DOF Robot Manipulator Research Articles

Low-cost vision-based embedded control of a 2DOF robotic manipulator

Simulations are frequently used to validate underactuated control of the planar two-link manipulator, a typical benchmark system. These new control strategies need to be evaluated on a mechanical setup in order to further research their validity. In this work the benchmark system is specifically designed and built to fit on a table and to be as low-cost as possible. One of the large discrepancies between the simulator and the real setup is the presence of friction. With the help of a carefully designed bearing topology and by using image vision to measure the states of the system, as opposed to mechanical sensors, this friction can be kept to a minimum. The image vision requires a computer that is capable to process the images and send the control effort to the actuator in a timely manner. For this purpose a Raspberry Pi 4 is used to interface with the camera. The architecture consists of a traditional Raspbian operating system, which is expanded with Robot Operating System (ROS) and Xenomai. With the latter, tasks can be done in hard real-time, which is needed to achieve a fixed sampling time. In this case, a sampling time of 9ms is reached. An open-loop and closed-loop experiment with PD-controller are performed to validate the software architecture.

Precision and processing speed comparison of control methods for 6-DOF robot manipulator

Precision and processing speed comparison of control methods for 6-DOF robot manipulator

Control of Unstable Systems Using a 7 DoF Robotic Manipulator

Robotic manipulators are widely used in industrial applications, and their rigidity and flexibility are very important factors during their deployment. However, their usage is not limited to repetitive point-to-point tasks and can be used for more real-time control of various processes. This paper uses a 7-degrees-of-freedom manipulator to control an unstable system (Ball and Plate) as a proof of concept. The Ball and Plate system is widely used for testing algorithms designed for unstable systems, and many recent works have dealt with robotic manipulators as a control motion system. Robots are not usually used to control unstable systems, but bipedal robots are an exception. This paper aims to design a controller capable of stabilizing an unstable system with solid robustness while keeping actuator action values as low as possible because these robots will be indented to work for a prolonged time. An algorithm for an LQ polynomial controller is described and designed, and the whole setup is tested for ball stabilization in the center. The results show that the designed controller stabilizes the ball even with large external and internal disturbances while keeping the controller effort as low as possible.

Design of robotic manipulator for painting purposes in automotive industry

This paper presents design of 6-DOF robotic manipulator for painting in automotive industry. The NX-CAD was employed as the software to model the robotic manipulator. Three questions were considerably taken into account before modelling the robotic manipulator and they include the following: ‘how to define the problem the design of manipulator is going to solve?’; ‘what is the purpose of the manipulator construction?’ and ‘what were the exact requirements?’, as first , second and third questions respectively. The robotic manipulator analysis showed that the manipulator is more effective in achieving task; it is faster than human efforts and in its interaction with the environment. Through this, it will help reduce health hazard associated with humans that do work of painting by them not having direct contact with the paint (which is produced by chemicals) anymore but engage in handling other paint delivery equipment. Correspondingly, the designed painting manipulator will create cost-effectiveness of labour in automotive industry.

A Robust Controller Design for Simple Robotic Human Arm

Nowadays, the manipulator of two degrees of freedom (2DOF) has many applications. One is a human arm that may be utilized in robotic rehabilitation. The 2DOF controlled robot manipulator usually acts like human arms. This paper aims to design a robust, stable controller for the upper limb robotic model. A sliding mode control (SMC) approach is proposed to realize stability, tracing accuracy, and robustness for 2DOF robotic manipulator. Based on the general manipulator equation of motion, two SMCs are designed. The first is designed according to the input–output stability constraints. The second is designed according to the adaptive law. Not only the trajectory tracking is guaranteed but also stability is ensured. The stability of the controllers is examined based on Lyapunov stability criteria. The controllers and the robotic arm are formulated analytically. The MATLAB platform is adopted to examine and validate the proposed controller’s performance. The addition of adaptation law in the SMC scheme improves the results for the two designed controllers and shows remarkable trajectory tracking and system stability as well. The improvement rate shows an enhancement of 40.5% and 36.7% for manipulator joints 1 and 2, respectively.

