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CaRoSaC: A Reinforcement Learning-Based Kinematic control of Cable-Driven Parallel Robots by Addressing Cable Sag Through Simulation

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IEEE Robotics and Automation Letters 2025年第6期10卷 5345-5352页

作者： Dhakate, Rohit Jantos, Thomas Allak, Eren Weiss, Stephan Steinbrener, Jan University of Klagenfurt Control of Networked Systems Research Group Institute of Smart Systems Technologies Klagenfurt9020 Austria

This letter introduces the Cable Robot Simulation and control (CaRoSaC) Framework, which integratesa realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach seeks to bridge the knowledge gap of the intricacies of CDPRs due to aspects such as cable sag and precision control necessities, which are missing in existing research and often overlooked in traditional models, by establishing a simulation platform that captures the real-world behaviors of CDPRs, including the impacts of cable sag. The framework offers researchers and developers a tool to further develop estimation and control strategies within the simulation for understanding and predicting the performance nuances, especially in complex operations where cable sag can be significant. Using this simulation framework, we train a model-free control policy rooted in Reinforcement Learning (RL). This approach is chosen for its capability to adaptively learn from the complex dynamics of CDPRs. The policy is trained to discern optimal cable control inputs, ensuring precise end-effector positioning. Unlike traditional feedback-based control methods, our RL control policy focuses on kinematic control and addresses the cable sag issues without being tethered to predefined mathematical models. We also demonstrate that our RL-based controller, coupled with the flexible cable simulation, significantly outperforms the classical kinematics approach, particularly in dynamic conditions and near the boundary regions of the workspace. The combined strength of the described simulation and control approach offers an effective solution in manipulating suspended CDPRs even at workspace boundary conditions where traditional approach fails, as proven from our experiments, ensuring that CDPRs function optimally in various applications while accounting for the often neglected but critical factor

关键词： End effectors

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Autonomous control of Redundant Hydraulic Manipulator Using Reinforcement Learning with Action Feedback

arXiv

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arXiv 2025年

作者： Dhakate, Rohit Brommer, Christian Böhm, Christoph Weiss, Stephan Steinbrener, Jan Gietler, Harald Department of Smart Systems Technologies the Control of Networked Systems Group University of Klagenfurt Klagenfurt9020 Austria Department of Smart Systems Technologies the Sensors and Actuators Group University of Klagenfurt Klagenfurt9020 Austria

This article presents an entirely data-driven approach for autonomous control of redundant manipulators with hydraulic actuation. The approach only requires minimal system information, which is inherited from a simulation model. The non-linear hydraulic actuation dynamics are modeled using actuator networks from the data gathered during the manual operation of the manipulator to effectively emulate the real system in a simulation environment. A neural network control policy for autonomous control, based on end-effector (EE) position tracking is then learned using Reinforcement Learning (RL) with Ornstein–Uhlenbeck process noise (OUNoise) for efficient exploration. The RL agent also receives feedback based on supervised learning of the forward kinematics which facilitates selecting the best suitable action from exploration. The control policy directly provides the joint variables as outputs based on provided target EE position while taking into account the system dynamics. The joint variables are then mapped to the hydraulic valve commands, which are then fed to the system without further modifications. The proposed approach is implemented on a scaled hydraulic forwarder crane with three revolute and one prismatic joint to track the desired position of the EE in 3-Dimensional (3D) space. With the emulated dynamics and extensive learning in simulation, the results demonstrate the feasibility of deploying the learned controller directly on the real system. © 2025, CC BY-NC-ND.

关键词： Reinforcement learning

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CaRoSaC: A Reinforcement Learning-Based Kinematic control of Cable-Driven Parallel Robots by Addressing Cable Sag through Simulation

arXiv

引用

arXiv 2025年

作者： Dhakate, Rohit Jantos, Thomas Allak, Eren Weiss, Stephan Steinbrener, Jan Control of Networked Systems Research Group The Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt9020 Austria

