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2021 China automation Congress, CAC 2021

作者： Liu, Bo-Chao Yu, Qian Gao, Jing-Wen Zhao, Shuang Liu, Xiong-Chang Lu, Yan-Feng Beijing Information Science and Technology University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426473

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot. © 2021 IEEE

关键词： Neural networks

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Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1045-1063页

作者： Chaoyue Zu Chao Yang Jian Wang Wenbin Gao Dongpu Cao Fei-Yue Wang the College of Computer Science and Technology Jilin UniversityChangchun 130012China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou 221004China the College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012 Chongqing Xibu Automobile Proving Ground Management Co.Ltd. Chongqing 404100China the State Grid JIBEI Electric Power Company Limited Management Training Center China the Advanced Vehicle Engineering Center Cranfield UniversityCranfield MK430ALUK the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.

关键词： Collision avoidance intelligent vehicles intervehicle communication simulation testing trajectory planning

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Zero-Shot Object Goal Visual Navigation

arXiv

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arXiv 2022年

作者： Zhao, Qianfan Zhang, Lu He, Bin Qiao, Hong Liu, Zhiyong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Nanjing Artificial Intelligence Research of IA Jiangning District Jiangsu Nanjing211100 China The College of Electronic and Information Engineering Tongji University China

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes predefined in the training stage. However, in real households, there may exist numerous target classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we study the zero-shot object goal visual navigation task, which aims at guiding robots to find targets belonging to novel classes without any training samples. To this end, we also propose a novel zero-shot object navigation framework called semantic similarity network (SSNet). Our framework use the detection results and the cosine similarity between semantic word embeddings as input. Such type of input data has a weak correlation with classes and thus our framework has the ability to generalize the policy to novel classes. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in the zero-shot object navigation task, which proves the generalization ability of our model. Our code is available at: https://***/pioneer-innovation/Zero-Shot-Object-Navigation. © 2022, CC BY.

关键词： Robots

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Feature Selection and Learning-Based Model for Uav Path Planning on Rough Terrains

SSRN

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SSRN 2022年

作者： Ali, Hub Xiong, Gang Husnain, Haider Muhammad Tamir, Tariku Sinshaw Dong, Xisong Shen, Zhen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Qingdao Academy of Intelligent Industries Qingdao266109 China Chengdu610054 China

Path planning and obstacle avoidance in 3D terrain have been identified as a monumental challenge for a UAV in a variety of autonomous missions, such as disaster management and search and rescue operations. In large terrain areas, it’s a key problem for traditional approaches to search within the point-cloud maps to find a global path for a UAV considering the flight safety, maneuverability, weather constraints, and fuel cost. Hence, this paper proposes a trajectory planning technique for global and local path planning of a fixed-wing UAV above 3D terrain under static and dynamic constraints. For global path generation, a novel Feature Selection and Learning-based Model (FSLM) has been introduced to select the features of a point-cloud map and transform them into the feature set. The feature set is utilized by an improved A* planner with an extensive search area to deliver an optimal global path. The global path is assumed as the UAV's reference waypoints. The motion of the UAV on reference waypoints is simplified with two coordinates (R, d), where R is the cumulative distance covered by the UAV along the reference waypoints and d is its offset distance from the reference line segment in time t. For any local collision, offset trajectories are generated along with reference waypoints to avoid collision. Cost functions have been added so that the best global and local path can be chosen, taking into account altitude, weather, and fuel constraints. The simulation results show the performance of this approach in various complex terrains by considering weather and dynamic constraints, and the results are also compared to other 3D UAV path planning techniques. © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Circulating Load Ratio Prediction Model Based on Clustering Sag Milling Operating Conditions

SSRN

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SSRN 2024年

作者： Liao, Zhenhong Xu, Ce Chen, Wen Wang, Feng She, Jinhua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Changsha410012 China School of Engineering Tokyo University of Technology Tokyo192-0982 Japan

