Articulated Robot Arm Research Articles

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards with NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multi-scale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute (LARRI), as a rapid prototyping tool through additively manufacturing a multi-layer flexible printed circuit board (FPC) with a printed strain sensor and soldered Surface Mounted Devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include Aerosol Jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and Intense Pulsed Light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a 5-DOF high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4-DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional PCB design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-micron wide lines, an insulating bridge with a thickness of around 100 microns, and SMDs soldered to silver AJP pads, were integrated within the demonstrator. An operational amplifier, and other SMDs, reduce the complexity of the accompanying control circuit and amplifies the sensor?s response by 1,830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus System.

Design and Fabrication of SCARA for Image Processing in Industry

The scope of our project is the design and fabrication of SCARA (Selective Compliance Articulated Robot Arm) robots optimized for image processing tasks within industrial environments. This report presents a comprehensive review of the design, control, and applications of SCARA robots, focusing on their integration with image processing technologies to enhance industrial automation. We delve into the underlying principles governing the kinematics and dynamics of SCARA robots, elucidating their operational mechanisms and capabilities. Advancementsin end-effector design and sensor integration have enhanced the adaptability of SCARA robots, enabling them to tackle complex tasks in unstructured environments.

Mechanical Design and Analysis of Six-Degree-of-Freedom (6-DOF) SCARA Robot for Industrial Applications

Abstract: A six-degree-of-freedom (6-DOF) SCARA robot is an advanced robotic system capable of moving and manipulating objects in a three-dimensional space with a high degree of precision and flexibility. The term "SCARA" stands for "Selective Compliance Articulated Robot Arm," indicating its design that allows a combination of rigidity and compliance along specific axes. This unique combination of features makes 6-DOF SCARA robots highly versatile and suitable for a wide range of industrial applications. Unlike traditional SCARA robots that typically have four degrees of freedom, the addition of two extra degrees of freedom enhances the 6-DOF SCARA robot's spatial reach and manipulation capabilities. This enables the robot to perform tasks that require complex orientations, intricate movements, and precise positioning within a 3D workspace. The mechanical design, kinematics, and control strategies of these robots are carefully developed to ensure accurate and efficient performance, making them valuable tools in various industries. 6-DOF SCARA robots find applications in numerous industries where precise manipulation, efficient automation, and versatile positioning are crucial.

Dynamics Simulation of a Small Satellite with a Robotic Manipulator for Additive Manufacturing

In-space manufacturing is an emerging concept that offers several benefits when compared with traditional space structures that are built on the ground and launched fully assembled. Structures manufactured in space are not limited by launch vehicle fairing volumes and do not require reinforcement to survive the ascent to orbit. Additive manufacturing is uniquely suited to this paradigm and has been proposed as a key piece of several in-space manufacturing architectures. However, the complex dynamics involved with 3D printing a structure on-orbit require additional study. Controlling a spacecraft with an attached multijointed, independently articulated robot arm is a complicated problem, and additive manufacturing is especially difficult due to the large tool paths required to complete a structure. Here, a numerical model of a free-flying small satellite with an attached robotic arm assembly is presented. Simulations for multiple 3D structures printed on-orbit and analysis of the small satellite controls required to stabilize the articulated arm are described. Multiple spacecraft control strategies are implemented, with resource utilization tracked throughout the simulation. The results demonstrate the feasibility of a small satellite platform for hosting a 3D printing robotic arm in space, and the simulation can be used to inform the design of future small satellite systems attempting complicated on-orbit manufacturing missions.

Parametric configuration and programming of flexible robotic cells producing biotechnology experiments

In the last decades there has been an ever-increasing trend of accelerated scientific research in the biotechnology sector and the COVID-19 pandemic has further emphasized the need for high-throughput yet flexible processes. Even though automation has gained significant popularity to deal with this problem, the focus has been placed at laboratory level and for specific type of processes only. The aim of these automation applications is to offset the increasing costs of clinical trials, automate tedious laboratory work, run experiments in parallel and make scientific testing efficient and programmable. This paper investigates the application of robotic systems at a larger scale that will allow the automated execution of even custom defined biotechnology experiments so that these can be offered as a service to any interested stakeholder. A digital model of the proposed robotic biotechnology workcell is developed and the necessary methodology is investigated to showcase the feasibility of this approach. The goal is to use the digital model for evaluating different design considerations, such as equipment layout and robot types, as well as for planning and simulating the robot operation under various offline programming strategies. To achieve this goal, the Damped Least Squares Method is used for inverse kinematics control and a robotic arm trajectory planner is developed parametrically using characteristic intermediate points so as to create a motion plan that can be used for any robotic manipulator and any experiment within a family of facility setups. Two experiments are simulated using a SCARA robot and an articulated arm robot, each with an alternative cell layout to show the flexibility and robustness of the proposed approach. The obtained results show that the trajectory planner can consistently generate appropriate motion plans.

