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检索条件"机构=Mechatronics and Robotics"

共 5372 条记录，以下是481-490 订阅

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Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation

Estimator-Coupled Reinforcement Learning for Robust Purely T...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Lennart Röstel Johannes Pitz Leon Sievers Berthold Bäuml DLR Institute of Robotics and Mechatronics Technical University of Munich and Deggendorf Institute of Technology

This paper identifies and addresses the problems with naively combining (reinforcement) learning-based controllers and state estimators for robotic in-hand manipulation. Specifically, we tackle the challenging task of purely tactile, goal-conditioned, dextrous in-hand reorientation with the hand pointing downwards. Due to the limited sensing available, many control strategies that are feasible in simulation when having full knowledge of the object's state do not allow for accurate state estimation. Hence, separately training the controller and the estimator and combining the two at test time leads to poor performance. We solve this problem by coupling the control policy to the state estimator already during training in simulation. This approach leads to more robust state estimation and overall higher performance on the task while maintaining an interpretability advantage over end-to-end policy learning. With our GPU-accelerated implementation, learning from scratch takes a median training time of only 6.5 hours on a single, low-cost GPU. In simulation experiments with the DLR-Hand II and for four significantly different object shapes, we provide an in-depth analysis of the performance of our approach. We demonstrate the successful sim2real transfer by rotating the four objects to all 24 orientations in the $\pi/2$ discretization of SO(3), which has never been achieved for such a diverse set of shapes. Finally, our method is able to reorient a cube consecutively to in median nine goals, which was beyond the reach of previous methods in this challenging setting. (Web: https://***/dlr-tactile-manipulation)

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Design of Fast Model Predictive Controller for Permanent Magnet Synchronous Motor Drive System 42

Design of Fast Model Predictive Controller for Permanent Mag...

引用

42nd Chinese Control Conference, CCC 2023

作者： Guo, Qian Liu, Qinyao Cao, Zhimin Institute of Intelligent Manufacturing and Smart Transportation Suzhou City University Suzhou215104 China School of Advanced Technology Xi'an Jiaotong Liverpool University Department of Mechatronics and Robotics Suzhou215123 China

ISBN: (纸本)9789887581543

In this article, a new predictive controller based on fast model predictive control has been proposed for permanent magnet synchronous motor(PMSM) drive system to solve the quadratic programming problem online in a very short period. The mathematical model of PMSM has been described in terms of state space and discretized by Euler method firstly. Then, the discrete model is linearized at the steady state point. Based on this, the optimization objective function which is used to measure the performance of the PMSM is designed properly. The approximate primal barrier interior point method is employed to speed up the optimization process. Finally, the simulation results declare that the proposed algorithm is useful for PMSM drive system. In addition, the proposed approach enables the model parameters, the horizon size as well as the weighting factors updated in real time so that it has potential to solve problems such as model mismatch in the future. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Quadratic programming

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Simulation And Real Time Of VR Controlled Robotic Manipulator Using ROS 4

Simulation And Real Time Of VR Controlled Robotic Manipulato...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Vairavasamy, Sasidharan Noel Innocent, J. Hamin, M.J. Ahmed, Syed Seenu, N. Mm Dean, Ramya Hindustan Institute of Technology and Science Department of Mechatronics Engineering Chennai India Centre for Automation and Robotics Department of Mechatronics Engineering Hindustan Institute of Technology and Science Chennai India Agurchand Manmull Jain College Chennai India

ISBN: (纸本)9781665492911

This paper focuses on developing a virtually controlled robot by integrating Virtual Reality (VR) and Robot Operating System (ROS). Robots can be used in certain environments where humans cannot be physically present to undertake a task. To increase the safety of humans, controlling a robot virtually is one of the best solutions. This paper shows how the VR-controlled manipulator will help in different fields like hazardous environments, underwater research, and the medical field for surgical operations. Unity 3D is used to develop the virtual environment, and ROS is used for communication with the physical robot. A Virtual environment would be developed where an exact robot (URDF) model can be designed. The interaction with the virtual environment is done with the help of VR headsets and controllers. ROS acts as the communication bridge between virtual and physical robots. A prototype has been developed to be controlled through virtual controllers that can interact through ROS. © 2022 IEEE.

