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Lifted Bilinear Model-based Linear Model Predictive control with Scalability

IFAC-PapersOnLine 2023年第2期56卷 9405-9410页

作者： Masaki Kanai Masaki Yamakita Controls and Robotics Innovation Center Research & Development Group Ltd. Hitachi Ibaraki Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

We propose a novel linear model predictive control (MPC) using a lifted bilinear model based on Koopman theory, which is computationally scalable against the dimension of the target system and the prediction horizon length. In MPC, the accuracy of the prediction model determines control performance, but it is a challenge to reduce the computational cost especially when considering nonlinearity of the model. To address this, a method has been proposed which represents the nonlinear input affine system as a lifted bilinear model and utilizes linear approximation and prediction error correction regarding the lifted state to achieve a low-computational-cost linear MPC with equivalent performance to nonlinear MPC. However, although the previous studies have shown its effectiveness for relatively low-order systems, it has not been applied to practical systems with higher dimensions. In this study, we extend the conventional method and propose a scalable linear MPC using a lifted bilinear model and apply it to higher-order nonlinear systems. In this paper, a quadrotor system operating in three-dimensional space is considered and its analytical lifted bilinear model is derived. In the formulation of linear MPC using the lifted bilinear model, an error correction method is newly introduced to feedback error for adjustment of the numerical relationships among the elements in the lifted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

关键词： Model predictive control Koopman theory Lifting linearization Bilinear system

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Design of a Flexible Robot Arm for Safe Aerial Physical Interaction

Design of a Flexible Robot Arm for Safe Aerial Physical Inte...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Julien Mellet Andrea Berra Achilleas Santi Seisa Viswa Sankaranarayanan Udayanga G.W.K.N. Gamage Miguel Ángel Trujillo Soto Guillermo Heredia George Nikolakopoulos Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Robotics and AI Team Electrical and Space Engineering LuleåUIniversity of Technology Luleå Sweden Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction.

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

arXiv

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arXiv 2024年

作者： Berra, Andrea Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Mellet, Julien Gamage, Udayanga G.W.K.N. Satpute, Sumeet Gajanan Ruggiero, Fabio Lippiello, Vincenzo Tolu, Silvia Fumagalli, Matteo Nikolakopoulos, George Soto, Miguel Ángel Trujillo Heredia, Guillermo CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications

A Cost-Effective Smart Labor Assistance Trolley for Industri...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muneeb Zafar Sarmad Shafique Faisal Riaz Samia Abid Umar Raza William Holderbaum Department of CS & IT National Center of Robotics and Automation (NCRA) Control Automotive and Robotics (CAR) Mirpur University of Science and Technology Mirpur Pakistan Department of Engineering Manchester Metropolitan University Manchester United Kingdom

In the last couple of decades, autonomous human assistance robots have been enormously attracting the industrial sector. For this purpose, numerous researchers have contributed towards designing efficient and robust human assistance mechanisms. However, their proposed approaches do not provide a cost-effective solution due to the deployment of exorbitant sensors and sophisticated infrastructure. Besides, it was quite challenging for existing human-following robots to track their assigned human companion in different illusional states and luminous conditions while detecting obstacles and taking respective maneuvers (i.e., abrupt turns, etc.). Moreover, self-driving solutions need to take fast and real-time actions to avoid collisions in the designated environment. For this purpose, literature has shown the efficiency of YOLOv3 with respect to providing real-time results in latency-sensitive applications. Hence, to overcome this dilemma, we propose to develop an economically efficient deep learning- based smart labor assistance trolley that uses the YOLO v3 as a core detective deep learning technique with advance and efficient perception and motion planning modules. The perception module succours the autonomous trolley to precisely detect and classify the objects. While the motion planning module uses the specific intended target detection technique to follow the targeted person in crowded environment. These techniques make the autonomous trolley able to take expeditious, meticulous, and conspicuous action in real-time. The labor assistant robot detects and tracks the respective person using YOLOv3. To validate the efficiency of the proposed solution, we have performed a series of experiments considering different test cases. Our proposed work achieved a mean average precision of 0.81%.