Cable SCARA Robot Controlled by a Neural Network Using Reinforcement Learning

Abstract In this work, three reinforcement learning algorithms (Proximal Policy Optimization, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient) are employed to control a two link selective compliance articulated robot arm (SCARA) robot. This robot has three cables attached to its end-effector, which creates a triangular shaped workspace. Positioning the end-effector in the workspace is a relatively simple kinematic problem, but moving outside this region, although possible, requires a nonlinear dynamic model and a state-of-the-art controller. To solve this problem in a simple manner, reinforcement learning algorithms are used to find possible trajectories for three targets out of the workspace. Additionally, the SCARA mechanism offers two possible configurations for each end-effector position. The algorithm results are compared in terms of displacement error, velocity, and standard deviation among ten trajectories provided by the trained network. The results indicate the Proximal Policy Algorithm as the most consistent in the analyzed situations. Still, the Soft Actor-Critic presented better solutions, and Twin Delayed Deep Deterministic Policy Gradient provided interesting and more unusual trajectories.

Design, manufacture, and control motion of cartesian robot prototype with 5-DOF robot arm for possible spinning applications at laboratory-level

This document presents the development of a prototype of a Cartesian robot with a 5 degrees-of-freedom articulated robot arm for possible applications in a wet spinning process at the laboratory-level. The mechatronic system was developed using various modular electronics elements compatible with an ArduinoTM Mega 2560 microcontroller. The control algorithm recognizes the position of the servomotors and the speed of the motors so that the prototype performs a complete cycle of displacement in 67 s, which includes the transport of a polymeric filament immersed in a coagulation bath for 10 s. A structural analysis indicates that there will be no tension failures because the maximum and axial stress of the Cartesian robot was 2.89 MPa while the von Mises tension of the robotic arm was 468 MPa, both tensions below their upper limits. The pulse signals, in the order of 4000 +25 ms, of the servomotors were consistent in 96-98% repeatability and 3-19% reproducibility. Forty percent of the extrusion tests performed were satisfactory, since the transport of a polymeric filament within a coagulant solution was achieved.

Nonlinear optimal control for a 4-DOF SCARA robotic manipulator

AbstractSelective compliance articulated robot arms (SCARA) robotic manipulators find wide use in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-degrees of freedom (DOF) SCARA robotic manipulator. The dynamic model of the SCARA robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system, a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains, an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control method is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, it is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs and (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators. The nonlinear optimal controller for the 4-DOF SCARA robot is finally compared against a flatness-based controller implemented in successive loops.

Enhancing Disturbance Rejection of PID Controllers for DC Joint Motors of Trajectory Tracking Robots Using Disturbance Observer

In this paper, disturbance rejection of DC motor PID trajectory control systems is enhanced for independent joint control of robot arms. The concept of disturbance observer is invoked to propose a linear auxiliary control that augments existing PID controllers. The design of the auxiliary control is developed using a state space approach rather than transfer function approaches commonly employed in many existing designs derived from the concept of disturbance observer. This provides new insight and leads to a compact design requiring only two design parameters. While many of the existing DC motor trajectory control systems assume the availability of current feedback from a motor coil, the proposed auxiliary control does not. This can highly facilitate its applications in the lacking situation. Realizing that the stability of the resulting control systems could be inconvenient to assert due to increased system dimension resulting from incorporating disturbance observer, compact criteria for asserting robust stability using readily available results is given explicitly. To evaluate the capability of the auxiliary control for disturbance rejection, experimental results on a DC joint motor of an articulated robot arm are given. In presence of smooth and abrupt loading variations due to gravity, it appears that the tracking error of the enhanced system can be approximately 67% of that of the unenhanced system. This result is consistent in all three rounds of experiments.

