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Neuromorphic Computing (ICNC), International Conference on

作者： Hong Chen Dingxin He Huantao Mao Yong Yang Zhi-Wei Liu School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

This paper investigates the area coverage problem for multiple Unmanned Surface Vehicles (USVs) in dynamic environments. Multiple USVs are employed to execute the coverage task using the Voronoi Partition method for task allocation. Parameter adaptive laws are employed to estimate the parameters of the sensing function and the Voronoi cell centroids for updating the target positions in dynamic environments. Additionally, considering the influence of water surface disturbances, a nonholonomic underactuated model analysis is conducted for the USV under non-full constraints, resulting in a motion strategy for USVs. This enables the multiple USVs to effectively cover the task area. Finally, simulation results are presented to validate the effectiveness of the coverage scheme designed in this paper and the ability of USVs to effectively cover dynamic environments.

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Thermal Safety Analysis and Dynamic control Strategy for Mode Switching Process of Reversible Solid Oxide Cell System

Thermal Safety Analysis and Dynamic Control Strategy for Mod...

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Chinese control Conference (CCC)

作者： Wei Zhao Guoqiang Liu Zexin Li Xi Li Key Laboratory of Image Processing and Intelligent Control of Education Ministry School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China

Reversible solid oxide cells (rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both functions. When used in dynamic operation with intermittent electrical power sources, rSOC system switches from the fuel cell (SOFC) to electrolyzer (SOEC) and vice versa depending on load and grid peculiarities. This can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the system. In the present work, a system-level dynamic model of rSOC is established and validated against experimental data. Subsequently, detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed, and a temperature management controller based on Model Predict control method (MPC) is proposed. Gas flow and temperature at the stack inlet are controlled by air flow and bypass valves, avoiding problems associated with temperature overshoot during transient operation. The simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety, which indicates the competitiveness of the controller.

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When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

arXiv

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arXiv 2023年

作者： Liu, Tianqi Ye, Xinyi Zhao, Weiyue Pan, Zhiyu Shi, Min Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain non-local feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-to-line search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://***/TQTQliu/ET-MVSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Relative-Posture-Fixed Model Predictive Human-Following control with Visibility Constraints in Obstacle Environments 8

Relative-Posture-Fixed Model Predictive Human-Following Cont...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Yikun Huang, Jian Yu, Jinqi Zhu, Yaonan Hasegawa, Yasuhisa School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China Nagoya University Department of Micronano Mechanical Science and Engineering Nagoya464-8603 Japan

ISBN: (纸本)9798350300178

The relative-posture-fixed human following is critical for the human-robot interaction and cooperation in daily scenes such as domestic service and healthcare. However, few of previous researches have discussed the difficulties faced by mobile robots in the real world, including the limitation of sensing range and hindrance from obstacles. For practicality, in this paper we investigate the relative-posture-fixed following control with collision avoidance and visibility constraints. The control task is formulated as a receding-horizon optimization problem and solved under the Nonlinear Model Predictive control (NMPC) framework, while an Extended Kalman Filter (EKF) based human trajectory predict algorithm is integrated into the robot controller. The effectiveness of the proposed algorithm is verified in a simulation, and the results demonstrate a promising performance of our controller for the human-following task in complicated environments. © 2023 IEEE.

关键词： Human robot interaction

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Koopman Operator Learning control of the Piezoelectric Actuator

Koopman Operator Learning Control of the Piezoelectric Actua...

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第42届中国控制会议

作者： Xiang Huang Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control(Koopman-MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman-MPC scheme.

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USV Path Planning Based on ACO-SA Algorithm

USV Path Planning Based on ACO-SA Algorithm

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第42届中国控制会议

作者： Chuanshang Luo Yifeng Tang Zhihui Huang Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization ***, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running;change the heuristic function and state transition probability taking three factors into consideration;change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same ***, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm.

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Fog images Generation about Unmanned Surface Vessels with Improved Generative Adversarial Network

Fog Images Generation about Unmanned Surface Vessels with Im...

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第42届中国控制会议

作者： Yifeng Tang Zhihui Huang Chuancong Tang Chuanshang Luo Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper proposes a fog weather data augmentation method for the unmanned surface vessels(USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images.

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WDNet: A Water Depth Perception Network with Global Information

WDNet: A Water Depth Perception Network with Global Informat...

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第42届中国控制会议

作者： Zhihui Huang Yifeng Tang Chuancong Tang Yifan Xu ChuanShang Luo Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures(sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network(WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image,and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model.

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Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity

arXiv

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arXiv 2024年

作者： Zhang, Huaxin Xu, Xiaohao Wang, Xiang Zuo, Jialong Huang, Xiaonan Gao, Changxin Zhang, Shanjun Yu, Li Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China University of Michigan Ann Arbor United States Kanagawa University Japan

How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-level anomaly prediction, often missing the interpretability of complex and diverse real-world anomalies. Recent multimodal approaches leverage visual and textual data but lack hierarchical annotations that capture both short-term and long-term anomalies. To address this challenge, we introduce HIVAU-70k, a large-scale benchmark for hierarchical video anomaly understanding across any granularity. We develop a semi-automated annotation engine that efficiently scales high-quality annotations by combining manual video segmentation with recursive free-text annotation using large language models (LLMs). This results in over 70,000 multi-granular annotations organized at clip-level, event-level, and video-level segments. For efficient anomaly detection in long videos, we propose the Anomaly-focused Temporal Sampler (ATS). ATS integrates an anomaly scorer with a density-aware sampler to adaptively select frames based on anomaly scores, ensuring that the multimodal LLM concentrates on anomaly-rich regions, which significantly enhances both efficiency and accuracy. Extensive experiments demonstrate that our hierarchical instruction data markedly improves anomaly comprehension. The integrated ATS and visual-language model outperform traditional methods in processing long videos. Our benchmark and model are publicly available at https: //***/pipixin321/HolmesVAU. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Dynamic Graph Dismantling with Reinforcement Supervised Learning

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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第42届中国控制会议

作者： Yumei Wang Chuancong Tang Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systemsthe Key Laboratory of Image Processing and Intelligent Controland the State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

There are always some "key" nodes in a big complex network,which can joint the most connected *** to identify these nodes,finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network,has become a research ***,a method for determining the"key" nodes based on reinforcement learning framework and supervised learning model is *** algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset,but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong *** order to further improve the algorithm's performance,-greedy mechanism is used to explore more network *** experiment results show that given the same fraction of removed nodes,our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization.

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