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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Xiaofei, Dong Zhang, Ren School of Automation Science and Electrical Engineering Beihang University Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9780738146577

This paper investigates the cooperative guidance issue for multiple unmanned aerial vehicles (UAVs) for simultaneous attacks on maneuvering targets with a prescribed angle formation. Each UAV is accessible to the relative information with respect to the target while the absolute information of the target is unavailable. Also, each UAV just interacts with its neighbors locally. By introducing the integral sliding mode technique, a finite-time observer for the exact estimation of the absolute information of the target is designed. With the help of such an observer, a distributed cooperative guidance algorithm is developed such that both the time-to-go estimate consensus and angle formation objectives are achieved in finite time given that the undirected graph is connected. Finally, a simulation example is presented to validate the efficiency of the suggested guidance algorithm. © 2021 IEEE.

关键词： Antennas

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Solving Ascent Trajectory Optimization Problems by Radua Pseudospectral Method

Solving Ascent Trajectory Optimization Problems by Radua Pse...

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intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Liaochao Deng Cheng Xu Weishuai You Beijing Institute of Mechanical and Electrical Engineering Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Ascent trajectory optimization problem of air-breathing hypersonic vehicles is a highly nonlinear and nonconvex problems. Most of the early works focus on the traditional indirect method, which needs to derive the complete first-order necessary conditions of the trajectory optimization problem. The derivation process is too complicated and error-prone. Additionally, indirect method has a high demand on the initial guess, and it needs to give the initial guess of covariant variables without physical significance. In this paper, we solve the ascent trajectory optimization problem directly using Radau Pseudospectral Method (RPM). Firstly, the complex three-dimensional ascent trajectory optimization problem is established in detail. Conmmon inequality path constraints including those on dynamic pressure and aerodynamic bending moment are taken into account. The performance index is given as maximizing the final mass considering minimizing the fuel consumption. Subsequently, the ascent trajectory optimization problem is transformed into a nonlinear programming problem (NLP) by RPM. Finally, the ascent trajectory optimization for Generic Hypersonic Aerodynamic Model Example (GHAME) is solved by RPM and the optimal results demonstrate the rapidity, effectiveness and high precision of RPM. The comparison between optimal trajectories with and without path constraints shows that path constraints increase fuel consumption.

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Gated Driver Attention Predictor

Gated Driver Attention Predictor

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International Conference on intelligent Transportation

作者： Tianci Zhao Xue Bai Jianwu Fang Jianru Xue College of Transportation Engineering Chang'an University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Some recent works explore driver attention prediction in critical or accident scenarios and find a positive role in helping accident prediction, while the promotion ability is constrained by the prediction accuracy of driver attention maps. In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP). Gate-DAP aims to learn the importance of different spatial, temporal, and modality information in driving scenarios with various road types, occasions, and light and weather conditions. The network connection gating in Gate-DAP consists of a spatial encoding network gating, long-short-term memory network gating, and information type gating modules. Each connection gating operation is plug-and-play and can be flexibly assembled, which makes the architecture of Gate-DAP transparent for evaluating different spatial, temporal, and information types for driver attention prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority of the proposed method with the comparison with state-of-the-art approaches.

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Identity Re recognition technology Based on Improved Lightweight ConvNetx Model

Identity Re recognition Technology Based on Improved Lightwe...

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Chinese control Conference (CCC)

作者： Peng Siting Li Lei Hongbo Zhu Ren Jun Song Zhichao Jiabing Yang The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions.

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Localization of Pallets in Narrow Aisles for Unmanned Forklifts 3

Localization of Pallets in Narrow Aisles for Unmanned Forkli...

