Industrial Dual-arm Robot Research Articles

Skillful Operation of Working Plate for Grasp Less Ball Handling with an Industrial Dual-Arm Robot Considering Temperature of Joint Motors

Skillful Operation of Working Plate for Grasp Less Ball Handling with an Industrial Dual-Arm Robot Considering Temperature of Joint Motors

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

Automatic self-contained calibration of an industrial dual-arm robot with cameras using self-contact, planar constraints, and self-observation

We present a robot kinematic calibration method that combines complementary calibration approaches: self-contact, planar constraints, and self-observation. We analyze the estimation of the end effector parameters, joint offsets of the manipulators, and calibration of the complete kinematic chain (DH parameters). The results are compared with ground truth measurements provided by a laser tracker. Our main findings are: (1) When applying the complementary calibration approaches in isolation, the self-contact approach yields the best and most stable results. (2) All combinations of more than one approach were always superior to using any single approach in terms of calibration errors and the observability of the estimated parameters. Combining more approaches delivers robot parameters that better generalize to the workspace parts not used for the calibration. (3) Sequential calibration, i.e. calibrating cameras first and then robot kinematics, is more effective than simultaneous calibration of all parameters. In real experiments, we employ two industrial manipulators mounted on a common base. The manipulators are equipped with force/torque sensors at their wrists, with two cameras attached to the robot base, and with special end effectors with fiducial markers. We collect a new comprehensive dataset for robot kinematic calibration and make it publicly available. The dataset and its analysis provide quantitative and qualitative insights that go beyond the specific manipulators used in this work and apply to self-contained robot kinematic calibration in general.

Study on Accuracy of Lissajous Ball Rolling Motion by Operating a Plate Based on Grasp-less Handling with an Industrial Dual-arm Robot

Study on Accuracy of Lissajous Ball Rolling Motion by Operating a Plate Based on Grasp-less Handling with an Industrial Dual-arm Robot

Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor

Assembly tasks executed by a robot have been studied broadly. Robot assembly applications in industry are achievable by a well-structured environment, where the parts to be assembled are located in the working space by fixtures. Recent changes in manufacturing requirements, due to unpredictable demanded products, push the factories to seek new smart solutions that can autonomously recover from failure conditions. In this way, new dual arm robot systems have been studied to design and explore applications based on its dexterity. It promises the possibility to get rid of fixtures in assembly tasks, but using less fixtures increases the uncertainty on the location of the components in the working space. It also increases the possibility of collisions during the assembly sequence. Under these considerations, adding perception such as force/torque sensors have been done to produce useful data to perform control actions. Unfortunately, the interaction forces between mating parts produced non-linear behavior. Consequently, machine learning algorithms have been considered an alternative tool to avoid the non-linearity. In this work we introduce an assembly strategy for an industrial dual arm robot based on the combination of a discrete event controller and Deep Neural Networks (DNN) to solve the peg-in-hole assembly. Our results show the difference between the use of DNN with one and with two force/torque sensors during the assembly task and demonstrate a 30% increase in the assembly success ratio when using a double force/torque sensor.

Applications of Light-Weight Wearable Devices to Online Programming of Industrial Dual-Arm Robots

Dexterous manipulation of dual-arm robots in unstructured environments is very useful. Programming a dual-arm industrial robot to efficiently complete dexterous tasks, however, is especially challenging due to the complexity of its inverse kinematics, motion planning, dual-arm coordination with self-collision avoidance, and so on. This paper presents a systematic solution to accurately manipulate a dual-arm industrial robot on-site via light-weight wearable devices. In the developed system, the human operator directly drives the robot through the human arms motions tracked by the combination of inertial measurement units and handheld joystick controllers. A proper motion retargeting method with self-collision avoidance is used to enable the user to manipulate the robot directly through intuitive arm motions within a comfortable range and ensure the task manipulation with safety in unstructured environments. The developed system has been tested with various tasks, such as the manipulation of objects of different shapes, dexterous turn-over, and dual-arm coordination. Compared with the existing telerobotic systems, the developed system with simultaneous 14 degree-of-freedom teleoperation directly driven by light-weight wearable devices is able to handle more dexterous and accurate manipulation tasks with the capability of fast deployment and self-collision awareness. Such a solution could pave the way for online dual-arm robot programming on efficient manipulation skills transfer in the future.

