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Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world lab.ratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalab.lity of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Situational awareness in an electric utility's control center of its generators' damping capabilities

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Situational awareness in an electric utility's control cente...

2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

作者： Arunagirinathan, Paranietharan Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Lab. Holcombe Department of Electrical and Computer Engineering Clemson University SC29634 United States Eskom Centre of Excellent in HVDC Engeneering University of Kwazulu Natal Duraban South Africa

ISBN: (纸本)9781538628904

Poorly damped low frequency oscillations in power systems have led to system blackouts in the past. Power system stabilizers (PSSs) are installed to improve synchronous generator's oscillation damping capabilities. PSS parameter tuning by various traditional and new optimization methods are studied to ensure stable and secure operation of interconnected power systems with uncertainties. It is imperative to evaluate the tuned PSSs with the different methods to ensure continued security of a power system during operation. There is a lack of such situational awareness at electric utility control centers to assist system operators with the knowledge of synchronous generator's oscillation damping capabilities. Since the human brain responses to images and colors faster compared to text and numbers, it is desirable to develop advanced visualizations and avail them to system operators. These visualizations could utilize real-time data from phasor measurement units. This paper presents the development of a new tool for use in control centers for modal analysis visualization (MAV). Modal analysis is performed on oscillations exhibited by generators in a power system. The MAV enhances situational awareness at control centers by providing near real-time insight into generators' oscillation damping capabilities. © 2017 IEEE.

关键词： Phasor measurement units

SEA: A Combined Model for Heat Demand Prediction

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SEA: A Combined Model for Heat Demand Prediction

IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Jiyang Xie Jiaxin Guo Zhanyu Ma Jing-Hao Xue Qie Sun Hailong Li Jun Guo Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China Department of Statistical Science University College London London United Kingdom Institute of Thermal Science and Technology Shandong University Jinan China Society and Engineering Mälardalen University Västerås Sweden

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

关键词： Heating systems Predictive models Market research Data models Time series analysis Load forecasting Neural networks

Incremental learning of high-level concepts by imitation

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arXiv 2017年

作者： Alibeigi, Mina Ahmadabadi, Majid Nili Araabi, Babak Nadjar Cognitive Systems Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Cognitive Systems Lab. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Iran

Nowadays, robots become a companion in everyday life. To be well-accepted by humans, robots should efficiently understand meanings of their partners’ motions and body language, and respond accordingly. Learning concepts by imitation brings them this ability in a user-friendly way. This paper presents a fast and robust model for Incremental Learning of Concepts by Imitation (ILoCI). In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation. In this method, perceptually similar demonstrations are abstracted by a dynamic model of mirror neuron system. An incremental method is proposed to learn their functional similarities through a limited number of interactions with the teacher. Learning all concepts together by the proposed memory rehearsal enables robot to utilize the common structural relations among concepts which not only expedites the learning process especially at the initial stages, but also improves the generalization ability and the robustness against discrepancies between observed demonstrations. Performance of ILoCI is assessed using standard LASA handwriting benchmark data set. The results show efficiency of ILoCI in concept acquisition, recognition and generation in addition to its robustness against variability in demonstrations. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

A Projection-Based Algorithm for Constrained L1- Minimization Optimization with Application to Sparse Signal Reconstruction

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A Projection-Based Algorithm for Constrained L1- Minimizatio...

International Conference on Information Science and Technology (ICIST)

作者： Qingshan Liu Wei Zhang Jiang Xiong Bingrong Xu Long Cheng School of Mathematics Southeast University Nanjing China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Automation Huazhong University of Science and Technolog< Wuhan China State Key Lab. of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China

In this paper, a projection-base algorithm is proposed for solving the constrained L 1 -minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L 1 -minimization is first converted into some equations which are described by the projections onto a hyper box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L 1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L 1 -minimization problem subject to L ∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.

关键词： Signal processing algorithms Signal reconstruction Optimization Convergence Electronic mail Recurrent neural networks Lyapunov methods

Learning task-specific dynamics to improve whole-body control

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arXiv 2018年

作者： Gams, Andrej Mason, Sean A. Ude, Aleš Schaal, Stefan Righetti, Ludovic Humanoid and Cognitive Robotics Lab Dept. of Automatics Bio-cybernetics and Robotics Jožef Stefan Institute Ljubljana Slovenia Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Tandon School of Engineering New York University New York United States Max Planck Institute for Intelligent Systems Tuebingen Germany

In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, good tracking accuracy often necessitates high feedback gains, which leads to undesirable stiff behaviors. The magnitude of these gains is anyways often strongly limited by the control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedback

Effects of drug resistance in the tumour-immune system with chemotherapy treatment

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arXiv 2020年

作者： Trobia, José Gabrick, Enrique C. Seifert, Evandro G. Borges, Fernando S. Protachevicz, Paulo R. Szezech, José D. Iarosz, Kelly C. Santos, Moises S. Caldas, Iberê L. Tian, Kun Ren, Hai-Peng Grebogi, Celso Batista, Antonio M. Graduate Program in Science - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Department of Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculty of Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal Technological University of Paraná Ponta Grossa Paraná84016-210 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Technological University Xi’an710021 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise the cancerous cells and attack them, though, it can be weakened by the cancer. One type of cancer treatment is chemotherapy, which uses drugs to kill cancer cells. Clinical, experimental, and theoretical research has been developed to understand the dynamics of cancerous cells with chemotherapy treatment, as well as the interaction between tumour growth and immune system. We study a mathematical model that describes the cancer growth, immune system response, and chemotherapeutic agents. The immune system is composed of resting cells that are converted to hunting cells to combat the cancer. In this work, we consider drug sensitive and resistant cancer cells. We show that the tumour growth can be controlled not only by means of different chemotherapy protocols, but also by the immune system that attacks both sensitive and resistant cancer cells. Furthermore, for all considered protocols, we demonstrate that the time delay from resting to hunting cells plays a crucial role in the combat against cancer cells. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemotherapy

Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary systems

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IFAC-PapersOnLine 2017年第1期50卷 3424-3428页

作者： Ghallab.A.G. Mabrok M.A. Petersen I.R. School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy Canberra ACT 2600 Australia Robotics Intelligent Systems and Control Lab at Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology (KAUST) Saudi Arabia Research School of Engineering The Australian National University Canberra ACT 2601 Australia

Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true. © 2017

关键词： Lyapunov stability Negative imaginary lemma Negative imaginary systems Strictly negative imaginary systems