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Depth Estimation for Small Obstacles Based on Monocular Vision

Depth Estimation for Small Obstacles Based on Monocular Visi...

引用

IEEE International Conference on Unmanned systems (ICUS)

作者： Wang Gao Changqing Wang Lei Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.

关键词： intelligent agents Estimation control systems Training Deep learning Computer vision Complex systems

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A Multi-branch Parallel Simulation Method based on Symbiotic Potential

A Multi-branch Parallel Simulation Method based on Symbiotic...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhao, Lu Zhen-Biao, Tu Jia-Ning, Wei Yu-Tong, Zhu Tong-Lin, Liu Qing-Hua, Liu Beijing Electro-Mechanical Engineering Insititute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

A multi-branch parallel simulation method and an architecture of application system are proposed in this paper based on symbiotic potential, and method of construction and calibration of parallel battlefield based on real data, as well as generation of simulation branch based on situation interpretation etc. are proposed. Fast building and adjustment of situation of real time simulation are realized based on simulation to meet the requirements of consistency and parallel operation between virtual simulation and real systems. Multibranch operation management, multi-branch highperformance operation support and other methods are proposed to support the dynamic generation of massive parallel simulation copies based on real-time intelligence and ultra-realtime and large-sample operation to meet the requirements of advanced evaluation of real-time situation. At the same time, a set of simulation application verification frameworks for typical task situation is presented to support the construction of multibranch simulation operation environment, and provide technical verification means for real-time situation prediction and evaluation. © 2019 IEEE.

关键词： Real time systems

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Estimate depth information from monocular infrared images based on deep learning 7

Estimate depth information from monocular infrared images ba...

引用

7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Li, Xu Ding, Meng Wei, Donghui Wu, Xiaozhou Cao, Yunfeng Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China Beijing Electro-.Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China

ISBN: (纸本)9781728170862

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception, we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving. © 2020 IEEE.

关键词： Deep learning

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Aircraft control Method Based on Deep Reinforcement Learning

Aircraft Control Method Based on Deep Reinforcement Learning

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Data Driven control and Learning systems (DDCLS)

作者： Yan Zhen Mingrui Hao Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

To address the failure of precise overload tracking and anti-interference caused by the difficulty of accurate modeling of a complex aircraft, the controller designing method based on deep reinforcement learning is studied. This paper trained the control network based on the Proximal Policy Optimization (PPO), studied the tracking control problem of the aircraft, and accurately tracked the typical command signals. Fixed-point simulation of the aircraft is performed, with results showing that, in presence of aircraft model parameter variation and external disturbance, the controller based on deep reinforcement learning can achieve accurate tracking of overload commands.

关键词： Aircraft Reinforcement learning Aerospace control Atmospheric modeling Aerodynamics Mathematical model Heuristic algorithms

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UAV Decision-making system Based on the Rough Set theory and the Optimal Genetic Algorithm

UAV Decision-making System Based on the Rough Set theory and...

引用

IEEE International Conference on Unmanned systems (ICUS)

作者： Wendi Sun Mingrui Hao Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Air combat decision-making is a critical issue in Unmanned Air Vehicle automatic combat. Precise and efficient maneuvering strategies are extremely important for the final victory. Quantitative research has been conducted on the maneuver strategy. In this study, a knowledge-based maneuver action library for air combat was established by the Rough Set Theory, which can help quick response to the battlefield situation. Since not all influence factors of battlefield situation are that significant and the computing source is finite, the rough set model was simplified to increase reaction rate. This paper introduced an advanced genetic algorithm to reduce the attributes of rough sets, taking the purity of each attribute into account, so as to make the condition attribute as close to 1 or 0 as possible. As a result, the reducing accuracy of the whole decision-making system is improved. The simulation results show that the rough set model built in this paper is efficient, feasible and reasonable, and the algorithm can support the air combat maneuver strategy decision system.