A Study on Ball Motion Control Method for Glass Press Handling Based on Plate Manipulation of Industrial Dual Arm Robot

A Study on Ball Motion Control Method for Glass Press Handling Based on Plate Manipulation of Industrial Dual Arm Robot

Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

As the application of industrial robots is limited by low stiffness that causes low precision, a joint stiffness identification algorithm for serial robots is presented. In addition, a deformation compensation algorithm is proposed for the accuracy improvement. Both of these algorithms are formulated by dual quaternion algebra, which offers a compact, efficient, and singularity-free way in robot analysis. The joint stiffness identification algorithm is derived from stiffness modeling, which is the combination of the principle of virtual work and dual quaternion algebra. To validate the effectiveness of the proposed identification algorithm and deformation compensation algorithm, an experiment was conducted on a dual arm industrial robot SDA5F. The robot performed a drilling operation during the experiment, and the forces and torques that acted on the end-effector (EE) of both arms were measured in order to apply the deformation compensation algorithm. The results of the experiment show that the proposed identification algorithm is able to identify the joint stiffness parameters of serial industrial robots, and the deformation compensation algorithm can improve the accuracy of the position and orientation of the EE. Furthermore, the performance of the forces and torques that acted on the EE during the operation were improved as well.

Modeling and Simulation of Coordinated Motion of Dual-arm Industrial Robot

Dual-arm industrial robot, Geometric project method, Coordinated movement, Pole-inhole insertion.

Robot learning of industrial assembly task via human demonstrations

Human–robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a task in a not fully repetitive manner using their expertise and cognitive capabilities. The traditional robot programming cannot cope with these challenges of human–robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. Through the demonstrations, the robot learns the sequence of actions for an assembly task (high-level learning) without the need of pre-programming. Additionally, the robot learns every path as needed for object manipulation (low-level learning). Once the robot has the knowledge of the demonstrated task, it can perform the task in collaboration with the human. However, the need for adaptation of the learned knowledge may arise as the human collaborator could introduce changes in the environment, such as placing an object to be manipulated in a position and orientation different from the demonstrated ones. In this paper, a novel real-time adaptation algorithm to cope with these changes in the environment, introduced by the human factor, is proposed. The proposed algorithm is able to identify the sequence of actions needed to be performed in a new environment. A Gaussian Mixture Model-based modification algorithm is able to adapt the learned path in order to enable robot to successfully complete the task without the need of additional training by demonstration. The proposed framework copes with changes in the position and orientation of the objects to be manipulated and also provides obstacle avoidance. Moreover, the framework enables the human collaborator to suggest different sequence of actions for the learned task, which will be performed by the robot. The proposed algorithm was tested on a dual-arm industrial robot in an assembly scenario and the results are presented. Shown results demonstrate a potential of the proposed robot learning framework to enable continuous human–robot collaboration.

Consideration of the Lissajous trajectory of the ball by the plate control of the industrial dual arm robot

Consideration of the Lissajous trajectory of the ball by the plate control of the industrial dual arm robot

Autonomous Clothes Manipulation Using a Hierarchical Vision Architecture

This paper presents a novel robot vision architecture for perceiving generic 3-D clothes configurations. Our architecture is hierarchically structured, starting from low-level curvature features to mid-level geometric shapes and topology descriptions, and finally, high-level semantic surface descriptions. We demonstrate our robot vision architecture in a customized dual-arm industrial robot with our in-house developed stereo vision system, carrying out autonomous grasping and dual-arm flattening. The experimental results show the effectiveness of the proposed dual-arm flattening using the stereo vision system compared with the single-arm flattening using the widely cited Kinect-like sensor as the baseline. In addition, the proposed grasping approach achieves satisfactory performance when grasping various kind of garments, verifying the capability of the proposed visual perception architecture to be adapted to more than one clothing manipulation tasks.

Examples of Art Performing with Industrial Dual-arm Robots

Examples of Art Performing with Industrial Dual-arm Robots

Robotic vision system for random bin picking with dual-arm robots

Random bin picking is one of the most challenging industrial robotics applications available. It constitutes a complicated interaction between the vision system, robot, and control system. For a packaging operation requiring a pick-and-place task, the robot system utilized should be able to perform certain functions for recognizing the applicable target object from randomized objects in a bin. In this paper, we introduce a robotic vision system for bin picking using industrial dual-arm robots. The proposed system recognizes the best object from randomized target candidates based on stereo vision, and estimates the position and orientation of the object. It then sends the result to the robot control system. The system was developed for use in the packaging process of cell phone accessories using dual-arm robots.

