Interfacing Computing Platforms for Dynamic Control and Identification of an Industrial KUKA Robot Arm

Salameen, Laith; Estatieh, Abdelkarim; Darbisi, Salman; Tutunji, Tarek A.; Rawashdeh, Nathir A.

doi:10.1109/rem49740.2020.9313878

Cited by 9 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is suggested that gaze-based decoding may become one of the most efficient ways to interface with robotic actuators. The KUKA Agilus industrial robot arm was used in [ 11 ] as an interface platform between the robot and personal computers, with two tools designed for collecting logging data and drawing image files. including the reduction of complexity and computational burden in flexible multibody systems [ 12 ], dynamic reduction algorithms for flexible mechanisms [ 12 ], and the development of integral robust control algorithms for uncertain nonlinear systems [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

Nasrat,

Kang,

Park

et al. 2023

Sensors

View full text Add to dashboard Cite

In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.

show abstract

Section: Related Workmentioning

confidence: 99%

Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

Nasrat,

Kang,

Park

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To train this type of network, a set of images and an annotation in .json format is necessary, with detailed information about the location of the class object (coordinates in 2D space) and the class itself. The following parameters were chosen for the neural network using Create ML [ 128 , 129 , 130 , 136 ] ( Figure 16 ): the algorithm is the complete network (trains a complete object detection network based on YOLOv2 architecture); the number of epochs is 5000; the batch size is automatic; and the grid size is 13 × 13.…”

Section: Object Recognition In Robot Working Space Using Convolutiona...mentioning

confidence: 99%

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

View full text Add to dashboard Cite

Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

show abstract