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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Radda Iureva Alexey Margun Egor Lykoyanov Faculty of Control Systems and Robotics ITMO Univresity Saint Petersburg Russia State Research Center of the Russian Federation Concern CSRI Elektropribor JSC ITMO University Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing control systems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey Box” approach, where the governing equations of ship dynamics are known but certain parameters remain unidentified, and the “Black Box” approach, which relies entirely on trajectory data without any prior knowledge of the underlying mathematical models. Leveraging machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, alongside optimization algorithms like L-BFGS-B, this research aims to evaluate and compare the efficacy of both approaches in estimating model parameters and predicting ship trajectories. The findings demonstrate that while the Grey Box approach benefits from incorporating physical laws for parameter tuning, the Black Box method offers flexibility in modeling complex, nonlinear dynamics purely based on empirical data. The integrated use of these methodologies provides a robust framework for enhancing the accuracy and reliability of ship dynamics models, contributing significantly to maritime engineering applications.

关键词： Parameter estimation Accuracy Predictive models Prediction algorithms Mathematical models Trajectory Vehicle dynamics Marine vehicles Optimization Long short term memory

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Universal Recurrent Traffic Flow Model for Urban Traffic Simulation

Universal Recurrent Traffic Flow Model for Urban Traffic Sim...

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2021 International Conference of Technology, Science and Administration, ICTSA 2021

作者： Sofronova, Elena Alnowaini, Ghazi Russian Academy of Sciences Department of Robotics Control Federal Research Center Computer Science and Control of Moscow Russia Taiz University Department of Mechatronic Engineering Taiz Yemen

ISBN: (纸本)9781665428620

This paper presents a universal recurrent traffic flow model designed to simulate traffic flows and solve the problem of finding the optimal durations of traffic light phases. The model belongs to the class of microscopic and is based on the theory of controlled networks. The road network is presented as a configurable graph. The nodes of the graph are the road sections, and the edges are the maneuvers between the sections. The graph configuration is changed by turning the graph edges on or off, which corresponds to the permission or prohibition of certain maneuvers. Traffic flows are controlled by traffic lights at regulated intersections. To apply this model it is assumed that the information about the parameters of flows and the road network is available. The description of the model, its properties, and a methodological example of calculation are given. © 2021 IEEE.

关键词： Roads and streets

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Whole Body control Formulation for Humanoid Robots with Closed/Parallel Kinematic Chains: Kangaroo Case Study

Whole Body Control Formulation for Humanoid Robots with Clos...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sait Sovukluk Johannes Englsberger Christian Ott Automation and Control Institute (ACIN) TU Wien Vienna Austria German Aerospace Center (DLR) Institute of Robotics and Mechatronics Wessling Germany

This study extends the whole-body control (WBC) formulation for bipedal humanoid robots that include closed (parallel) kinematic chains in their structure. Along with general formulation, we also stress the implementation of this formulation on Kangaroo, which is a highly dynamic humanoid robot developed by PAL robotics. This 76-DOF robot includes 24 independent closed-kinematic chains in its structure and constitutes a good case study for our approach. We discuss the WBC formulation for various control structures, including inverse dynamics control (IDC) and Modular Passive Tracking control (MPTC). As a test scenario, we employ a 3D spring-loaded inverted pendulum (SLIP) jumping trajectory with disturbance rejection as the desired CoM trajectory.

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Unity RL Playground: A Versatile Reinforcement Learning Framework for Mobile Robots

arXiv

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arXiv 2025年

作者： Ye, Linqi Li, Rankun Hu, Xiaowen Li, Jiayi Xing, Boyang Peng, Yan Liang, Bin School of Future Technology Shanghai University Shanghai200444 China Navigation and Control Research Center Department of Automation Tsinghua University Beijing100084 China National and Local Co-Built Humanoid Robotics Innovation Center China

This paper introduces Unity RL Playground, an open-source reinforcement learning framework built on top of Unity ML-Agents. Unity RL Playground automates the process of training mobile robots to perform various locomotion tasks such as walking, running, and jumping in simulation, with the potential for seamless transfer to real hardware. Key features include one-click training for imported robot models, universal compatibility with diverse robot configurations, multi-mode motion learning capabilities, and extreme performance testing to aid in robot design optimization and morphological evolution. The attached video can be found at https://***/video/***4 and the code is coming soon. © 2025, CC BY.

