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第43届中国控制会议

作者： Hao-Xiang Chen Xi-Wen Zhang Jun-Nan Shen State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning(GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages.

关键词： Multi-Agent System Hierarchical Graph Convolutional Network Heterogenous Multi-Agent Collaboration

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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2023 China automation Congress, CAC 2023

作者： Chen, Cheng Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% reduction in parameter count and a 35% reduction in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56. © 2023 IEEE.

关键词： Object detection

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Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Gao, Yuan Guo, Yu Zhu, Fenghua Wang, Fei-Yue The School of Navigation The State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer the poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which has more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with the state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://***/QuJX/DDNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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A bi-level optimization approach for joint rack sequencing and storage assignment in robotic mobile fulfillment systems

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Science China(Information Sciences) 2023年第11期66卷 127-149页

作者： Xiang SHI Fang DENG Sai LU Yunfeng FAN Lin MA Jie CHEN Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Zhejiang Cainiao Supply Chain Management Co. Ltd. Shanghai Research Institute for Intelligent Autonomous Systems

This paper studies a novel rack scheduling problem with multiple types of multiple storage locations(RS-MTMS), which can decide the retrieval sequence of racks and assign each rack a storage location after visiting a picking station. A major challenge in RS-MTMS is that the storage assignment problem and the retrieval sequence decision are closely coupled. If the RS-MTMS is solved directly, the storage assignment scheme and the retrieval sequence of racks are generally generated separately, thus resulting in poor performance. To overcome this difficulty, we propose a bi-level optimization approach for jointly optimizing the storage assignment and retrieval sequence(Bi JSR). In Bi JSR, the storage assignment problem is solved by variable neighborhood search(VNS) in the upper-level optimization. Effective candidate modes are incorporated into VNS to improve solution quality and computational efficiency. The sequencing optimization is obtained in the lower-level according to the given storage location set. A transformation strategy with sufficient problem-specific knowledge is developed to identify the lower-level optimization as the traveling salesman problem and its variants. Then these identified problems are solved using the loop-based *** results show that the proposed Bi JSR is more effective and efficient than the representative algorithms in solving the RS-MTMS problem.

关键词： rack scheduling sequence decision storage assignment bi-level optimization robotic mobile fulfillment system

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AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

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International Conference on Intelligent Transportation

作者： Fei Lin Yonglin Tian Yunzhe Wang Tengchao Zhang Xinyuan Zhang Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Capital University of Economics and Business Beijing China Department of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks.

关键词： Three-dimensional displays Large language models Urban areas Land transportation Autonomous aerial vehicles Cognition Space exploration Planning

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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Chinese control Conference (CCC)

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Deforestation is the primary source of global warming; traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

Compound Fault Separation and Diagnosis Method Based on FSA-...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781665401159

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method. © 2021 IEEE.

关键词： Failure analysis

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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