Monitoring and Control of Dual-Arm Industrial Robot Tasks Using IoT Application and Services

In the context of latest technological revolution, Industry 4.0, connectivity and therefore access and control of cyber-physical systems and resources from any place, at any time by any means represent a technological enabler of crucial importance. The first part of this paperwork contains a brief introduction of cyber-physical systems and IoT concepts, together with a review of major IoT providers. The second part introduces an approach towards achieving connectivity and remote control of task selection for a dual-arm industrial robot using a commercially available IoT infrastructure and technology provided by ioBridge. Within the third part, details about experimental testing and evaluation of the selected solutions are presented. The last part is allocated for conclusions and further research directions.

Employing Smart Units and Servitization towards Reconfigurability of Manufacturing Processes

Increasing environmental concerns together with the need for sustainable consumption and production gave birth to the concept of product service-system. This paperwork presents how the concept of product-service systems (PSS), also known as servitization, can increase performance within the value creation chain in the manufacturing environment by deployingits key features within smart manufacturing units to provide them withreconfigurabilityproperties. Highlights on how thekey feature of a product-service system can be used to enable the deployment of reconfigurability within smart manufacturing units towards achieving the concept of reconfigurable manufacturing systems are revealed in the first part of the paper.Considering the new economic models, the ever changing society needs and the effects of disruptive innovations, a business model for manufacturing environmentis further proposed. The business model deploys small scale reconfigurable manufacturing systems by means of smart manufacturing units, intelligent information management platforms, cloud computing services and Internet of Things. An experimental test bench is afterwards presentedin this context. Three similar manufacturing scenarios are considered and reproduced viaa mix ofsix smart manufacturing units and interfaces. The goal of these scenarios isto test the feasibility of servitization towards achieving reconfigurability of smart units.Several industrial equipment among which a dual-arm industrial robot, two electro-mechanical grippers, two electro-mechanical axes and two proximity sensors areintegrated with the embedded system to enable the deployment of PSS features. Also, how servitization is deployed within available smart units, their generic architecture and how they are interconnected is presented in the paper,together with advantages and constraints of the proposed experimental approach. The paper ends with conclusions, remarks regarding the experimental tests and further research directions.

双腕ロボットの複雑なプレート操り動作における動的精度の研究

Nowadays, industrial dual-arm robots have gained attention as novel tools in the factory automation field for anext generation. We therefore focus on them to flexibly control both the linear motion and the rotational motion of a working plate. However, there has been one problem that it is difficult to measure the synchronous accuracy of two rotary axes without high accuracy gyro sensor. Thus, we proposed a novel method to measure the synchronous accuracy of two rotary axes of working plate with a ball, which keeps a ball rolling around a circular path on it by dual-arm cooperative control and demonstrated that a proposed method is effective to estimate its motion of frequency response. In present report, for widening the range of applications, we tried to keep a ball rolling around rhomboid path which is one of polygonal path on working plate by dual-arm cooperative control and to investigate its characteristic.

A Sensor-Based Dual-Arm Tele-Robotic System

We present a novel system to achieve coordinated task-based control on a dual-arm industrial robot for the general tasks of visual servoing and bimanual hybrid motion/force control. The industrial robot, consisting of a rotating torso and two seven degree-of-freedom arms, performs autonomous vision-based target alignment of both arms with the aid of fiducial markers, two-handed grasping and force control, and robust object manipulation in a tele-robotic framework. The operator uses hand motions to command the desired position for the object via Microsoft Kinect while the autonomous force controller maintains a stable grasp. Gestures detected by the Kinect are also used to dictate different operation modes. We demonstrate the effectiveness of our approach using a variety of common objects with different sizes, shapes, weights, and surface compliances.

Real‐time synchronisation method in multi‐robot system

There is an increasing need for manufacturing systems to produce batches in small quantities. Such manufacturing systems are significantly difficult to develop with conventional automation equipment. Recently, several research groups have applied industrial dual-arm robots to cell production lines. A synchronisation method for robots is necessary for the cell production process when robots work in a shared workspace. Conventional automation factories do not need this method because the main control system operates all machines or robots. However, the intended application for the developed robot is in small manufacturing environments which cannot install an expensive main control system. An inexpensive and high-performance method with a simple digital in/out channel using EtherCAT is proposed. The developed method was validated for practical use on a cell production line.

산업용 양팔로봇 제어 S/W 프레임 개발

Human rights at poor working condition is the severe problem in modern manufacturing system. The industrial dual-arm robot is being developed to meet these social issues fundamentally. The dual-arm robot can work instead of human workers. We developed the new dual-arm robot for manufacturing mobile phone and TV. It has advantages such as the solo controller for both arms, the human sized body and arms. The software platform for the industrial dual-arm robot is being developed which has strength in its convenience and intelligence compared to conventional the robot software platforms. Here the development of the dual-arm robot software platform is introduced.