关键词： intelligent agents Conferences Complex systems systems simulation Rough sets Power system dynamics intelligent control

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Failure mode and effects analysis based on intuitionistic fuzzy sets and evidential correlation coefficient

Failure mode and effects analysis based on intuitionistic fu...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Hang Huang, Chan Lu, Mingsheng Dong, Xiaofei School of Software Northwestern Polytechnical University Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Xi'an Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Agent Cooperation Laboratory Science and Technology on Complex System Control and Intelligent Beijing China

ISBN: (纸本)9781728137926

As an effective implement, failure mode and effects analysis (FMEA) is widely applied in the security of system for practical application. Nowadays, many methods determine the order of fault mode by a crisp risk priority number (RPN). However, these methods exist several shortcomings, for instance, the correlation of the assessments given by team members are not fully considered. In this article, a new method for risk assessment and sequence for failure modes in FMEA is proposed on account of the D-S evidence theory and the evidential correlation coefficient. By using the proposed approach, the weights of team members for each failure mode and risk factor is obtained. Then the weighted assessments are used to perform the aggregation process by Intuitionistic fuzzy weighted averaging (IFWA) operator. A classic application regarding risk assessment is used to verify the effectiveness of the proposed method. © 2019 IEEE.

关键词： Failure modes

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Channel Estimation Based on Lorentzian Adaptive Algorithm in α-stable Noise Environment

Channel Estimation Based on Lorentzian Adaptive Algorithm in...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Aleksey Cherednichenko Xinqi Huang Yanyan Wang Yingsong Li Harbin Engineering University Harbin China Intelligent Agent Cooperation Laboratory Science and Technology on Complex System Control Beijing China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel inα-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons.

关键词： Computer simulation Adaptive algorithms Channel estimation Adaptive arrays

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Design of Reusable Effectiveness Evaluation Model Framework

Design of Reusable Effectiveness Evaluation Model Framework

引用

Chinese control and Decision Conference, CCDC

作者： Shuang Zhang Wei Li Shenglin Lin Ming Yang Jiahui Tong Control and Simulation Center Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to make the effectiveness evaluation models of the weapon equipment system universal and flexible, and adapt to different evaluation objects, a framework of effectiveness evaluation model is designed. Next a series of modular and reusable effectiveness evaluation models are proposed under the framework. Each type of evaluation model is built based on IDEF0, and the corresponding input and output of each model are designed. To further improve reusability, several models are mathematically described and model interface is designed based on XML.

关键词： Indexes Data models Data mining Analytical models Mathematical model Optimization Adaptation models

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Motion Parallax Estimation for Ultra Low Altitude Obstacle Avoidance

Motion Parallax Estimation for Ultra Low Altitude Obstacle A...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Wang, Changqing Liang, Xinkai Zhang, Shan Shen, Chao Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

By making full use of the curvature of the earth and ground clutter, the ultra-low altitude flight technique can significantly increase the difficulty of detecting and intercepting unmanned aerial vehicles (UAVs), and improve their penetration probabilities. However, unpredicted static/dynamic obstacles seriously threaten the flight safety of the ultra-low altitude UAVs. Therefore, fine autonomous spatial perception capability is essential for ultra-low altitude UAVs with limited loads and computing resources to avoid obstacles. To solve the difficult problem of spatial perception for ultra-low altitude obstacle avoidance, a motion parallax estimation algorithm is proposed by combing the deep learning and the epipolar geometry in this paper. By utilizing the deep neural network, the end-to-end mapping relationship from sequence images to depth values of pixels is constructed for spatial perception. At the same time, to overcome the interference caused by the image consistency hypothesis, the loss function is constructed based on the 3D geometric information of the scene rather than the re-projection error between images. Finally, the proposed algorithm is evaluated on the KITTI dataset and infrared image dataset to verify its effectiveness and generalization. © 2019 IEEE.

关键词： Deep neural networks

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Observability Enhancement of the Target Tracking Model Based on Infrared Semi-strapdown Seeker

Observability Enhancement of the Target Tracking Model Based...

引用

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Siting, Peng Qian, Zhao Yichao, Ma Cheng, Xu Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137926

As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method. © 2019 IEEE.

关键词： Observability

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