This paper considers a localized data-driven consensus problem for leader-follower multi-agent systems characterized by unknown linear agent dynamics, where each agent computes its local control gain using only its lo...
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The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive control (MPC...
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ISBN:
(纸本)9798350399462
The main goal of this paper is to present an end-to-end, data-driven framework for the control of Autonomous Electric Vehicles (AEV) for Mobility-on-Demand (MoD). We present a data-driven Model Predictive control (MPC) algorithm that rebalances (i.e. preemptively repositions) the AEV fleet in order to meet the mobility demand in the near future. The algorithm consists of Mixed Integer Linear Programming (MILP) that leverages the short-term forecast of the mobility demand as well as the charging station availability in order to optimally rebalance the AEV fleet. The proposed algorithm is evaluated by means of simulations with the New York City (NYC) taxi data. The proposed algorithm outperforms other state-of-the-art rebalancing strategies by reducing the mean customer wait time by 82.3% and the number of rejected requests by 94.6% for a given fleet size and a number of charging stations.
In robot manipulation skill learning, due to the complexity of the system, encoding the human demonstrations with a non-parametric learning method is always more effective. Gaussian Process is a data-driven approach t...
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Existing data-driven lane change trajectory prediction methods lack the capability to achieve broad consistency in the complex and variable real-world traffic scenarios due to their reliance on training with pre-colle...
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Transcatheter mitral valve procedures have transformed the treatment of mitral regurgitation and stenosis by providing less invasive alternatives to conventional open-heart surgery. However, they introduce stringent r...
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Transcatheter mitral valve procedures have transformed the treatment of mitral regurgitation and stenosis by providing less invasive alternatives to conventional open-heart surgery. However, they introduce stringent requirements for catheter navigation, force modulation, and real-time imaging in a highly dynamic cardiac environment. In this article, a comprehensive technical overview of continuum robotic catheter systems developed specifically for transcatheter mitral valve interventions is presented. Fundamental design principles of flexible, tendon-driven architectures are examined, highlighting their capacity to navigate tortuous vascular pathways and offer multi-degree-of-freedom control. The integration of advanced sensing technologies, real-time imaging methods, and intelligent control strategies is discussed. Clinical studies and in vivo validations are reviewed, underscoring critical performance metrics such as positional accuracy, procedural safety, and device miniaturization. Persistent challenges are also addressed, including limited high-fidelity data for machine learning, a lack of robust haptic feedback in delicate cardiac tissue manipulation, and regulatory hurdles for complex robotic platforms. Furthermore, emerging innovations in materials science, three-dimensional printing, and sensor fusion are explored, illustrating the potential for next-generation systems that enhance precision while reducing operator workload. Finally, key opportunities for future research are outlined, with an emphasis on personalized navigation algorithms, standardized evaluation protocols, and broader applicability in cardiovascular and endovascular procedures.
Indoor Air Quality (IAQ) significantly impacts people's health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusiv...
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ISBN:
(纸本)9798350369458;9798350369441
Indoor Air Quality (IAQ) significantly impacts people's health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. This paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. The system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system's effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.
Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynami...
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ISBN:
(纸本)9798350377712;9798350377705
Landing on a vertically oscillating platform poses a significant challenge for multi-rotor unmanned aerial vehicle (UAVs) due to the time-varying ground effect (GE). In this work, we formulated a data-driven GE dynamic model that accurately describes the complex interactions between UAVs and both stationary and oscillating platforms. Integrating this model with a feedforward controller effectively compensates for GE, resulting in improved landing performance. The proposed GE model elucidates the relationship between GE and factors such as UAVs' velocity, throttle magnitude, and the motion of the landing platform. It highlights that the GE experienced during the landing process of UAVs is not only contingent on the current state but also related to past states. The resulting GE model is parsimonious and suitable for onboard computers with limited computational power, and its accuracy has been confirmed through a series of flight experiments. To demonstrate the effectiveness of the developed UAVs landing scheme, we compared our approach with robust control and internal model control methods. Experimental results indicate that the proposed landing strategy achieves faster and smoother landings, with at least a 22% improvement in smoothness and a 25% reduction in landing time.
Cyber-physical systems, particularly those with extended service lives such as railways, often necessitate significant investment in maintenance activities encompassing repairs, upgrades, or inspections. These decisio...
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ISBN:
(纸本)9798350395525;9798350395518
Cyber-physical systems, particularly those with extended service lives such as railways, often necessitate significant investment in maintenance activities encompassing repairs, upgrades, or inspections. These decisions are generally based on fixed schedules, or informed by the judgment of experienced maintenance staff. To improve this process, predictive maintenance (PdM) has emerged as a viable solution to anticipate maintenance needs and preempt system failures. With data-driven PdM, maintenance needs are identified through machine learning (ML) solutions that monitor the system logs and recommend interventions before a failure occurs. This paper presents preliminary findings from a case study concerning the development of a ML system for PdM in railways. We present the current maintenance process, the existing logging platform, and our strategy for leveraging log data to support PdM. Our preliminary results are promising. However, they show that, although the log dataset spans three years and three railway vehicles, in some cases the log data alone are insufficient for accurately inferring maintenance requirements. To address the problem, we discuss the necessity of employing synthetic data generation methods and rule-based, knowledge-driven strategies.
A data-driven strategy to estimate the optimal feedback and the value function in an infinite-horizon, continuous-time, linear-quadratic optimal control problem for an unknown system is proposed. The method permits th...
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A data-driven strategy to estimate the optimal feedback and the value function in an infinite-horizon, continuous-time, linear-quadratic optimal control problem for an unknown system is proposed. The method permits the construction of the optimal policy without any knowledge of the model, without requiring that the time derivatives of the state are available for the design, and without even assuming that an initial stabilizing feedback policy is available. Two alternative architectures are discussed: the first scheme revolves around the periodic computation of some matrix inversions involving the Q-function, whereas the second approach relies on a purely continuous-time implementation of some dynamic systems whose trajectories are uniformly attracted by the solutions to the above algebraic equations. Interestingly, the proposed strategy essentially constitutes a (direct) data-driven implementation of the celebrated Kleinman algorithm, hence subsuming the particularly appealing features of the latter, such as quadratic monotone convergence to the optimal solution. The theory is then validated by the means of practically motivated applications.
Transportation operations especially in railroad domain are time critical. Scheduling conflicts driven by disruptions and delays in any one zone significantly affect the overall network operations. In this work applic...
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