Visual servoing control schemes of 4 DOF robot manipulators

Visual servoing control schemes of 4 DOF robot manipulators

Finite-Time Adaptive Event-Triggered Control for Robot Manipulators With Output Constraints

This brief investigates the tracking control of robot manipulators with output constraints. A novel finite-time adaptive event-triggered command filtered backstepping scheme is given, in which the command filters are utilized to avoid the direct differentials of virtual control signal and the compensation mechanism is used to eliminate filtering errors. The barrier Lyapunov functions are employed to ensure the outputs are constrained in the predefined region, and the finite-time control with fraction-power signals can help the closed-loop system get better convergence rate and steady-state performance. Moreover, the relative threshold-event-triggered mechanism is used to reduce the resource waste and communication burden effectively. A simulation model of 2-DOF robot manipulator is employed to demonstrate the proposed algorithm.

GR-ConvNet v2: A Real-Time Multi-Grasp Detection Network for Robotic Grasping.

We propose a dual-module robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We present an improved version of the Generative Residual Convolutional Neural Network (GR-ConvNet v2) model that can generate robust antipodal grasps from n-channel image input at real-time speeds (20 ms). We evaluated the proposed model architecture on three standard datasets and achieved a new state-of-the-art accuracy of 98.8%, 95.1%, and 97.4% on Cornell, Jacquard and Graspnet grasping datasets, respectively. Empirical results show that our model significantly outperformed the prior work with a stricter IoU-based grasp detection metric. We conducted a suite of tests in simulation and the real world on a diverse set of previously unseen objects with adversarial geometry and household items. We demonstrate the adaptability of our approach by directly transferring the trained model to a 7 DoF robotic manipulator with a grasp success rate of 95.4% and 93.0% on novel household and adversarial objects, respectively. Furthermore, we validate the generalization capability of our pixel-wise grasp prediction model by validating it on complex Ravens-10 benchmark tasks, some of which require closed-loop visual feedback for multi-step sequencing.

Prediction of Joint Acceleration of 2 DOF Robot Manipulator Using Supervised Learning

Robo-analyzer (RA) is open-source software that uses a 3D representation of a robot manipulator to carry out various analytical studies. It was created primarily to assist instructors and students in getting started with robotics teaching and learning utilizing framework-based skeleton models or computer aided design (CAD) software designs of serial robots i.e., articulated robot. The RA software is used in this work to simulate and examine a two-degree of freedom (DOF) robot with two link and two revolute joints respectively. The joint length is kept constant at 0.2m, and the joint velocity is varied from 0 to 180 degrees per second. The two-link manipulator is permitted to carry out forward kinematics after generating and establishing the input parameters for the simulation of the 2DOF model, which results in simulating the joint acceleration values, and that is the primary prerequisite for the machine learning (ML) process. The model tends to deduce the relationship between the input and output parameters in this study, which further aids in the deduction of a linear relationship between the two parameters, especially input and output parameter i.e., link length coordinates, joint velocity, and joint acceleration. The experimentation was then carried out on the basis of RA data to apply linear regression machine learning technique (LRMLT), which will assist in the prediction of an output, namely joint acceleration. The model tends to pave way for future research which can be carried out for joint vibration which is solely based on the basis of the acceleration present at joint.

