Robot Arm Motion Research Articles

Assistive Humanoid Robot Arm Motion Generation in Dynamic Environment Based on Neural Networks

Assistive Humanoid Robot Arm Motion Generation in Dynamic Environment Based on Neural Networks

Temporal Logic Inference with Prior Information: An Application to Robot Arm Movements

Temporal logics are widely used to express (desired) system properties in controller synthesis and verification. In linear temporal logics, the semantics of the formulae are defined on the execution trajectories of the system. Recently, there have been a lot of interest in using dense-time linear temporal logic, such as Signal Temporal Logic (STL) in characterizing system trajectories. In this paper, we present a new method to derive an STL formula that characterizes the motion of a robot arm. Our work generalizes earlier work in this area by (i) allowing the use of polyhedral predicates, and (ii) incorporating a priori knowledge about the predicates. The formula is defined by a set of parameters, whose values are determined by minimizing a cost function that balances the trade-off between the formula's match with the trajectories and the the similarity between its predicate and an a priori known predicate. We apply our algorithm on experimental trajectories generated using a PHANToM Omni robot.

Research on the Control Strategy of Eye-to-Hand Target Track Real-Time Tracking System

This paper presents an Eye-to-hand system, designed for hand-eye system presents a automatically real-time tracking of moving targets control strategy. The first strategy uses a color mark on the target, the use of binocular stereo vision system for color measurement and then in the visual system of the target three-dimensional reconstruction to give the three-dimensional coordinates in the visual coordinate system when the target position and orientation when changes according to the amount of change in the three-dimensional coordinates of the direct control of the robot arm motion of each joint, enabling the tracking of moving targets.

Simulation of Control of a Scara Robot Actuated by Pneumatic Artificial Muscles Using RNAPM

This paper describes the simulation of movement control of a one-degree-of-freedom articulated robot arm SCARAactuated by a pair of McKibben pneumatic artificial muscles. The pneumatic artificial muscle is the actuator andemulates the behavior of biological muscles; due to its nonlinear behavior, there is also a need to develop controlsystems for robot arms using this type of actuator. Research begins with the transfer function that represents, inmathematical language, the movement of the robot arm’s joints; this allows using a PID controller on the transferfunction and generating data to train the Multilayer Perceptron Artificial Neural Network (RNAPM). So far, the PIDcontrol system has been able to control the movement of robot arms but, based on experimental tests, the RNAPMhas proved to outperform the PID control’s response time by up to 2.95 seconds, minimizing the angular error by 1.3°and avoiding the oscillation problem due to its continuous, constant behavior.

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Robotic phrenic nerve harvest: A feasibility study in a pig model

The aim of this study was to report on the feasibility of robotic phrenic nerve harvest in a pig model. A surgical robot (Da Vinci S™ system, Intuitive Surgical®, Sunnyvale, CA) was installed with three ports on the pig's left chest. The phrenic nerve was transected distally where it enters the diaphragm. The phrenic nerve harvest was successfully performed in 45 minutes without major complications. The advantages of robotic microsurgery for phrenic nerve harvest are the motion scaling up to 5 times, elimination of physiological tremor, and free movement of joint-equipped robotic arms. Robot-assisted neurolysis may be clinically useful for harvesting the phrenic nerve for brachial plexus reconstruction.

Humanoid Robot Arm Kinematics Modeling and Motion Planning

This paper establishes the kinematics model of humanoid robot arm, the arm forward kinematics equations were built and solved, based on the advantages of CCD (Cyclic Coordinate Descent) and BFS (Broyden-Fletcher-Shanno) algorithm to solve the inverse kinematics of humanoid robot arm. In the joint space, using cubic polynomial and quintic polynomial interpolation method respectively for each joint angle interpolation. Cubic polynomial trajectory planning can meet the point-to-point movement in general, but can not guarantee the continuity of acceleration of each point; Quintic polynomial trajectory planning can ensure that each point is continuous of the joint angle, angular velocity, and angular acceleration, so this polynomial method can meet the movement of the humanoid robot arm.

