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第40届中国控制会议

作者： Lei Xu Jiexi Liu Bo Chen Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

Temporal Action Detection is a research hotspot in Video *** is necessary not only to give the specific moments of the beginning and end of each action instance in the video,but also to give the category of the action *** present,most of the methods in temporal action detection are divided into two steps:The video is divided into a series of video clips to determine whether each clip is an action instance,and the fragments that are not an action instance are deleted;Segments that may be action instances are then classified to get the final *** present,the difficulties in action detection research mainly include the following two points:1) The boundary is not *** from action recognition,action detection requires precise positioning,but an action in life is often not very *** is also the reason why the mean average precision(mAP) of action detection is low at present.2) The time span is *** life,an action often spans a very long *** movements such as waving can last for a few seconds,while long movements such as rock climbing or cycling can last for tens of minutes,which makes it extremely difficult for us to extract *** view of the above difficulties,this paper proposes a large receptive field boundary matching network(LRFBMN) model which takes advantage of the relationship between proposals to improve the accuracy of proposal *** model is mainly divided into two parts:1) Clipping feature map is processed by large kernel convolution,and then proposal feature is generated by ROI-pooling;2) The proposals are arranged in a certain order to form a fixed graph,and the information exchange between graph nodes is realized by using convolution with *** experiments,this model is 2.35% higher than baseline and 1.06% higher than state-of-the-art in THUMOS14 data set.

关键词： Temporal Action Detection Video Understanding Proposal Large Receptive Field

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ADFNet: A two-branch robotic grasping network based on attention mechanism

ADFNet: A two-branch robotic grasping network based on atten...

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第40届中国控制会议

作者： Pengwei Xie Nan Hao Bo Chen Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

One of the challenges of RGB-D-based robotic grasping technology is how to make full use of these two complementary heterogeneous data sources while ensuring real-time performance. The existing robotic grasping methods mainly extract information from single-mode images or use a one-branch network to process RGB and depth images. These methods are insufficient in fully fusing the effective information of the two modes, which limits the anti-jamming performance. In this work,we propose Attention Dense Fusion Network（ADFNet）, a novel RGB-D based robotic grasping system that directly predicts the optimal grasping pose from RGB-D images. Our system uses a two-branch network with a heterogeneous framework that processes RGB and depth image information separately and retain the original structure of each data source. After applying dense fusion networks at different scales, the high-dimensional features in the RGB and depth branches are embedded and fused at the pixel level. Besides, we incorporate attention mechanisms to effectively suppress the independent background regions of the feature map and enhance the significant features, which improves the prediction accuracy. We conduct qualitative and quantitative experiments on the standard Cornell Grasp Dataset. The experimental results show that ADFNet can effectively improve the prediction accuracy to 98.9 %, which is better than existing methods while ensuing real-time performance.

关键词： robotic grasping RGB-D attention mechanism

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An attention-based RGBD dual-branch gesture recognition network

An attention-based RGBD dual-branch gesture recognition netw...

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第40届中国控制会议

作者： Bo Chen Pengwei Xie Nan Hao Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

In this work,we use a hierarchical architecture based on detector-classifier for gesture recognition *** the operation of the architecture,the detector,which is essentially the switch of the classifier,is always *** the output of the detector is true,then the classifier is activated and returns a classification label for the input video *** work focuses on the improvement of detectors and *** the detector,we introduce an attention mechanism to guide the network to focus on the space and channel where the gesture is *** the classifier,based on the RGB information stream,we use an independent branch to extract the features of the depth stream,and finally merge the two *** gestures move in a three-dimensional space,depth information can make up for the lack of RGB *** show that on the Egogesture test set,our detector achieves 98.86% accuracy on RGB input,while the classifier achieves 93.85% *** the same time,our gesture recognition architecture can fully meet the real-time requirements.

关键词： Gesture Recognition RGBD feature fusion attention mechanism real-time

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Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Aerial battlefield targets grouping based on DTW-DBSCAN algorithm

Aerial battlefield targets grouping based on DTW-DBSCAN algo...

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第40届中国控制会议

作者： Shao Zhuang Chen Chen School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

Targets grouping is the basis of second-level information fusion,which can effectively assist commanders to make *** aerial targets grouping algorithm only takes the radar acquisition data at the current time as the object and cannot update the clustering results automatically.A grouping method combining dynamic time warping(DTW) and the algorithm Density-Based Spatial Clustering of Applications with Noise(DBSCAN) is *** DTW distance of each attribute historical time-series data is used to measure the similarity between ***,an improved DBSCAN algorithm is used for *** simulation results show that the method has better grouping effect and can automatically cluster regularly.