An Active Fault-Tolerant Control Based on Synchronous Fast Terminal Sliding Mode for a Robot Manipulator

To maintain the safe operation and acceptable performance of robot manipulators when faults occur inside the system, fault-tolerant control must deal differently uncertainties and disturbances, especially with the occurrence of loss-effective faults. Therefore, in this paper, an active fault-tolerant control for robot manipulators based on the combination of a novel finite-time synchronous fast terminal sliding mode control and extended state observer is proposed. Due to the internal constraints of the synchronization technique, the position error at each actuator simultaneously approaches zero and tends to be equal. Therefore, the proposed controller can suppress the effects of faults and guarantee the acceptable performance of robot manipulators when faults occur. First, an extended state observer is designed to estimate the lumped uncertainties, disturbance and faults. Then, the information from the observer is used to combine with the main novel synchronous fast terminal sliding mode controller as a compensator. By combining the merits of the observer compensation, sliding mode and synchronization technique, the proposed fault-tolerant controller is able to deal with uncertainties and disturbances in normal operation mode and reduce the effects of faults in case faults occur, especially in the occurrence of loss-effective faults. Finally, the enhanced safety, reality and effectiveness of the proposed fault-tolerant control are evaluated through the control of a 3-DOF robot manipulator in both a simulated environment and experiment.

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

Deep Koopman Operator With Control for Nonlinear Systems

Recently Koopman operator has become a promising data-driven tool to facilitate real-time control for unknown nonlinear systems. It maps nonlinear systems into equivalent linear systems in embedding space, ready for real-time linear control methods. However, designing an appropriate Koopman embedding function remains a challenging task. Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control input. In this work, we propose an end-to-end deep learning framework to learn the Koopman embedding function and Koopman Operator together to alleviate such difficulties. We first parameterize the embedding function and Koopman Operator with the neural network and train them end-to-end with the K-steps loss function. Then, an auxiliary control network is augmented to encode the nonlinear state-dependent control term to model the nonlinearity in the control input. This encoded term is considered the new control variable instead to ensure linearity of the modeled system in the embedding system. We next deploy Linear Quadratic Regulator (LQR) on the linear embedding space to derive the optimal control policy and decode the actual control input from the control net. Experimental results demonstrate that our approach outperforms other existing methods, reducing the prediction error by order of magnitude and achieving superior control performance in several nonlinear dynamic systems like damping pendulum, CartPole, and the seven DOF robotic manipulator.

Implementation and Analysis of Motion Control Techniques on an Industrial 7 DOF Robotic Manipulator

With the quest for modernization in robotics motion control of robot manipulators is an area of active research. In motion control, inverse trajectory application is the most adopted solution which is further improved by considering the effect of external forces of the environment. The impact of external force involves dynamic modelling of the system and force control, impedance control, and admittance control techniques are adopted for the solution. This research implements the simulation and more precise visualization of the real-life manipulator under the rules of kinematics and dynamics on a 7 Degree of Freedom manipulator. The objective of this research is to implement motion control techniques: Forward Kinematics, Inverse kinematics, and Trajectory tracking using MATLAB Robotic System Toolbox and analyze the results obtained. These simulations can an effective tools for tuning the motion of robotics by varying the control parameter in simulation and then implementing them in physical hardware.

Vision-Based Mobile Manipulator for Handling and Transportation of Supermarket Products

Robot manipulators are growing more widely employed in the retail market, mostly for warehousing, but automating them in-store logistics processes is still a difficult task. Supermarkets and large retail stores face many challenges: shortages, handling, and placement of a single product on shelves. Various issues needed to be considered to develop a robot which can manipulate products of different sizes, shapes, and weight in limited spaces on shelves. The aim of this article is to design and develop a system to address the issues of shortage, identification, moving, and placements of products in supermarkets by properly incorporating database, camera vision, and line following mobile manipulator. A four-wheeled differential drive mobile robot was designed and developed which has a 5 DOF robotic manipulator on it. The line following technique is used to move it around the warehouse. The barcode recognition technique for the localization of product sections and object detection using SIFT is successfully and efficiently employed. The demonstration of the usefulness of the method was shown by carrying out experiments in a relevant environment which imitates a real supermarket.

Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

Background: In this paper we have developed an intelligent control law for the control of mobile manipulator robots by investigating the various techniques proposed in the literature. Thus, we have adopted a hybrid approach integrating part of classical and advanced automation in order to create an efficient control structure that can cope with a certain level of complexity. Our research logic is based on the process of keeping in mind that the control system must comply with the constraints imposed during the implementation of the control architecture. Objective: This paper aims to develop a control law in order to guarantee a certain level of performance and, more precisely, during a trajectory tracking application for mobile handling missions. The developed control law guarantees robustness with respect to external disturbances and parametric uncertainties due to the modelling of the system. Methods: In this paper, a study of the basic concepts of robotics and robot modelling is presented in order to set up the dynamic model used for the elaboration of the command. A sliding-mode controller based on a radial base function neural network with minimum parameter learning is developed for the Pelican robot as a two link robot manipulator. This approach, which combines a Radial Base Function Neuronal Network (RBFNN) and a Sliding Mode Control (SMC), is presented for the tracking control of this class of systems with unknown nonlinearities. The centres and outputs weights of the RBFNN are updated via online learning in accordance with the adaptive laws, allowing the control output of the neural network to approach the equivalent control in the sliding mode in the predetermined direction. The Lyapunov function is used to develop the adaptive control algorithm based on the RBFNN model. To reduce computational load and increase real-time arm performance, an RBFNN-based on the SMC with the Minimum Parameter Learning (MPL) method is designed. Results: Neural network sliding mode control is designed to underline the effectiveness of the approach to control the manipulator. This method of control ensure the tracking trajectories. Conclusion: The results of the simulation for the manipulator arm presented demonstrates the effectiveness of the modelling strategy, the correction and the robustness of the control approach.

An improved inverse kinematics solution for 6-DOF robot manipulators with offset wrists

AbstractEfficiently solving inverse kinematics (IK) of robot manipulators with offset wrists remains a challenge in robotics due to noncompliance with Pieper criteria. In this paper, an improved method to solve the IK for 6-DOF robot manipulators with offset wrists is proposed. This method is based on the Newton iteration technique, but it does not require a selection of initial estimation of joint variables. The solution is divided into two parts: the first part is to reconstruct a simplified structure with analytical IK solution, and the second part is to obtain a numerical solution by iteration. Further, a robot manipulator HSR-BR606 with an offset wrist is used as an example to specifically elaborate the mathematical procedure of the method and to investigate the algorithm in terms of accuracy, efficiency, and application of motion planning. A comparative experiment is conducted with a typical IK algorithm, which demonstrates a higher accuracy and shorter calculation time of the proposed method. The mean calculation time for a single IK solution required for this algorithm is only 4% of the comparison algorithm.

Time-Varying Transformation-Based Adaptive Tracking Control of Uncertain Robotic Systems With Event-Triggered Mechanism

In this paper, an adaptive global event-triggered control scheme for uncertain robotic systems, capable of guaranteeing adjustable tracking performance, is developed. Compared with most of the existing results, the proposed control exhibits some features. Firstly, to improve the transient tracking performance without the requirement of large control effort, a time-varying scaling diagonal matrix and an error transformation are constructed so that each element of the tracking error has its own convergence rate, which can be adjusted by selecting the design parameters. Secondly, due to the consideration of decreasing the consumption of network resources, some additional diagonal matrices embedded in the new closed-loop system make the new control gain matrix not symmetric and positive definite, then the stability analysis becomes rather more challenging. To solve this issue, an alternative Lyapunov function is constructed to circumvent the above obstacles. The effectiveness of the proposed approach is verified by a 2-DOF robotic manipulator.

