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SSRN 2022年

作者： Petrović, Milica Jokić, Aleksandar Miljković, Zoran Kulesza, Zbigniew University of Belgrade Faculty of Mechanical Engineering Department of Production Engineering Laboratory for Industrial Robotics and Artificial Intelligence Kraljice Marije 16 35 Belgrade11120 Serbia University of Belgrade Faculty of Mechanical Engineering Department of Production Engineering Kraljice Marije 16 35 Belgrade11120 Serbia Bialystok University of Technology Faculty of Electrical Engineering Department of Automatic Control and Robotics Wiejska 45D Bialystok15-351 Poland

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used to fulfill material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually be in conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously faced with several conflicting objectives is a highly challenging task. In order to address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation for 13 novel fitness functions, which are combined to form a Pareto front of the multi-objective optimization problem, as well as a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with two state-of-the-art metaheuristic methods (MOGA, MOAOA) on 25 benchmark problems. The obtained results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and robust optimal Pareto solution obtained. Finally, transportation paths based on obtained scheduling plans are experimentally validated by mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of materia

关键词： Mobile robots

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A social power game for the concatenated Friedkin-Johnsen model

A social power game for the concatenated Friedkin-Johnsen mo...

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IEEE Conference on Decision and control

作者： Lingfei Wang Guanpu Chen Yiguang Hong Guodong Shi Claudio Altafini Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences and also with the University of Chinese Academy of Sciences Beijing China JD Explore Academy Beijing China Department of Control Science and Engineering Tongji University Shanghai China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Sydney Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

If a concatenated Friedkin-Johnsen model is used to describe the evolution of the opinions of stubborn agents in a sequence of discussion events, then the social power achieved by the agents at the end of the discussions depends from the stubbornness coefficients adopted by the agents through the sequence of events. In this paper we assume that the agents are free to choose their stubbornness profiles, and ask ourselves what strategy should an agent follow in order to maximize its social power. Formulating the problem as a strategic game, we show that choosing the highest possible values of stubbornness in the early discussions leads to the highest possible social power.

关键词： Games

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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE controllers Under External Uncertainties

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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Achieving consensus in spite of stubbornness: time-varying concatenated Friedkin-Johnsen models

Achieving consensus in spite of stubbornness: time-varying c...

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IEEE Conference on Decision and control

作者： L. Wang C. Bernardo Y. Hong F. Vasca G. Shi C. Altafini Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Group for Research on Automatic Control Engineering University of Sannio Benevento Italy Tongji University Shanghai China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Sydney Division of Automatic Control Linköping University Linköping Sweden

ISBN: (纸本)9781665436601

A concatenated Friedkin-Johnsen (FJ) model is a two time-scale opinion dynamics model in which stubborn agents discuss a sequence of issues. For each issue, a FJ model is adopted, and concatenation refers to the fact that the final opinion of the agents at issue s becomes the initial condition at issue s + 1. In this paper we deal with the case in which the system is open, i.e., the group of interacting agents changes at each issue, and so does their stubbornness. A concatenated FJ model can in this case be represented as an infinite product of stochastic matrices. For such system, we obtain sufficient conditions under which the opinions of the agents converge to consensus.

关键词： Sufficient conditions Conferences Stochastic processes

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Optimisation of a siamese neural network for real-time energy efficient object tracking

arXiv

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arXiv 2020年

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations - from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. Copyright © 2020, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Optimisation of a siamese neural network for real-time energy efficient object tracking

TechRxiv

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TechRxiv 2020年

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, CC BY-NC-SA.

关键词： Field programmable gate arrays (FPGA)

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Simultaneous tactile exploration and grasp refinement for unknown objects

arXiv

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Mechanical Design and control of an Underwater Robot for Pitch and Roll Stability 3

Mechanical Design and Control of an Underwater Robot for Pit...

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3rd International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2019

作者： Hachicha, Saber Zaoui, Chiheb Rabhi, Yassine Dallagi, Habib Nejim, Samir Maalej, Aref Automatic Control Marine Robotics Naval Academy Tunisia Laboratory of Signal Image and Energy Mastery National Higher School of Engineering of Tunis Tunisia Laboratory of Electromechanical Systems University of Sfax Tunisia

ISBN: (纸本)9781728113173

The use of the underwater robot for ship hull cleaning is increasingly studied given the valuable operational, economic and environmental advantages that it offers. The automatic control and Marine robotics Unit, within the Tunisian Naval Academy, has suggested a new underwater robot design that is equipped with two manipulating arms. The offered solution allows to combine a system portability with the improved cleaning efficiency offered by the manipulator arms. However, during underwater operation, the robot, located beneath the ship's hull, is exposed to several perturbation forces. These forces are mainly caused by water current and waves. This research focuses on the robot static and dynamic stability when it is exposed to external perturbations. The proposed approach to ensure the robot stability consists in improving its mechanical design. Therefore, we developed the design of the scaled robot to eliminate static behavior. The dynamic model was established and the electronic architecture was described. Besides, the control scheme was proposed. In conclusion, our study demonstrates the importance of the mechanical symmetry design and the control law to ensure the stability of the underwater robot. © 2019 IEEE.

关键词： Robots

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Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion during Conventional Microsurgery Using Machine Learning

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Journal of Medical robotics Research 2021年第3-4期6卷

作者： Fattahi Sani, Mohammad Ascione, Raimondo Dogramadzi, Sanja Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Bristol Heart Institute and Translational Biomedical Research Centre Faculty of Health Science University of Bristol Bristol United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffiled United Kingdom

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-Time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (M

关键词： feature extraction hand tracking machine learning minimally invasive surgery real-Time low-cost hand tracking Robot-Assisted surgery

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Development a cross-loop during adaptive stimulation of hippocampal neural networks by an artificial neural network 4

Development a cross-loop during adaptive stimulation of hipp...

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4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022

作者： Lebedeva, Albina Beltyukova, Anna Fedulina, Anastasiya Gerasimova, Svetlana Mishchenko, Mikhail Matveeva, Mariya Maltseva, Kseniya Belov, Alexey Mikhaylov, Alexey Pisarchik, Alexander Kazantsev, Victor University of Nizhny Novgorod Neurotechnologies Department N.I. Lobachevsky State Nizhny Novgorod Russia University of Nizhny Novgorod Faculty of Radiophysics N.I. Lobachevsky State Department of Theory of Oscillations and Automatic Control Nizhny Novgorod Russia Intitute of Neuroscience N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia Center for Neuroscience and Mashine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (数字)9781665463294

ISBN: (纸本)9781665463294

The development of promising neuromorphic compact devices (including micro- and nanochips) that reproduce the information and computational functions of the brain is currently one of the breakthrough areas of modern science and technology. Firstly, this is a promising direction in regenerative medicine - the creation of artificial neuromorphic implants that allow the regulation and modulation of electrically excitable cells, as well as replacing damaged areas of the brain. The use of a memristive structure and its ability to change and maintain internal resistance when voltage is applied and turned off. This makes the structure an almost perfect electronic analogue of synapses. Recent discoveries that nanoscale memristors can mimic the basic properties of synaptic plasticity have set the stage for the fusion of nanoelectronics and living neurons to create biohybrid neural networks (bNNs) interconnected by electronics-based plasticity elements. Thus, investigation the interaction of artificial neural networks and memristive connection with living brain neurons is actually problem in neuroscience and technologies. This study is devoted to the development of neuromorphic system for neuroprosthetics problems. © 2022 IEEE.

关键词： Memristors

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