Articulated Robot Arm for Garbage Disposal in Hospital Environment

The use of robotic arms is crucial in the medical industry, particularly in hospital settings. It can be used for a wide range of things, including as an aide in the operating room or for the removal of medical waste, among many other things. In this work, the robotic arm is designed to segregate medical waste as hazardous or non-hazardous. A dataset with five classes was created because there was no readily available data set for medical waste. The primary challenge in doing so is to programme the robotic arm’s movements and train the image dataset to classify objects as hazardous or non-hazardous. The 3D-printed robotic arm model has 6-Degree of Freedom(DOF) and is coupled with MG996R and SG90 servo motors. The robotic arm that is attached to an Arduino uno board is operated by the Blynk IoT platform. It uses YOLO V5 (You Only Look Once) algorithm to detect objects, and it favours intersection over union (IOU). To demonstrate, static robotic arm model was placed near the pile of medical waste to identify the waste and segregate it accordingly.

VIRTUAL GEOMETRIC MODEL WITH DYNAMIC PARAMETERS FOR 6 DOF ARTICULATED ARM ROBOT

To calculate joints angles of an articulated arm robot, when the coordinates of the point to be reached are known, different calculation methods or iterative algorithms for inverse kinematics (IK) can be used. IK requires that the dimensions of the robot segments and the initial positions of the joints to be known, described, and implemented mathematically, so it is based on the geometric model of the robot. In practice, the geometric modeling of the robots is done considering that all their structural elements are rigid, and their dimensions and positions are considered constant (while in reality the robots suffer certain deformations that can have different causes). This article considers the thermal deformations that a robot suffers during operation which are leading to positioning errors. The deformations are variable during the warm-up period of the robot and become constant after reaching the thermal stabilization level. From this point of view, if it is desired to consider and possibly compensate these thermal induced errors, the elaboration of the geometric model of the robot in the classical way is no longer possible and the geometric parameters must be somehow described as variables. Thermal deformations produce displacements and torsions of the robot elements. Linear and angular deviations may occur from the initial (theoretical) position in all 3 directions of the cartesian axis systems used in robot modeling. This paper presents a technique for creating a virtual model of the ABB IRB140 robot in CoppeliaSim, programming and modeling environment, with the positioning of the axis systems attached to the joints identical to the real position (unlike simplified versions of Denavit-Hartenberg geometric models) and the logic of a custom written software algorithm for automatic deformation of the model.

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

Inverse kinematics of robots is a critical topic in the robotics field. Although there are conventional ways of solving inverse kinematics, soft computing is an important technology that has lately gained prominence due to its ability to reduce the complexity of the inverse kinematics problem. This paper presents an inverse kinematics solution using multiple adaptive neuro-fuzzy inference systems (MANFIS). Different models were established by employing various methods of identification. Subtractive Clustering (SCM), Fuzzy C-Means Clustering (FCM), and Grid Partitioning (GP) are the three methods used in this study. This work is being carried out on a 5-DOF articulated robot arm, which is commonly used in industry. A mathematical model is built based on the Denavit-Hartenberg (DH) approach. Following confirmation that the kinematic findings of the mathematical model match the actual observed values of the robot arm, two types of data sets are generated: a random data set and a systematic data set based on a trajectory. The data sets are then utilized to train and evaluate ANFIS models and choose the optimal models to develop MANFIS model. Thus, the prediction and experimental data are compared to assess the performance of the MANFIS model.

Development of a hyper CLS data-based robotic interface for automation of production-line tasks using an articulated robot arm

Development of a hyper CLS data-based robotic interface for automation of production-line tasks using an articulated robot arm

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.

EDUCATIONAL 5 AXIS ROBOT CONTROLLER OPTIMIZATION USING ARM HARDWARE INSTRUCTIONS

This paper presents a programming method of a Raspberry Pi controller for a 5-axis articulated arm robot. The goal is optimization of direct kinematics calculations, based on the universal approach for direct kinematics method. Matrix multiplication is implemented using ARM hardware instructions. The main advantage of this method is lower computation time, which means faster robot response time. Programmability of the robot is done by means of a custom-made instruction set, called RASM.