关键词： Virtual reality

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Algorithm of Personalized Adjustment of the Active-Passive Mechanotherapy Device for the Ankle Joint

Algorithm of Personalized Adjustment of the Active-Passive M...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Anrdey Knyazev Sergey Jatsun Andrey Fedorov Department of Mechanics Mechatronics and Robotics SouthWest State University Kursk Russian Federation

One of the most common injuries sustained by humans is injury to the ankle joint. After an injury, there is a long process of treatment and rehabilitation, which can be complicated by prolonged immobility of the joint. To reduce the rate of complications, medical devices for mechanotherapy are usually used, which can also speed up the patient's recovery. Each patient requires a personalized adjustment of the device due to the physiological characteristics of the foot. The aim of the study is to increase the effectiveness of ankle joint rehabilitation by personalized adjustment of the device. It is necessary to create a methodology for adjusting the device based on controlling the interaction force between the foot and the platform to ensure uniform contact at the time of fixation of the patient's leg. The results of the research can be used to create a mechanotherapy device that provides a given movement of the foot under unpredictable changes in physiological parameters with the possibility of personalized adjustment to increase the effectiveness of rehabilitation measures.

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ROS-like framework using modern development concepts and microservices

ROS-like framework using modern development concepts and mic...

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Ivanou, Mikhail Mikhel, Stanislav Maloletov, Alexander Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781665424066

The Robot Operating System (ROS) has established itself as a useful set of software libraries and tools that can help to build robot applications. It provides services such as hardware abstraction, low-level device management, implementation of frequently used functions, inter-process message passing, and package management. ROS is good for small static research environments, however, it does not scale so well to large real-world applications. Besides, there is no security model, the test approach is unclear, the library versions can be incompatible. The paper presents a survey, the results of which show that many ROS users have problems with such modern programming techniques as testing or automatic deployment. Nowadays, many systems based on modern programming paradigms are developed by leading companies, and there is a lot of good open-source software out there. An application can be built without writing a lot of code, one just needed to combine high-quality libraries with necessary functionality. This paper intends to show how to build a ROS-like system using modern technologies, such as microservices and message queues, in a simple way with high quality and functionality using an example of the RhoMBus framework. © 2021 IEEE.

关键词： Robot Operating System

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Parrying perturbations in the systems with complementary control

Parrying perturbations in the systems with complementary con...

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2021 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021

作者： Kobzev, Alexandеr Department of Automation Mechatronics and Robotics Vladimir State University Vladimir Russia

ISBN: (纸本)9781728145877

The paper consideres complementary control for parrying external and internal perturbations of the system. The directions of parrying perturbations in automatic control systems (ACS) are marked. The basics of complementary control are displayed as the modified fourth form of invariance and its application for task solution. Two cases in the basis of the fourth form of invariance are distinguished. The first is for parrying external perturbations, the second is for parrying internal perturbations. Expressions for obtaining additional components in the control action are defined. The implementation of additional control is possible in the form of an autonomous component and by correcting the control action at the stage of its formation. a block diagram of ACS with complementary and predictive control is proposed, and its features are pointed out. The implementation of the adaptation loop, which forms an additional component in the control action, is possible at the program level and on the basis of a neural network. The results of practical implementation of ACS with complementary control and neural controller are given. © 2021 IEEE.

关键词： Automation

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Vision Based Leader-Follower Control of Wheeled Mobile Robots using Reinforcement Learning and Deep Learning

Vision Based Leader-Follower Control of Wheeled Mobile Robot...

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International Symposium on System and Software Reliability (ISSSR)

作者： Kayleb Garmon Ying Wang Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

Vision-based control of mobile robots often involves complex calculations to derive a control law. The reinforcement learning algorithm (Q-learning) offers a machine learning method to extrapolate a control law from an environment given discretized actions, without the need of complex calculations. In this paper, a vision-based controller is created using Q-Learning to enable tracking in a leader-follower configuration of two nonholonomic autonomous mobile robots. The follower robot gathers its desired trajectory values by using a deep learning SSD model to identify a distinguishing visual feature on the leader robot and uses a lidar to determine the distance between two robots. These parameters are utilized to select an optimal action of the follower robot through reinforcement learning. The emulated results in a ROS Gazebo environment show this method to be effective in enabling a wheeled mobile robot to follow another, while simultaneously avoiding obstacles.

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Audio Perception in Robotic Assistance for Human Space Exploration: A Feasibility Study

Audio Perception in Robotic Assistance for Human Space Explo...