This paper introduces the Cable Robot Simulation and control (CaRoSaC) Framework, which integrates a realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach seeks to bridge the knowledge gap of the intricacies of CDPRs due to aspects such as cable sag and precision control necessities, which are missing in existing research and often overlooked in traditional models, by establishing a simulation platform that captures the real-world behaviors of CDPRs, including the impacts of cable sag. The framework offers researchers and developers a tool to further develop estimation and control strategies within the simulation for understanding and predicting the performance nuances, especially in complex operations where cable sag can be significant. Using this simulation framework, we train a model-free control policy rooted in Reinforcement Learning (RL). This approach is chosen for its capability to adaptively learn from the complex dynamics of CDPRs. The policy is trained to discern optimal cable control inputs, ensuring precise end-effector positioning. Unlike traditional feedback-based control methods, our RL control policy focuses on kinematic control and addresses the cable sag issues without being tethered to predefined mathematical models. We also demonstrate that our RL-based controller, coupled with the flexible cable simulation, significantly outperforms the classical kinematics approach, particularly in dynamic conditions and near the boundary regions of the workspace. The combined strength of the described simulation and control approach offers an effective solution in manipulating suspended CDPRs even at workspace boundary conditions where traditional approach fails, as proven from our experiments, ensuring that CDPRs function optimally in various applications while accounting for the often neglected but critical factor

关键词： Reinforcement learning

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Modular Meshed Ultra-Wideband Aided Inertial Navigation with Robust Anchor Calibration

arXiv

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arXiv 2024年

作者： Jung, Roland Santoro, Luca Brunelli, Davide Fontanelli, Daniele Weiss, Stephan The Department of Smart Systems Technologies The Control of Networked Systems Group University of Klagenfurt Austria The Department of Industrial Engineering University of Trento Italy

This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity - a perquisite for handling and processing meshed range measurements among a time-varying set of devices. In order to utilize these measurements in the estimation framework, positions of newly detected stationary devices (anchors) and the pairwise biases between the ranging devices are required. In this work an autonomous calibration procedure for new anchors is presented, that utilizes range measurements from multiple tags as well as already known anchors. To improve the robustness, an outlier rejection method is introduced. After the calibration is performed, the sensor fusion framework obtains initial beliefs of the anchor positions and dictionaries of pairwise biases, in order to fuse range measurements obtained from new anchors tightly-coupled. The effectiveness of the filter and calibration framework has been validated through evaluations on a recorded dataset and real-world experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Inertial navigation systems

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Modular Meshed Ultra-Wideband Aided Inertial Navigation with Robust Anchor Calibration

Modular Meshed Ultra-Wideband Aided Inertial Navigation with...

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

作者： Roland Jung Luca Santoro Davide Brunelli Daniele Fontanelli Stephan Weiss Department of Smart Systems Technologies Control of Networked Systems Group University of Klagenfurt Austria Department of Industrial Engineering University of Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity – a perquisite for handling and processing meshed range measurements among a time-varying set of devices. In order to utilize these measurements in the estimation framework, positions of newly detected stationary devices (anchors) and the pairwise biases between the ranging devices are required. In this work an autonomous calibration procedure for new anchors is presented, that utilizes range measurements from multiple tags as well as already known anchors. To improve the robustness, an outlier rejection method is introduced. After the calibration is performed, the sensor fusion framework obtains initial beliefs of the anchor positions and dictionaries of pairwise biases, in order to fuse range measurements obtained from new anchors tightly-coupled. The effectiveness of the filter and calibration framework has been validated through evaluations on a recorded dataset and real-world experiments.

关键词： Performance evaluation Protocols Position measurement Sensor fusion Distance measurement Robustness Real-time systems Calibration State estimation Ultra wideband technology

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A Framework for the Consistency Analysis of Relative Pose Sensors for Unmanned Aerial Vehicles (Uavs)

A Framework for the Consistency Analysis of Relative Pose Se...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Roland Jung Jiri Horyna Thomas Jantos Saska Martin Stephan Weiss Department of Smart Systems Technologies in the Control of Networked Systems Group University of Klagenfurt Austria Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In autonomous multi-robot systems robot-to {robot/object} localization methods can be utilized to increase the robustness and to achieve a precise and robust localization of the individuals. This paper investigates on the performance of two promising systems: UVDAR, a vision-based mutual localization in the UV spectrum, which has shown to be effective in swarm formation and leader-following tasks, and PoET, which is a deep learning-based visual relative object pose estimator. To evaluate these methods, we collected datasets in a controlled indoor environment equipped with a motion capture system for precise ground truth measurements. Our evaluation considers two key aspects: the absolute error between measured and true relative poses, and the consistency of the provided measurement uncertainty estimates with the actual errors. We introduce a novel framework for evaluating the consistency of relative pose measurements. This framework supports various error definitions and leverages spline-based trajectory representations to generate smooth, $C^control$ -continuous reference measurements. Both the UVDAR dataset and the evaluation framework are made publicly accessible to foster further research and development in this field.