Development of Extended Super Dragon Articulated Robot Arm with a Linear Telescopic Boom

Development of Extended Super Dragon Articulated Robot Arm with a Linear Telescopic Boom

Precision optimized pose and trajectory planning for vertically articulated robot arms

Vertically articulated robot arms are widely used in various industries for the realization of conventional handling tasks due to their high flexibility, good workspace-to-footprint ratio and low price. However, due to their kinematic structure, their accuracy, precision and stiffness are highly dependent on the robots configuration. Standards such as EN-ISO-9283 define procedures for determining accuracy and precision by averaging measurements at different points in the robot’s main workspace. While these averages can be used to estimate the robots overall performance, they do not support process developers in identifying precision optimal poses. This paper presents a generic and comprehensive routine for the combined identification of precision-optimal robot poses and trajectories for pick and place processes. The routine consists of several cascading optimization loops with the objectives of minimizing the structural and dimensional sensitivity costs of the trajectories’ start and end configurations, minimizing the number of actuated joints and minimizing the required direction changes of the joints in motion. Using a sampling-based trajectory planner (OMPL in MoveIt! / ROS), a smooth and collision free trajectory for an exemplary pick and place process is generated and experimentally validated for a KUKA LWR iiwa 14. The optimized trajectory is benchmarked against a conventional, unoptimized reference trajectory for the same handling task. The experimental validation shows a significant increase in the repeatability of the positioning of 36µm (at 3σ) compared to both the nominal repeatability of 150µm and the repeatability of the reference trajectory of 94µm (at 3σ, 2.6-fold increase). Transferring the developed routine to inherently more precise robot models will lead to highly precise vertically articulated robot arms capable of solving more demanding assembly tasks.

Position/Force Visual-Sensing-Based Robotic Sheet-Like Peg-in-Hole Assembly

This article deals with a sheet-like peg-in-hole robotic assembly task which is usually done in manual operation. Thin-walled deformable objects will introduce some special uncertainties in robotic peg-in-hole assembly, which may make this kind of robotic assembly difficult. To deal with this issue, three four-phase selective compliance articulated robot arm (SCARA)-based peg-in-hole assembly strategies which imitate manual assembly with a hybrid switching force/position control scheme are proposed. Both position/force visual sensing methods are proposed based on a single-lens imaging system in an eye-to-hand configuration, respectively. Based on the developed visual sensing techniques, an iterative learning control (ILC) scheme is designed to approximate the synchronous position control between a SCARA robot and a two-degree of freedom (DoF) hybrid compliant gripper. The experiments are implemented on our built-up SCARA-based robotic peg-in-hole (peg: electrode ear, hole: plastic shell) assembly platform. The results indicate that the visual force sensing nonlinearity is about 3% at the full range, and the proposed robotic assembly strategies are suited to this kind of peg-in-hole assembly from industrial scenarios.

Articulated Robot Arm

In medical rehabilitation programs, trajectory tracking is used to increase the repeatability of joint movement and the patient's recovery in the early phases of rehabilitation. In order to achieve that, the robotic arm has been implemented since it can provide a precise and move in almost perfect motion. This manuscript aim to develop and simulate a 2DOF robotic arn that will able to tracking the trajectory successfully. Hence, in order to achieved that a modeling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm is being discussed in this manuscript. First, the robot specifications, as well as Robot Kinematics forward and inverse kinematics of a 2-DOF robot arm, are provided. The dynamics of the 2-DOF robot arm were then formulated in order to obtain motion equations by using the Eular-Lagrange Equation. For the controller of the robot, a control design was created utilising a PID controller. All the data is recorded from the margin of error as well as the overshoot and peak settling time is being record via matlab. The data is differentiate by with with controller, with PI and PID, in which the error is less than 12.5 and 1.63 consecutively. The data that being gathered show that a controller best suited in this rehabilitation robot

A novel constraint tracking control with sliding mode control for industrial robots

As industrial robots are characterized by flexibility, load variation, and unknown interference, it is necessary to develop a control strategy with strong robustness and adaptability, fast convergence rate, and simple structure. Sliding mode control is a special method widely used to handle nonlinear robot control. However, the existing control law for sliding mode control has limitations in the chattering and convergence rate. The sliding mode manifold and reaching law are firstly discussed in this article. In the meanwhile, a proposed control law for sliding mode control combining linear sliding mode manifold and double-power reaching law is developed, which is based on the robot dynamic equation derived by the Udwadia–Kalaba theory. Furthermore, a compared control law for sliding mode control combining linear sliding mode manifold with exponential reaching law is presented to test the proposed control law for sliding mode control. The comparison indicates that the proposed law effectively improves the performance in convergence rate and the chattering of constraint tracking control. Finally, the two control laws for sliding mode control are applied to the Selective Compliance Articulated Robot Arm robot system with modeling error and uncertain external disturbance to demonstrate the merit and validation of the proposed scheme.