Unmanned Surface Vehicles (USVs) are critical in maritime applications, requiring robust motion planning in dynamic and uncertain environments. Traditional algorithms like Velocity Obstacles (VO) and Dynamic Window Ap...
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In the realm of distributed energy resources, Doubly-Fed Induction Generator (DFIG)-based Wind Energy systems are pivotal technologies. However, integrating them into the grid poses challenges like ensuring Low Voltag...
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This paper presents a hybrid safe learning-based path planning algorithm that integrates classical graph-based planning with deep reinforcement learning techniques. The algorithm dynamically adjusts its strategy based...
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This article proposes a decision support system in the competitive agricultural production context that also promotes environmental sustainability. It is formulated as a hierarchic double-layer control problem that co...
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This paper explores the application of the Chopped Random Basis (CRAB) algorithm, integrated within the Quantum Toolbox in Python (QuTiP), to control and manipulate entanglement dynamics in a two-qubit quantum system....
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The control of nonlinear systems subjected to parametric uncertainty and achieving stabilizing behavior in finite-time is a challenging task. In this paper, the problem of finite-time stabilization of a general class ...
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This research focuses on implementing Machine Learning Impedance Control for robot arms to ensure both effective and safe interaction within unknown environments. The designed Machine Learning system comprises two key...
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This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings ...
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The issue of non-regular mixed constraints in optimal control problems is addressed. The necessary optimality conditions are derived in the case of a weak minimum. An example of a control problem with non-regular mixe...
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Unmanned Surface Vehicles (USVs) are critical in maritime applications, requiring robust motion planning in dynamic and uncertain environments. Traditional algorithms like Velocity Obstacles (VO) and Dynamic Window Ap...
详细信息
ISBN:
(数字)9798331538606
ISBN:
(纸本)9798331538613
Unmanned Surface Vehicles (USVs) are critical in maritime applications, requiring robust motion planning in dynamic and uncertain environments. Traditional algorithms like Velocity Obstacles (VO) and Dynamic Window Approach (DWA) face limitations in adaptability and efficiency, especially in highly dynamic scenarios. This paper presents a novel framework combining behavior grid maps and Proximal Policy Optimization (PPO) to address these challenges. Key contributions are the framework combining behavior grid maps and PPO for USV motion planning in dynamic environments, ensuring adherence to COLREGs, and the validation of the proposed approach through extensive simulations.
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