关键词： Location awareness Visualization Measurement uncertainty Sensors Trajectory Indoor environment Motion measurement Vehicle dynamics Robots Tuning

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Revisiting Multi-GNSS Navigation for UAVs – An Equivariant Filtering Approach

Revisiting Multi-GNSS Navigation for UAVs – An Equivariant ...

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International Conference on Advanced Robotics (ICAR)

作者： Martin Scheiber Alessandro Fornasier Christian Brommer Stephan Weiss Institute of Smart Systems Technologies Control of Networked Systems Group University of Klagenfurt Klagenfurt Austria

In this work, we explore the recent advances in equivariant filtering for inertial navigation systems to improve state estimation for uncrewed aerial vehicles (UAVs). Traditional state-of-the-art estimation methods, e.g., the multiplicative Kalman filter (MEKF), have some limitations concerning their consistency, errors in the initial state estimate, and convergence performance. Symmetry-based methods, such as the equivariant filter (EqF), offer significant advantages for these points by exploiting the mathematical properties of the system - its symmetry. These filters yield faster convergence rates and robustness to wrong initial state estimates through their error definition. To demonstrate the usability of EqFs, we focus on the sensor-fusion problem with the most common sensors in outdoor robotics: global navigation satellite system (GNSS) sensors and an inertial measurement unit (IMU). We provide an implementation of such an EqF leveraging the semi-direct product of the symmetry group to derive the filter equations. To validate the practical usability of EqFs in real-world scenarios, we evaluate our method using data from all outdoor runs of the INSANE Dataset. Our results demonstrate the performance improvements of the EqF in real-world environments, highlighting its potential for enhancing state estimation for UAVs.

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Revisiting multi-GNSS Navigation for UAVs - An Equivariant Filtering Approach

arXiv

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arXiv 2023年

作者： Scheiber, Martin Fornasier, Alessandro Brommer, Christian Weiss, Stephan Institute of Smart Systems Technologies Control of Networked Systems Group University of Klagenfurt Universitätsstrase 65-67 Klagenfurt9020 Austria

关键词： State estimation

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Correction: Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions

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European radiology experimental 2023年第1期7卷 36页

作者： Visar Arapi Alexander Hardt-Stremayr Stephan Weiss Jan Steinbrener Control of Networked Systems Research Group Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria. visar.arapi@aau.at. Control of Networked Systems Research Group Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria.

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Covariance Pre-Integration for Delayed Measurements in Multi-Sensor Fusion

Covariance Pre-Integration for Delayed Measurements in Multi...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Eren Allak Roland Jung Stephan Weiss Department of Smart Systems Technologies in the Control of Networked Systems Group Universität Klagenfurt Klagenfurt Austria Karl Popper School on Networked Autonomous Aerial Vehicles Alpen-Adria-Universität Klagenfurt Klagenfurt Austria

Delay compensation in filter based sensor fusion frameworks for multiple sensors with varying delays and different rates quickly results in large computational overhead should the delayed measurements be incorporated in a statistically meaningful way. Even more so if high rate propagation sensors (e.g. IMU) are used. This work presents an approach to implement such frameworks with significant complexity reduction compared to standard implementations. We set particular focus on the state covariance propagation as this chain of re-computations (i.e. FPF T + Q per propagation step) upon a delayed update is the dominant bottleneck. We draw our inspiration from the scattering theory and propose a method which projects the idea of wave propagation to an efficient concatenation of covariance propagation steps between filter updates. Through this approach, we reach a speed-up of more than a factor of 10 for the covariance propagation and render the computational complexity independent of the number of propagation steps between filter updates. We evaluated our method in simulation and with real data.

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