This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap *** proposed a space partitioning method based on sampling and consistency control to conduct a preli...
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This paper presents a human-like motion decision-making method for unmanned aerial vehicles(UAVs)navigating in trap *** proposed a space partitioning method based on sampling and consistency control to conduct a preliminary analysis of the indoor environment based on architectural *** method reduces the dimensionality of the path planning problem,thereby enhancing the ***,we designed a target-switching logic for the dynamic window *** improvement endows the UAV with the capability of both real-time obstacle avoidance and global navigation,enhancing the efficiency of the UAV in flying to task spots ***,by applying human-like methods of batch distance perception and obstacle perception to this scheme,we have further enhanced the robustness and efficiency of path ***,considering the scenario of high-rise fire rescue,we conducted simulation *** demonstrates that our scheme enhances the efficiency and robustness of path planning.
In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristo...
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Leaks in natural gas pipelines can cause very serious safety accidents, and timely detection and remedial action can greatly reduce the losses. In recent years, pipeline leak detection has received extensive studies. ...
Leaks in natural gas pipelines can cause very serious safety accidents, and timely detection and remedial action can greatly reduce the losses. In recent years, pipeline leak detection has received extensive studies. Most methods use pressure sensors or acoustic sensors to detect pipelines, but there are certain limitations on the usage scenarios and detection time *** this basis, this paper selects maglev vibration detector to detect the vibration signal of pipelines. The difficulty lies in that,sudden changes in vibration signals due to external disturbances, may lead to false alarms. Therefore, this paper proposes a pipeline leak detection method using Multivariate Gaussian Distribution based Kullback-Leibler Divergence(MGD-KLD) and on-delay timer to reduce false alarms during the detection process. In this paper, by constructing a simulated pipeline platform for leak experiments and applying the above method to process the experimental data, the false alarm rate of pipeline leak detection can be effectively reduced.
Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote ...
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Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote sensing object detection. How to apply horizontal detection in remote sensing images has important research significance. The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning. Circular smooth label(CSL)solved this problem well by transforming the regression of angle into a classification form. YOLOv5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small *** use YOLOv5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets,and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images. Our improved model achieves an average category accuracy of 39.2% on the FAIR1M dataset. Although our method does not achieve satisfactory results,this approach is very efficient and simple, reducing the hardware requirements of the model.
For hybrid energy storage systems in DC microgrids,a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components,then assign the...
For hybrid energy storage systems in DC microgrids,a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components,then assign them to batteries and supercapacitors to respond ***,aiming at the service life of the energy storage system,this paper considers the characteristics and key parameters of the hybrid energy storage structure and proposes an adaptive drooping comprehensive control strategy considering the SOC of the energy storage unit given the shortcomings of power distribution within the current hybrid energy *** to the self-regulation capacity of each energy storage unit,it is sorted and constrained,and protected by using SOC,which ensures the economy and safety of the system while ensuring power *** traditional droop control and adaptive droop control are simulated to verify the effectiveness of the proposed control strategy.
This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators (UMs) in a vertical plane. The proposed method solves the problem that the UMs cannot always enter ...
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作者:
Qiming LiuXinru CuiZhe LiuHesheng WangDepartment of Automation
Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence
AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation
Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China
Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-b...
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Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.
Currently, multi-agent reinforcement learning (MARL) has been applied to various domains such as communications, network management, power systems, and autonomous driving, showcasing broad application scenarios and si...
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Currently, multi-agent reinforcement learning (MARL) has been applied to various domains such as communications, network management, power systems, and autonomous driving, showcasing broad application scenarios and significant research potential. However, in complex decision-making environments, agents that rely solely on temporal value functions often struggle to capture and extract hidden features and dependencies within long sequences in multi-agent settings. Each agent's decisions are influenced by a sequence of prior states and actions, leading to complex spatiotemporal dependencies that are challenging to analyze directly in the time domain. Addressing these challenges requires a paradigm shift to analyze such dependencies from a novel perspective. To this end, we propose a Multi-Agent Reinforcement Learning system framework based on Fourier Topological Space from the foundational level. This method involves transforming each agent's value function into the frequency domain for analysis. Additionally, we design a lightweight weight calculation method based on historical topological relationships in the Fourier topological space. This addresses issues of instability and poor reproducibility in attention weights, along with various other interpretability challenges. The effectiveness of this method is validated through experiments in complex environments such as the StarCraft Multi-Agent Challenge (SMAC) and Google Football. Furthermore, in the Non-monotonic Matrix Game, our method successfully overcame the limitations of non-monotonicity, further proving its wide applicability and superiority. On the application level, the proposed algorithm is also applicable to various multi-agent system domains, such as robotics and factory robotic arm control. The algorithm can control each joint in a coordinated manner to accomplish tasks such as enabling a robot to stand upright or controlling the movements of robotic arms.
The current inertia testing method for inverters is complex and difficult to apply to grid-forming converter. This article proposes an inertia damping identification method for grid-forming converter based on the rate...
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PurposeIn pursuit of higher maneuverability and aerodynamic characteristics, aerodynamic configuration for an attitude control system of aircraft adopts generally an over-actuated form. The purpose of this paper is to...
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PurposeIn pursuit of higher maneuverability and aerodynamic characteristics, aerodynamic configuration for an attitude control system of aircraft adopts generally an over-actuated form. The purpose of this paper is to address allocation problems of multiple actuators to improve the system rapidity and performance and simultaneously solve parameter uncertainties of the aircraft attitude control ***/methodology/approachTaking into account the respective frequency characteristics of actuators as well as position and rate constraints, the proposed method extends regular quadratic-programming control allocation by using a dynamic weighting matrix. And a robust controller, issuing a virtual control command to be allocated, is designed based on H infinity mixed sensitivity *** an attitude control system with parametric uncertainties, the proposed virtual robust controller and control allocation method is verified and the system performance is ***/valueThe proposed dynamic control allocation method is designed based on actuator dynamic characteristics and the changing rate of the virtual control command, considering some actuator constraints simultaneously. The efficiency of the attitude control system is improved and the complex multi-input and multi-output system model can be simplified.
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