Vision Based Pick and Place Robot for Sorting Objects Using MATLAB

In recent years, vision system in robotics have a major impact on research field and industry. Many industrial applications have a huge requirement of sorting out the things as per different color or label. This paper deals with an automated material handling system, which synchronizes the movement of robotic arm to pick a single object from the maximum numbers. This paper aims to sort out different kind of objects by their color occupied pin code and place them to its respective pre-programmed place.

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.

Error modeling and sensitivity analysis of a parallel robot with SCARA(selective compliance assembly robot arm) motions

Parallel robots with SCARA(selective compliance assembly robot arm) motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

Visual Analysis of Trajectories in Multi‐Dimensional State Spaces

AbstractMulti‐dimensional data originate from many different sources and are relevant for many applications. One specific sub‐type of such data is continuous trajectory data in multi‐dimensional state spaces of complex systems. We adapt the concept of spatially continuous scatterplots and spatially continuous parallel coordinate plots to such trajectory data, leading to continuous‐time scatterplots and continuous‐time parallel coordinates. Together with a temporal heat map representation, we design coordinated views for visual analysis and interactive exploration. We demonstrate the usefulness of our visualization approach for three case studies that cover examples of complex dynamic systems: cyber‐physical systems consisting of heterogeneous sensors and actuators networks (the collection of time‐dependent sensor network data of an exemplary smart home environment), the dynamics of robot arm movement and motion characteristics of humanoids.

An $\bm{O(n\log n)}$ Shortest Path Algorithm Based on Delaunay Triangulation

In Euclidean and/or λ-geometry planes with obstacles, the shortest path problem involves determining the shortest path between a source and a destination. There are three different approaches to solve this problem in the Euclidean plane: roadmaps, cell decomposition, and potential field. In the roadmaps approach, a visibility graph is considered to be one of the most widely used methods. In this paper, we present a novel method based on the concepts of Delaunay triangulation, an improved Dijkstra algorithm and the Fermat points to construct a reduced visibility graph that can obtain the near-shortest path in a very short amount of computational time. The length of path obtained using our algorithm is the shortest in comparison to the other fastest algorithms with O(n log n) time complexity. The proposed fast algorithm is especially suitable for those applications which require determining the shortest connectivity between points in the Euclidean plane, such as the robot arm path planning and motion planning for a vehicle.

Robust Adaptive Observer for Single-Linked Robotic Arm Systems

A robust adaptive observer was proposed for single-linked robotic arm systems, where all the parameters in the robotic arm motion equation are treated to be unknown. Under certain assumptions, the proposed observer ensures that the estimation error is uniformly bounded in the presence of unmodeled dynamics, unknown nonlinearities, bounded disturbances and unknown parameters. Furthermore, the estimation error can be made arbitrarily small by choosing appropriately design parameters. Simulation results illustrated the effectiveness of the proposed observer.

A Novel Teaching System for Industrial Robots

The most important tool for controlling an industrial robotic arm is a teach pendant, which controls the robotic arm movement in work spaces and accomplishes teaching tasks. A good teaching tool should be easy to operate and can complete teaching tasks rapidly and effortlessly. In this study, a new teaching system is proposed for enabling users to operate robotic arms and accomplish teaching tasks easily. The proposed teaching system consists of the teach pen, optical markers on the pen, a motion capture system, and the pen tip estimation algorithm. With the marker positions captured by the motion capture system, the pose of the teach pen is accurately calculated by the pen tip algorithm and used to control the robot tool frame. In addition, Fitts' Law is adopted to verify the usefulness of this new system, and the results show that the system provides high accuracy, excellent operation performance, and a stable error rate. In addition, the system maintains superior performance, even when users work on platforms with different inclination angles.

Adaptive arm motion generation of humanoid robot operating in dynamic environments

Purpose – The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach – The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions; minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered. Findings – The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions. Research limitations/implications – The motion generated yield good results in both simulation and experimental environments. Practical implications – The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion. Originality/value – A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.