关键词： Automation Target tracking Service robots Transportation Object detection Robot sensing systems Real-time systems

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Challenges when Partially Training a Machine Learning-Based Optical Communication System in Variable Experimental Conditions

Challenges when Partially Training a Machine Learning-Based ...

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Propagation Through and Characterization of Atmospheric and Oceanic Phenomena, pcAOP 2022

作者： Jarrett, William A. Avramov-Zamurovic, Svetlana Esposito, Joel M. Nelson, Charles Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States Electrical Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States

We present challenges when training a machine learning-based underwater wireless optical communication system in selected experimental scenarios. The system is tested under different conditions, that include minor bea... 详细信息

ISBN: (纸本)9781557528209

关键词： Machine learning

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Design of a Flexible Robot Arm for Safe Aerial Physical Interaction

arXiv

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arXiv 2024年

作者： Mellet, Julien Berra, Andrea Seisa, Achilleas Santi Sankaranarayanan, Viswa Gamage, Udayanga G.W.K.N. Soto, Miguel Ángel Trujillo Heredia, Guillermo Nikolakopoulos, George Lippiello, Vincenzo Ruggiero, Fabio PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors’ integrity. Notably, the arm’s damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction. © 2024, CC BY.

关键词： End effectors

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An Energy-Efficient Communication Protocol for Power-Constrained IoT Networks: A Deep Reinforcement Learning Approach

An Energy-Efficient Communication Protocol for Power-Constra...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Syed Asad Ullah Syed Muhammad Khalid Umair Ahmed Korai Anayat Ullah Department of Electronic Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) BUITEMS Quetta Pakistan Department of Telecommunication Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

Power-limited devices (or sensors) constrain the deployment of modern IoT networks, such as Next-Generation Industrial IoT (NG-IIoT). These networks are envisioned as the key enablers to facilitate connectivity to billions of devices for applications like smart industries, smart healthcare, etc. This paper presents an optimal up-link communication protocol for a power-limited unlicensed sensor operating among numerous licensed sensors communicating in a time division multiple access (TDMA)-based scheme. The transmission of the power-limited sensor is ensured by employing the non-orthogonal multiple access (NOMA) technique during the time slot of a licensed sensor. To ensure energy-efficient communication, we maximize the throughput of the power-limited sensor using a deep reinforcement learning (DRL) framework recognized as a combined experience replay deep deterministic policy gradient (CER-DDPG) algorithm. Our simulation results demonstrated that the CER-DDPG-based communication protocol outperforms the benchmark schemes, such as DDPG and stochastic algorithms, in terms of throughput.

关键词： Optical losses NOMA Protocols Simulation Reinforcement learning Throughput Energy efficiency

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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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Neural Network Regression Analysis of Magnetic Sensor Data for Spatial Magnet Positioning

Neural Network Regression Analysis of Magnetic Sensor Data f...

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Electrical, Electronic and Computing engineering (IcETRAN), International Conference on

作者： Vladimir Sibinović Mirko Raković Department of Automatic Control Faculty of Electronic Engineering University of Niš Niš Serbia Chair of Mechatronics Robotics and Automation Faculty of Technical Science Novi Sad Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

This paper deals with the magnet position estimation based on the measurements from multiple Hall sensors. Estimation of the position is obtained from trained artificial neural networks. Data are gathered using a custom sensor board that has eight three-axis Hall sensors on it, and correlation between magnetic field and position was determined to be able to estimate position in X and Y directions. First measurements were made to determine the optimal height of the magnet for later data gathering. The outcome of the research was evaluated by comparing the ground truth with the estimated values of the best-trained network to determine the error of estimation. Precise measurement of the position of the magnet is intended to be used for the development of a tactile sensor that estimates the contact force through the displacement of the magnet in the deformable layer.

关键词： Magnetic field measurement Systematics Magnetic sensors Layout Estimation Tactile sensors Artificial neural networks

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