In this paper, a novel algorithm based on Multi-Agent Reinforcement Learning for controlling parallel robots has been suggested. The dynamic models of parallel robots are complex and full of uncertainties, and derivin...
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ISBN:
(纸本)9781665454537
In this paper, a novel algorithm based on Multi-Agent Reinforcement Learning for controlling parallel robots has been suggested. The dynamic models of parallel robots are complex and full of uncertainties, and deriving them requires deep knowledge of the mechanism of the robot. Therefore, the proposed algorithm is designed model-free to be independent of prior knowledge about the system from the outset. Moreover, this algorithm comprises two primary components, making it efficient in training and convergence. The proposed algorithm takes each loop or limb in parallel robots as a separate agent. These agents then learn to collaborate to fulfill the robot’s defined task by producing appropriate control signals from a decentralized point of view. For studying the performance of the proposed algorithm, a 3-DOF parallel robot called Agile Eye is taken into account as a case study which is simulated in CoppeliaSim simulation environment for the task of reference tracking. Two other controllers, including the classic Proportional Integral Derivative (PID) controller and the single-agent counterpart of the suggested algorithm, have been implemented for a better performance comparison of the proposed algorithm. Using the Root Mean Square Error (RMSE) index, the recommended algorithm with an RMSE value of 0.0553 is superior to its single-agent counterpart with an RMSE of 0.1105. On the other hand, the proposed algorithm is inferior to the PID controller with an RMSE of 0.0275, mainly due to the fact that the PID Controller is in velocity control mode, while the proposed algorithm manipulates the robot in torque control mode, which is less stable.
Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for infor...
Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.
Independent Component Analysis (ICA) is a powerful tool for solving blind source separation problem in biomedical engineering. The traditional ICA algorithm ignores the Lie group structure of constrained matrix manifo...
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This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robotinteractions. By using the robust capabilities of the MediaPip...
This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robotinteractions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robotinteractions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robotinteractions, with potential benefits for individuals with unique communication needs.
This article investigates the performance of the Delta robot using model-free controller. Since it is difficult to obtain an accurate dynamic model of the robot and identification methods are complex and challenging. ...
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ISBN:
(纸本)9781665454537
This article investigates the performance of the Delta robot using model-free controller. Since it is difficult to obtain an accurate dynamic model of the robot and identification methods are complex and challenging. Therefore, in this article, a new approach is presented which checks the performance of the Delta robot in pick-and-place operation using model-free dynamic controllers. For the purposes of this paper, Deep Reinforcement Learning (DRL) and also the Deep Deterministic Policy Gradient (DDPG) network is used. The DDPG algorithm is implemented for the first time on the Delta robot, and according to the obtained results, it reveals that it can provide a favorable approach for the pick-and-place operation. In this method, based on the science of DRL, a reward function based on the pick-and-place operation is also defined, which gives negative reward (punishment) or a positive reward according to the way the robot performs. It is noteworthy that according to the obtained results, it is possible to generalize other points for the pick-and-place operation. It should be noted that the robot was able to learn the target after a long episode and its graph converged after approximately 18,000 episodes.
作者：
Tian, YePan, JingwenYang, ShangshangZhang, XingyiHe, ShupingJin, YaochuAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center
Institute of Artificial Intelligence Hefei230088 China Anhui University
School of Computer Science and Technology Hefei230601 China Anhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University
Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University
Faculty of Technology Bielefeld33619 Germany
The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision s...
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Bulky external power supplies largely limit the continuous long-term application and miniaturization development of smart sensing ***,we fabricate a flexible and wearable integrated sensing system on an electrospun al...
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Bulky external power supplies largely limit the continuous long-term application and miniaturization development of smart sensing ***,we fabricate a flexible and wearable integrated sensing system on an electrospun all-nanofiber *** three parts of the sensing system are all obtained by a facile ink-based direct writing *** resistive pressure sensor is realized by decorating MXene sheets on TPU ***,the resistive temperature sensor is prepared by compositing MXene sheets into poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS).The thin-film zinc–air battery(ZAB)includes an interdigital zinc–air electrode that is bonded with a gel polymer *** can supply a high open-circuit voltage of 1.39 V and a large areal capacity of 18.2 mAh cm^(-2) for stable and reliable power-supplying sensing parts *** to the hydrophobic nature of TPU and open-ended micropores in the TPU nanofiber,the sensing system is waterproof,self-cleaning,and air and moisture *** application,the above-mentioned functional components are seamlessly integrated into an intelligent electronic wristband,which is comfortably worn on a human wrist to monitor pulse and body temperature in real time with continuous operation of up to 4 *** the novel design and remarkable performance,the proposed integrated all-nanofiber sensing system presents a promising solution for developing advanced multifunctional wearable electronics.
Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently nee...
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Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.
In this paper, three types of neural networks including, multilayer perceptron, radial basis function, and Local Linear Model Trees, are considered based on adaptability with the significant behavior of data to solve ...
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This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this...
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