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IEEE Conference on Aerospace

作者： Marco Sewtz Werner Friedl Adrian Bauer Anne Köpken Florian Lay Nicolai Bechtel Peter Schmaus Rudolph Triebel Neal Y. Lii German Aerospace Center (DLR) Institut of Robotics and Mechatronics Weßling Germany

Future crewed missions beyond low earth orbit will greatly rely on the support of robotic assistance platforms to perform inspection and manipulation of critical assets. This includes crew habitats, landing sites or assets for life support and operation. Maintenance and manipulation of a crewed site in extra-terrestrial environments is a complex task and the system will have to face different challenges during operation. While most may be solved autonomously, in certain occasions human intervention will be required. The telerobotic demonstration mission, Surface Avatar, led by the German Aerospace Center (DLR), with partner European Space Agency (ESA), investigates different approaches offering astronauts on board the International Space Station (ISS) control of ground robots in representative scenarios, e.g. a Martian landing and exploration site. In this work we present a feasibility study on how to integrate auditory information into the mentioned application. We will discuss methods for obtaining audio information and localizing audio sources in the environment, as well as fusing auditory and visual information to perform state estimation based on the gathered data. We demonstrate our work in different experiments to show the effectiveness of utilizing audio information, the results of spectral analysis of our mission assets, and how this information could help future astronauts to argue about the current mission situation.

关键词： Visualization European Space Agency Low earth orbit satellites Inspection International Space Station Space exploration Task analysis

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Optimization of multi-arm robot locomotion to reduce satellite disturbances during in-orbit assembly

Optimization of multi-arm robot locomotion to reduce satelli...

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IEEE Conference on Aerospace

作者： Jean-Pascal Lutze Robert Schuller Hrishik Mishra Ismael Rodríguez Máximo A. Roa German Aerospace Center (DLR) Institute of Robotics and Mechatronics Weßling Germany

Traditionally, manufacturing and assembly of space assets is performed on ground before sending them into orbit. However, this monolithic approach involves high launch costs due to increasing asset sizes, e.g., large telescopes for space observation. Alternatively, in-orbit assembly of space structures after launching the raw materials to orbit opens wider possi-bilities at a reduced cost. Mobile robotics, such as walking manipulators or multi-arm robots, are a critical component for this approach due to their mobility in orbit. However, unlike ter-restrial assembly tasks, the continuous motion of the robot and materials, coupled with the change of inertial properties of the structure, results in a rotational deviation of the platform due to conservation of angular momentum in orbit. This might violate the tolerance limits of the platform antenna's cone angle for communication with the ground stations. Although exploiting the attitude control system of the platform is a straightforward solution, it might lead to issues related to the associated actu-ators like reaction wheels saturation, high-frequency vibration, or high fuel consumption. To deal with this problem, in this paper we formulate the attitude disturbance problem as a minimization of the effects created by the gait of the walking manipulator. Investigating the dynamic coupling between the robot system and the space structure gives a deeper understanding of the spacecraft's be-havior depending on the robot gaits. The paper proposes a controller that optimizes the forces that the robotic arm applies to the structure, hence minimizing the base rotation. As an application, we use a space structure composed of identical elements, namely the mirrors of a segmented telescope, endowed with standard interfaces to allow the robot locomotion. We show the effects of optimizing these interaction forces in various sce-narios and positions on the structure through multiple dynamic simulations.

关键词： Space vehicles Legged locomotion Vibrations Costs Wheels Telescopes Aerospace electronics

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Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture

Dextrous Tactile In-Hand Manipulation Using a Modular Reinfo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Johannes Pitz Lennart Röstel Leon Sievers Berthold Bäuml DLR Institute of Robotics and Mechatronics Technical University of Munich Deggendorf Institute of Technology

Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, and when no external sensors are used. For the task of reorienting an object to a given goal orientation (vs. infinitely spinning it around an axis), the lack of external sensors is an additional fundamental challenge as the state of the object has to be estimated all the time, e.g., to detect when the goal is reached. In this paper, we show that the task of reorienting a cube to any of the 24 possible goal orientations in a π/2-raster using the torque-controlled DLR-Hand II is possible. The task is learned in simulation using a modular deep reinforcement learning architecture: the actual policy has only a small observation time window of 0.5 s but gets the cube state as an explicit input which is estimated via a deep differentiable particle filter trained on data generated by running the policy. In simulation, we reach a success rate of 92% while applying significant domain randomization. Via zero-shot Sim2Real-transfer on the real robotic system, all 24 goal orientations can be reached with a high success rate. (Web: ***/dlr-tactile-manipulation)

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