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3rd International Symposium on Computer technology and Information science, ISCTIS 2023

作者： Sun, Peng Ding, Wei Lian, Mingchang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9798350325386

As the application of unmanned forklifts becomes more and more widespread, logistics scenarios are constantly evolving. This paper focuses on a scenario where the pick-up of pallets perpendicular to forklift in narrow aisles is required. The narrow aisles only allow the forklift to complete the turning, performing pallet localization in front of it will be difficult. We proposes a method for localizing pallets in narrow aisles for unmanned forklifts. By installing the camera at an oblique angle above the pallet, the collected images are uploaded to the image processing system. The YOLOv5 is used to detect the pallet and extract the bounding boxes of the pallet class. Based on the bounding box coordinates, the original image is cropped. Convert the cropped image to grayscale for Canny edge detection, and then perform Hough line detection to extract the right edge of the pallet. This right edge will be compared with the reference pallet edge to obtain the relative localization between the pallet and the unmanned forklift. © 2023 IEEE.

关键词： Feature extraction

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Research on Simulation technology of Cooperative Guidance for Aircraft Formation

Research on Simulation Technology of Cooperative Guidance fo...

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Chinese intelligent systems Conference, CISC 2020

作者： Tong, Jiahui Liu, Jin Li, Jing Zhan, Tianming Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China TAIJI Computer Corporation Limited Beijing China

ISBN: (纸本)9789811584572

This paper analyzed the structure and characteristics of cooperative guidance system of aircraft formation, puts forward a multi-level simulation system architecture of centralized/distributed driving to solve the cooperative guidance for aircraft formation, realized the modeling of aircraft formation members and interaction by using multi-agent method, discussed the simulation methods of data link networking transmission and positioning, space-time registration of simulation system, and so on. The simulation is carried out based on a certain combat scenario, and the results show that the simulation system constructed in this paper can simulate the whole process of cooperative guidance of aircraft formation. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer architecture

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Gated Driver Attention Predictor

arXiv

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arXiv 2023年

作者： Zhao, Tianci Bai, Xue Fang, Jianwu Xue, Jianru College of Transportation Engineering Chang’an University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China College of Artificial Intelligence Xi’an Jiaotong University Xi’an China

关键词： Forecasting

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Prediction of Hybrid Asynchronous Frequency Hopping Pattern based on Deep Learning in Wideband Spectrum

Prediction of Hybrid Asynchronous Frequency Hopping Pattern ...

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Chinese Automation Congress (CAC)

作者： Ya Wang Jiaqi Wang Shuai Yuan Naijin Liu Yuanming Lu Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Unit 63893 of PLA Luoyang China

In non-cooperative scenarios, wideband frequency hopping (FH) communication reconnaissance including FH signal detection, parameter estimation, and network sorting under single-channel reception is challenging. The FH pattern contains the most information about FH signals. So it is the core of FH parameter estimation. Based on the task analysis, this paper proposes a blind prediction framework combining deep learning (DL) and clustering methods to improve the accuracy and efficiency of FH pattern estimation for mixed signals in the wideband spectrum. The unique advantage of this framework is that it requires no prior signal information and anchors but exploits the inherent time-frequency (TF) properties of asynchronous FH signals. It has a strong generalization ability and can adapt to signals of any shape. Moreover, a new comprehensive evaluation metric, normalized root mean square error including missed detection (NRMSE-MD), suitable for sequence data prediction is proposed to evaluate the level of missed detection and false detection in signal monitoring. Experimental results demonstrate the superiority of the proposed framework and the effectiveness of the proposed evaluation metric.

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Visual Explanation of Object Detectors via Saliency Maps

Visual Explanation of Object Detectors via Saliency Maps

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jin Xiao Wenrui Liu Xiaoguang Hu Hao Jiang Weipeng Wang Li Yahang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beihang University Beijing China The Third Academy of CASIC Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators.

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance 5

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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5th International Conference on Computer Engineering and intelligent control, ICCEIC 2024

作者： Chen, Maolin Chen, Songhang Lian, Mingchang School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9798331507992

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs. © 2024 IEEE.

关键词： Automatic guided vehicles

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