Economic 6-DOF robotic manipulator hardware design for research and education

This work proposes economic design of robotic manipulator hardware for education and laboratory experiment purposes. This prototype can be used for conducting experiments and validation of various control strategies like robust control design, artificial intelligence-based control design, vision-based control, etc. Designed 6-DOF robotic manipulator is connected to the computer system through Arduino board, which acts as a communication device. High torque DC servo motors are used to control each joint of the manipulator. Frame of the manipulator is designed with mild steel brackets. Robot operating system (ROS), an open-source meta operating system is used to control and perform series of operations on a robotic manipulator. Robotic manipulator is configured and controlled using the MoveIt package. MoveIt will plan the trajectory using inverse kinematics for desired goal state and the same can be visualized through Rviz. Experiment results show that robotic manipulator prototype successfully follow the trajectory planned by the MoveIt and reached to the desired goal state. Because of open-source alternatives like ROS, cost of the prototype design is economical and hence, the prototype can be utilized for experimental validation in research and laboratory purpose.

Generic Development of Bin Pick-and-Place System Based on Robot Operating System

Bin pick-and-place is an important topic in factory automation and warehouse automation. In this paper, a bin pick-and-place system based on robot operating system (ROS) is implemented to make a six-degree-of-freedom (6-DOF) robot manipulator to complete multiple pick-and-place tasks. The proposed system uses ROS to integrate an object perception module and a pick-and-place module, where the former uses an RGB-D camera to capture images inside the bin, and the latter controls a 6-DOF robot manipulator and two self-made vacuum tools. To estimate the pose of the target object, a YOLOv4 object detector is implemented, and an object sorting method is proposed to find the target object in the image. Then, a pose estimation method based on computer aided design (CAD) is proposed to estimate the pose of target object. To perform the object pick-and-place operations, a coordinate transformation node is designed to transfer the pose of the target object into the workspace. Then, a link distance-based bin collision avoidance method is proposed to avoid collisions. Finally, the angle of the 1-DOF vacuum tool and the picking and placement poses of the robot manipulator are obtained from the result of the bin collision avoidance and the pose of the target object. In this study, a total of ten ROS nodes are designed, and the solutions that make each function easier to implement and reproduce are proposed. In the experiments, we set up four experiments with two task types and two object types to verify the effectiveness of the implemented bin pick-and-place system.

Green Fruit Removal Dynamics for Development of Robotic Green Fruit Thinning End-Effector

HighlightsPulling and stem-cutting methods were used for measuring the green fruit removal dynamics.A stem-cutting end-effector prototype was designed, developed, and tested for green fruit removal.No significant correlation was found between the fruit or stem size and the force required for fruit removal.The success rate of the stem-cutting end-effector prototype for all experiments was over 90%.Abstract. Green fruit thinning is one of the most important operations in apple production for obtaining high-quality fruit. Manual thinning is time-intensive, making it impractical for large orchards. Some alternative methods, such as chemical and mechanical thinning, have greatly improved work efficiency, but both methods have drawbacks due to non-selective targeting. Robotic green fruit thinning can potentially be as selective as manual thinning. This study developed an effective end-effector for robotic green fruit thinning. Prior to designing the end-effector, a series of fruit removal dynamics tests were conducted to determine the forces required for robotic thinning using pulling or stem-cutting methods on three different apple cultivars. The overall mean forces for pulling detachment were 24.78 ±0.48 N and 19.91 ±0.55 N when detaching stem from the fruit-end and the spur-end, respectively. The average force required for stem-cutting was 33.6 ±8.0 N among the three cultivars. No significant differences were found between the fruit or stem dimensions and the force required for fruit removal. A stem-cutting end-effector prototype was then developed to conduct fruit removal experiments in field conditions. Two end-effector prototype configurations were tested: one placing the end-effector on a handheld bar, and the other integrating the end-effector with a six DoF robotic manipulator. The success rates of green fruit removal for all end-effector prototype experiments were over 90%. The end-effector is a core component of an automated green fruit thinning system. Integration with the robotic manipulator also indicated the potential of a robotic green fruit system to remove fruit at different locations and orientations. A machine vision system will be developed and integrated with the end-effector to develop a robotic green fruit thinning system. Keywords: Apple, Crop load management, End-effector, Fruit removal dynamics, Robotic thinning.