff as possible, in complete contrast to natural systems, where compliance is present. In the past, to create some compliance in a drive, springs have been added to the link between the actuator and load. Many of these springs have been in series with the drive, but recently a more ‘biological’ approach has been taken where two springs have been used in parallel to counteract each other. This paper describes the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed, along with various control strategies.

Motion control of the satellite mounted robot arm which assures satellite attitude stability

When a robot arm is mounted on a satellite to perform some tasks, the satellite’s attitude must be stabilized to retain the communication link and to generate electrical power from solar panels. It is not realistic to control the total system as one dynamic system, since the number of degrees of freedom becomes too large to be handled by state-of-the-art satellite mounted computers. This paper proposes a coordinated control between the satellite’s attitude control system and the robot-arm control system. The robot-arm control system estimates the angular momentum of the planned robot-arm’s motion. The satellite’s attitude control system will compensate for the reaction by using feed-forward control. The robot-arm controller also manages the motion plan of the robot arm in order not to disturb the satellite’s attitude stability.

衛星塔載ロボットアームと衛星姿勢の協調制御 ロボットアーム動作時の衛星の姿勢安定の保証

When a robot arm is mounted on a satellite to perform tasks, the satellite attitude must be maintained to retain the communication link and to generate electrical power from its solar pannels. It is not realistic to control the total system as one dynamic system, since the number of degrees of freedom becomes large, and the computational requirement for the satellite-mounted computer becomes stringent. The proposed control method used independent control systems for control of the motion of the robot arm and satellite attitude control. The robot arm control system estimates the angular momentum which will be produced by the robot arm motion, and the attitude control system compensates for the disturbance by using the feedforward control. The robot controller also manages the motion plan of the robot arm in order not to disturb the satellite's attitude stability.

Coordinated control of spacecraft attitude and space manipulators

When a robot arm is mounted on a satellite to perform some tasks, the satellite's attitude must be stabilized to retain the communication link and to generate electrical power from solar panels. It is not realistic to control the total system as one dynamic system, since the number of degrees of freedom becomes too large to be handled by state-of-the-art satellite-mounted computers. This paper proposes a coordinated control between the satellite's attitude control system and the robot arm control system. The robot arm control system estimates the angular momentum of the planned robot arm's motion. The satellite's attitude control system will compensate for the reaction by using feed-foward control. The robot arm controller also manages the motion plan of the robot arm in order not to disturb the satellite's attitude stability.

Nonlinear Robot ARM Control Through Third Order Motor Model

Design of robot controllers are typically based on the second order model of rigid robot arms. However, the robot joint motor dynamics constitutes an important part of the overall robot dynamics, and should be included in robot controller design. A third order robot dynamic model is obtained in this paper which includes the robot arm dynamics as well as the actuator dynamics. Differential geometric control theory is used to linearize and decouple the resultant nonlinear and coupled dynamic system, yielding linear subsystems for each degree of freedom in the robot task space. Linear system control theory and optimal control theory are then used to obtain desired system performance. Extensive computer simulations are carried out using the joint actuators. They demonstrate that the controller designed in this paper produces excellent steady state as well as transient system performance, and it is robust in the presence of robot model parameter inaccuracy. The importance of robot motor dynamics is further illustrated by computer simulations, where a controller based on the second order robot arm dynamical model is adopted to control the third order robot dynamics and produces unacceptable transient state response.