关键词： targets grouping dynamic time warping density clustering

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Event-triggered Consensus control of Heterogeneous Multi-agent systems: Model- and Data-based Analysis

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

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Science China(Information Sciences) 2021年第7期64卷 132-147页

作者： Lele XI Zhihong PENG Lei JIAO Ben M.CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System Peng Cheng Laboratory Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view of the onboard sensor in cluttered environments. Compared to existing methods, safe flying zone,vehicle physical limits, and smoothness are fully considered to guarantee flight safety, kinodynamic feasibility, and tracking performance. To tackle the cluttered environments, a parallel particle swarm optimization algorithm is applied to find the feasible waypoints that the generated trajectory should be as close to as possible, with consideration of the target's future state as well as obstacles to trade off the tracking performance and flight safety. Then, a sequential motion planning method, considering the above constraints, is applied and embedded into a cost function for solving the problem of robust tracking trajectory generation for the quadrotor via a convex optimization approach. The feasibility and effectiveness of the proposed method are verified by numerical simulations.

关键词： quadrotor target tracking trajectory generation B-spline convex optimization

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Torque Compensation Model and Touchdown Detection Model of the Hydraulic Quadruped Robot controlled by VMC

Torque Compensation Model and Touchdown Detection Model of t...

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Chinese control and decision Conference, CCDC

作者： Ranjiang Wang Jiangbo Zhao Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665478977

In this paper, according to the different dynamic models of the support phase and the swing phase, the joint torque compensation of the support phase and the swing phase are given to compensate the joint torque of the hydraulic quadruped robot controlled by the virtual model control(VMC). This compensation can increase the tracking effect of the foot end on the swing trajectory, reduce the elasticity and damping coefficient of the virtual model method, and increase the flexibility of the leg. Then this paper establishes the touchdown detection function based on the joint torque obtained by the Lagrange dynamics equation and the joint torque obtained by the force sensor to determine the touch state of the foot of the quadruped robot to switch the phase of the leg. At the end of this paper, simulation and experiment prove that the compensation model and touchdown detection function model have feasibility and correctness.

关键词： Legged locomotion Damping Torque Switches Hydraulic systems Elasticity Mathematical models

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Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound for Multi-Target Tracking in Netted Colocated MIMO Radar systems

Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound ...

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第40届中国控制会议

作者： Sijian Liao Zhihong Peng Junqi Cai School of Automation Beijing Institute of Technology China State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, an effective joint beam selection and power allocation（JBSPA） method is proposed to solve the online resource scheduling problem for multi-target tracking in distributed MIMO radar system. Overall target tracking performance can be improved by optimizing radar system beam selection and power allocation, since each MIMO radar can generate a timevarying beam, and adjust the quality of the observed signal by controlling the transmit power of each beam. In our method the feedback information of the target tracking period is used to complete the optimal radar-targets assignment and power allocation,the objective function is formulated based on the Bayesian Cramér-Rao lower bound, as the formulated JBSPA problem is nonconvex, a three steps solution method based on reward matrix is proposed to solve it effectively. Numerical results show that the proposed JBSPA method can deliver better performance than the competitors in overall MTT performance.

关键词： Resource Scheduling BCRLB Target Tracking Power Allocation MIMO Radar systems

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Efficient Multi-Robot Coverage of an Unknown Environment

Efficient Multi-Robot Coverage of an Unknown Environment

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第40届中国控制会议

作者： Zihao Chen Zhihong Peng Lei Jiao Yuanyuan Gui School of Automation Beijing Institute of Technology China State Key Laboratory of Intelligent Control and Decision of Complex System

This paper discusses the complete area coverage of multi-robots in unknown environments. Finding the optimal solution for the problem of area coverage with multiple robots has been proven to be NP-hard. This paper proposes the Multiagent Node Backtracking Area Coverage（MANBAC） algorithm, which uses improved BCD local area coverage method, and the node backtracking method that is based on the A* algorithm to achieve complete coverage of the entire unknown *** with the area coverage of a single robot, use multi robots can greatly improve the efficiency of area *** with the existing coverage algorithms in known area, this study can be applied to a wider range of area scenarios which are not known.

关键词： Area coverage Unknow environment Multirobot

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