关键词： Reinforcement learning

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controllable Mechanical-domain Energy Accumulators

Controllable Mechanical-domain Energy Accumulators

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sung Y. Kim David J. Braun Department of Mechanical Engineering Advanced Robotics and Control Laboratory within the Center for Rehabilitation Engineering and Assistive Technology Vanderbilt University Nashville TN

Springs are efficient in storing and returning elastic potential energy but are unable to hold the energy they store in the absence of an external load. Lockable springs use clutches to hold elastic potential energy in the absence of an external load, but have not yet been widely adopted in applications, partly because clutches introduce design complexity, reduce energy efficiency, and typically do not afford high fidelity control over the energy stored by the spring. Here, we present the design of a novel lockable compression spring that uses a small capstan clutch to passively lock a mechanical spring. The capstan clutch can lock over 1000 N force at any arbitrary deflection, unlock the spring in less than 10 ms with a control force less than 1% of the maximal spring force, and provide an 80% energy storage and return efficiency (comparable to a highly efficient electric motor operated at constant nominal speed). By retaining the form factor of a regular spring while providing high-fidelity locking capability even under large spring forces, the proposed design could facilitate the development of energy-efficient spring-based actuators and robots.

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TacRot: A Parallel-Jaw Gripper with Rotatable Tactile Sensors for In-Hand Manipulation

TacRot: A Parallel-Jaw Gripper with Rotatable Tactile Sensor...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Wuyi Xia, Chongkun Zhu, Xiaojun Liu, Houde Liang, Bin Tsinghua Shenzhen International Graduate School Center for Artificial Intelligence and Robotics Shenzhen518055 China Beijing University of Posts and Telecommunications School of Automation Beijing100876 China Tsinghua University Navigation and Control Research Center Department of Automation Beijing100084 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Finger dexterity and tactile perception are key capabilities for humans to manipulate objects within hand, as well as robots. Inspired by the thumb-forefinger dexterous manipulative movement, we devised a novel robotic finger with an active rotational tactile sensor (i.e. TacRot), and mounted the finger on a parallel-jaw gripper. By processing the high-resolution images of the vision-based tactile sensor, we achieved depth reconstruction of the surface and localization of the contact area. To improve gripping flexibility and stability, we applied a self-adaptive grasping strategy with real-time contact detection feedback, which performed 94% success rate in experiment. Based on the rotational actuator at the fingertip, we proposed two in-hand manipulation primitives: (1) pivot: fingertips co-rotating for object reorientation;(2) twist: fingertips contra-rotating for object spin. The primitives are theoretically analyzed and experimentally verified in two practical tasks: pivoting a paper cup under vertical constraints and twisting a screw with spin angle estimation. Our design and experiments demonstrate a feasible way to enhance the active tactile manipulation ability for common parallel-jaw grippers. © 2022 IEEE.

关键词： Grippers

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Modeling and Preliminary Characterization of a Large Differential-Drive Mobile Robot 26

Modeling and Preliminary Characterization of a Large Differe...

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26th robotics Mexican Congress, COMRob 2024

作者： Zepeda-Hernández, José A. Santos-Ruiz, Ildeberto Parra-Vega, Vicente López-Estrada, Francisco R. I.T. Tuxtla Gutiérrez Turix-Dynamics Diagnosis and Control Group Tecnológico Nacional de México Tuxtla Gutiérrez29050 Mexico Center for Research and Advanced Studies Centro de Investigatión y Estudios Avanzados Del IPN Robotics and Advanced Manufacturing Ramos Arizpe25903 Mexico