Toward Flexibility in Sensor Placement for Motion Capture Systems: A Signal Processing Approach

Human body motion can be captured by body area sensor networks. Accurate sensor placement with respect to anatomical landmarks is one of the main factors determining the accuracy of motion-capture systems. Changes in position of the sensors cause increased variability in the motion data, so isolating the characteristic features that represent the most important motion patterns is our concern. As accurate sensor placement is time-consuming and hard to achieve, we propose a signal processing technique that can enable salient data to be isolated. By using functional principal component analysis (f-PCA), we compensate for the variation in data due to changes in the on-body positioning of sensors. More precisely, we investigate the use of f-PCA for filtering and interpreting motion data, whilst accounting for variability in the sensor origin. Data are collected through a marker-based motion capture system from two designed experiments based on human body and robot arm movement. Results show differences between similar actions across different sessions of marker wearing with random changes in position of sensors. After applying the f-PCA filter on the data, we show how uncertainties due to sensor position changes can be compensated for.

Touch-Less Java Based Hand Tracking System to Control an Artificial Arm

Modern sensing technologies create new possibilities to control mobile robots without any dedicated manipulators. In this article authors present a novel method that enables driving of the Mindstorms NXT artificial arm with Microsoft Kinect, using gesture recognition and hand tracking. To imitate the movement of an artificial robotic arm, an algorithm of the human-computer interaction is employed using skeleton tracking and gesture control in 3D space. The solution is implemented using free, open source software, Java based.

3P2-V03 Kinectを利用したロボットアームの制御(非接触センシング)

The purpose of this study is the realizing of natural industrial robot arm control method by tracking the operator's arm motion. In this study, we detect subject's motion of the arm using skeleton tracking of the Kinect. Using the results of this motion tracking motion of the industrial robot arm was controlled. In this study, we suggest two methods for the robot arm controlling. One is the gesture control method which controls a robot arm according to pre-decided hand or arm motions, and the other is the motion tracking method which controls a robot arm as same as hand or arm motions. Each method were tested using our developed control system. In this paper we report the results of evaluation for the two methods.

Minimizing Energy Consumption for Robot Arm Movement

Robots are widely used in industry due to their efficiency and high performance. Many of them are operating in the manufacturing stage of the production line where the highest percentage of energy is consumed. Therefore, their energy consumption became a major focus for many robots manufacturers and academic research groups. Nevertheless, the optimization of that consumption is still a challenging task which requires a deep understanding of the robot's kinematic and dynamic behaviors. This paper proposes an approach to develop an optimization module using Matlab® to minimize the energy consumptions of the robot's movement. With the help of Denavit-Hartenberg notation, the approach starts first by solving the inverse kinematics of the robot to find a set of feasible joint configurations required to perform the task, solving the inverse kinematics is usually a challenging step which requires in-depth analyses of the robot. The module then solves the inverse dynamics of the robot to analyze the forces and torques applied on each joint and link in the robot. Furthermore, a calculation for the energy consumption is performed for each configuration. The final step of the process represents the optimization of the calculated configurations by choosing the one with the lowest power consumption and sends the results to the robot controller. Three case studies are used to evaluate the performance of the module. The experimental results demonstrate the developed module as a successful tool for energy efficient robot path planning. Further analyses for the results have been done by comparing them with the ones from commercial simulation software. The case studies show that the optimization of the location for the target path could reduce the energy consumption effectively.

An Empirical Framework for Controlling Artificial Hand Gripper System Using Smart Glove

A human hand is a very complex grasping architecture which can handle objects of different sizes and shapes. When such important feature is lost, the replacement artificial hand should capable to imitate the real hand capability, hence ensuring user comfort. This paper proposes a system to control multi finger grippers with emphasis on the finger tips and finger joints. It consists of two modules namely smart glove in master module and hand gripper in slave module. The former responsible to infer user control command while the latter controlling the robot arm movement according to the user instruction. In master module, the system comprises combination of flex sensors and force sensor mounted under the glove to determine fingers bending angle and the force value. Such information is useful to control the artificial gripper for grasping objects in various shapes with right amount of force. In the slave module, the robot arm consist combination of power window motor and servos to initiate the arm and finger movement. Experimental results have shown the feasibility of the proposed system for controlling the artificial gripper and arm precisely according to the user command.