Grinding in mineral processing is used to control the ore at the technically feasible and economically optimum particle size to achieve mineral liberation for separation. A circulating-load ratio (CLR) during a semi-autogenous grinding (SAG) milling process is critical for controlling particle size and energy consumption. This paper presents a CLR-prediction model based on clustering SAG milling operating conditions. First, operating parameters affecting the CLR are identified by comprehensively analyzing the complex mechanism and characteristics of a typical industrial SAG milling ***, a method is developed to cluster operating conditions of the SAG milling process based on the power consumption and CLR of the process. The method reveals the actual physical significance of each operating condition. Then, the support vector regression (SVR) is used to model the CLR in each operating condition. After that, a distance-based model integration strategy is designed to determine the weights of each SVR model to predict the CLR. Finally, integrating the SVR submodels yields a CLR prediction model. Actual run data demonstrated the accuracy and effectiveness of the model in predicting CLR. This method has significant practical value for improving SAG milling efficiency and is applicable to other complex industrial processes. © 2024, The Authors. All rights reserved.

关键词： Particle size

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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A review of cooperative path planning of an unmanned aerial vehicle group

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1671-1694页

作者： Hao ZHANG Bin XIN Li-hua DOU Jie CHEN Kaoru HIROTA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China Department of Computational Intelligence and Systems Science Tokyo Institute of TechnologyTokyo 1528550Japan

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.

关键词： Unmanned aerial vehicle group Cooperation Path planning Optimization problem

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Deep Convolutional Correlation Filter Learning Toward Robust Visual Object Tracking

Deep Convolutional Correlation Filter Learning Toward Robust...

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Chinese control and Decision Conference, CCDC

作者： Tayssir Bouraffa Zihang Feng Yuxuan Wang Liping Yan Yuanqing Xia Bo Xiao School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing P. R. China

ISBN: (纸本)9781665478977

Recently, convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding background. Most of the visual object trackers extract deep features from a specific layer, generally from the last convolutional layer. However, these trackers are less effective, especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations, such as occlusion, illumination change, background clutter and so on. In this research paper, a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video sequence. Hierarchical features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and robustness. As the deep convolutional layers capture important semantic information, they are more robust to the target appearance variations. As for the shallow convolutional layers, they encode significant spatial details, which are more accurate to precisely localize the desired target. Moreover, Peak–Strength Context–Aware correlation filters are embedded to each convolutional layer output that produce multi–level convolutional response maps to collaboratively identify the estimated position of the target in a coarse–to–fine manner. Quantitative and qualitative experiments are performed on the widely used benchmark, the OTB–2015 dataset that shows impressive results compared to the state–of–the–art trackers.

关键词： Visualization Target tracking Correlation Semantics Video sequences Feature extraction Information filters

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A Deep Reinforcement Learning Method with Action Switching for Autonomous Navigation

A Deep Reinforcement Learning Method with Action Switching f...

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第40届中国控制会议

作者： Zuowei Wang Xiaozhong Liao Fengdi Zhang Min Xu Yanmin Liu Xiangdong Liu Xi Zhang Rui wei Dong Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control Capital Aerospace Machinery Co. Ltd.

Stochastic policy-based deep reinforcement learning（DRL） has successfully gained the widespread application but demands plenty of stochastic exploration to learn the environment at the initial training *** the agent is exposed to more complex environment,not only is the methodology inefficient,but its performance may also suffer from the issue of high *** paper develops a framework to accelerate the training procedure and reduce the variance by introducing a stochastic switching network,which specifically allows the agent to choose between heuristic actions and actions output by proximal policy optimization（PPO） *** of starting from the random actions,the agent can be effectively guided by the heuristic actions so that the navigation capability of the agent can be rapidly *** vanilla policy gradient（VPG） algorithm is further utilized to train the switching network,which can be jointly trained with the baseline *** the experimental comparison with the baseline PPO in the customized maze environment with openAI Gym toolkit,our method greatly contributes to the more efficient execution of navigation task by means of the heuristic actions for guidance.

关键词： Robot navigation proximal policy optimization（PPO） deep reinforcement learning（DRL） Vanilla policy gradient（VPG） action switching

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