ISBN: (纸本)9798331533397

A comprehensive procedure is presented for deriving the kinematic and dynamic models of an electrically driven differential drive mobile robot (DDMR) with four wheels, configured in a 2:1 arrangement. The synthesis of this Lagrangian formulation, subject to non-holonomic constraints, includes the dynamics of the motors for each wheel. This model is characterized using the Turix VE 2.0 mobile robot from the control Group Laboratory, aimed at executing arbitrary trajectories comparing it with an experimental data set based on sensor fusion from an IMU (Inertial Measurement Unit), encoder on the axis of each motor and the extended Kalman *** results show consistent data validating the effectiveness of the proposed modeling approach, using parameters from a real robot. Valuable insights are obtained that guide the next phase of closed-loop control, highlighting the importance of the third-order model, which integrates motor dynamics into the motion control architecture. These findings provide a foundation for the design and development of a robotic autonomous vehicle of these dimensions. © 2024 IEEE.

关键词： Extended Kalman filters

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An Approach to the Extraction of Intersecting Pipes Weld Seam Based on 3D Point Cloud 11

An Approach to the Extraction of Intersecting Pipes Weld Sea...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Yang, Shuai Shi, Xiaorui Tian, Xincheng Liu, Yan Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China SINOTRUK Jinan Power Co. Ltd. Jinan250220 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a method based on point cloud for weld seam extraction of intersecting pipes workpiece is proposed. Firstly, a lightweight clustering algorithm based on the normal vector direction was proposed to extract the feature points of the weld seam. After filtering the feature points, a novel key points extraction algorithm was proposed to extract the key points of the weld seam. After that, a non-Uniform Rational B-Spline curve was used to approximate the key points and the preliminary spatial weld curve was obtained. Finally, an iterative algorithm was adopted to optimize the generated weld curve. The proposed method is verified on the ideal and real intersecting pipes point cloud models. Experiments show that the proposed method has high accuracy and robustness, and can accurately extract the intersecting pipes weld seam curve, providing guidance for the subsequent accurate automatic welding. © 2022 IEEE.

关键词： Extraction

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Multi-Agent Reinforcement Learning for Cooperative Vehicle Motion control

Multi-Agent Reinforcement Learning for Cooperative Vehicle M...

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International Conference on Intelligent Transportation

作者： Kenan Ahmic Johannes Ultsch Jonathan Brembeck Darius Burschka Institute of System Dynamics and Control at the German Aerospace Center (DLR) Wessling Germany Chair of Robotics Artificial Intelligence and Embedded Systems Technical University of Munich (TUM) Munich Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The longitudinal and lateral low-level motion control of multiple vehicles within a platoon is a challenging task, since several different control objectives need to be solved: (i) Each vehicle in the platoon needs to follow the reference path, (ii) the leading vehicle needs to drive with a desired reference velocity, and (iii) the following vehicles need to maintain a safe spacing distance to their respective preceding vehicle. Typically, several distinct controllers are developed for each task individually, which increases both the engineering effort as well as the susceptibility to errors. We address this issue and present a cooperative low-level vehicle motion controller based on Multi-Agent Reinforcement Learning (MARL) that is able to solve all of the above-mentioned control objectives for both the leading vehicle and the following vehicles. Therefore, we apply parameter sharing within MARL to update a single control policy in a centralized fashion using the experiences of all vehicles in the environment. Additionally, we utilize the concept of agent indication during the training process and enable the policy to specialize on the control objectives of the vehicle it is currently controlling. This leads to a unifying control approach and makes the development of further controllers redundant. The simulative assessment demonstrates the effectiveness of learned policy and shows that it is able to successfully solve all of the above-mentioned control objectives for both vehicles roles, even on unseen paths.

关键词： Training Process control Reinforcement learning Drives Vectors Robustness Collision avoidance Intelligent transportation systems

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Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots

Nonlinear Modes as a Tool for Comparing the Mathematical Str...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Pietro Pustina Davide Calzolari Alin Albu-Schäffer Alessandro De Luca Cosimo Della Santina Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Cognitive Robotics Delft University of Technology Delft Netherlands Institute of Robotics and Mechatronics German Aerospace Center Oberpfaffenhofen Germany Department of Informatics Technical University of Munich Garching Germany

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory -called eigenmanifolds- as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model.

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