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arXiv 2021年

作者： Yin, Huan Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps. To bridge the gap of sensing modalities, deep neural networks are constructed to create shared embedding space for radar scans and lidar maps. Herein learned feature embeddings are supportive for similarity measurement, thus improving map retrieval and data matching respectively. In RobotCar and MulRan datasets, we demonstrate the effectiveness of the proposed framework with the comparison to Scan Context and RaLL. In addition, the proposed pose tracking pipeline is with less neural networks compared to the original RaLL. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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A novel hierarchical charging algorithm for the AGV dispatching problem in a multi-robot system

A novel hierarchical charging algorithm for the AGV dispatch...

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IEEE International Conference on Networking, Sensing and control

作者： Haoyi Niu Weimin Wu Tao Zhang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665440493

Charging problem for the AGVs (automated guided vehicles) in a multi-robot system based on a real-world intelligent warehouse aims to maximize the transportation efficiency of AGVs considering the charging process. For this purpose, a hierarchical charging algorithm is presented by taking into account both the continuity of transportation and the charging efficiency. A multi-level threshold scheme is designed to reduce the impact of the charging process on AGV scheduling. The numerical experiment results indicate that at the same charging rate, the number of completed tasks by AGVs can be increased from 18 to 28 an hour compared with traditional charging algorithm thus the efficiency can be improved from 34.62% to 51.86% correspondingly, which shows the superior performance of the presented algorithm.

关键词： Remotely guided vehicles Job shop scheduling Service robots Transportation Robot sensing systems Real-time systems Dispatching

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A Dynamic Joint Real-time Scheduling Strategy of Truck and Yard Crane in Container Terminals

A Dynamic Joint Real-time Scheduling Strategy of Truck and Y...

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IEEE International Conference on Networking, Sensing and control

作者： Xiaojie Jin Xingkai Wang Weimin Wu Zhenrui Rong Quanling Zhang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665440493

With the continuous growth of port container throughput, it is difficult to meet the port production demand by simply configuring automation equipments to improve the operation efficiency of machinery. The container truck scheduling and yard crane scheduling are the two important scheduling links in container terminals. At the same time, there is a tight coupling between them. Thus, the joint scheduling of container trucks and yard cranes is necessary to improve the operation efficiency of container terminals. In this paper, the joint real-time scheduling of trucks and yard cranes is mathematically modeled, and is solved by the elite genetic algorithm. Finally, a variety of scheduling strategies are compared to verify the effectiveness of the proposed strategy.

关键词： Cranes Loading Production Containers Dynamic scheduling Throughput Real-time systems

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Efficient learning of goal-oriented push-grasping synergy in clutter

arXiv

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arXiv 2021年

作者： Xu, Kechun Yu, Hongxiang Lai, Qianen Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

—We focus on the task of goal-oriented grasping, in which a robot is supposed to grasp a pre-assigned goal object in clutter and needs some pre-grasp actions such as pushes to enable stable grasps. However, in this task, the robot gets positive rewards from environment only when successfully grasping the goal object. Besides, joint pushing and grasping elongates the action sequence, compounding the problem of reward delay. Thus, sample inefficiency remains a main challenge in this task. In this paper, a goal-conditioned hierarchical reinforcement learning formulation with high sample efficiency is proposed to learn a push-grasping policy for grasping a specific object in clutter. In our work, sample efficiency is improved by two means. First, we use a goal-conditioned mechanism by goal relabeling to enrich the replay buffer. Second, the pushing and grasping policies are respectively regarded as a generator and a discriminator and the pushing policy is trained with supervision of the grasping discriminator, thus densifying pushing rewards. To deal with the problem of distribution mismatch caused by different training settings of two policies, an alternating training stage is added to learn pushing and grasping in turn. A series of experiments carried out in simulation and real world indicate that our method can quickly learn effective pushing and grasping policies and outperforms existing methods in task completion rate and goal grasp success rate by less times of motion. Furthermore, we validate that our system can also adapt to goal-agnostic conditions with better performance. Note that our system can be transferred to the real world without any fine-tuning. Our code is available at https://***/xukechun/Efficient_goaloriented_push-grasping_synergy Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Collaborative recognition of feasible region with aerial and ground robots through DPCN

arXiv

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arXiv 2021年

作者： Li, Yunshuang Huang, Zheyuan Chen, Zexi Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causing severe damage to the robots. To address this issue, we present a collaborative system with aerial and ground robots to gain precise recognition of feasible region. Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjust its pose ahead of time. Under normal circumstance, the transformation between these two devices can be obtained by GPS yet with much error, directly causing inferior influence on recognition of feasible region. Thereby, we utilize the state-of-the-art research achievements in matching heterogeneous sensor measurements called deep phase correlation network(DPCN), which has excellent performance on heterogeneous mapping, to refine the transformation. The network is light-weighted and promising for better generalization. We use Aero-Ground dataset which consists of heterogeneous sensor images and aerial road segmentation images. The results show that our collaborative system has great accuracy, speed and stability. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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A Decision-support Framework for Port Container Stowage Plan

A Decision-support Framework for Port Container Stowage Plan

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IEEE International Conference on Networking, Sensing and control

作者： Shuo Wang Tao Zhang Zichao Xing Weimin Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665440493

With the development of international trade, container transportation increases rapidly. Loading large quantities of containers requires efficient port operations and an efficient container stowage plan (CSP). A CSP is the placement of containers onto the shipping plan slots, which should decrease loading cost and keep the ship stability. The yard operational efficiency, ship stability, and operational safety are the major factors that should be considered in port loading and unloading. Since, factors are related to others, to generate a CSP is proven to be NP-complete. This paper proposes a decision-support framework for determining a shipment plan, which is to achieve the optimal shipment objects by a planning model. The experimental results show that the proposed framework can improve the operating efficiency of the storage yard while keeping the ship’s stability and reducing the dangerous loading scenes.

关键词： International trade Loading Transportation Containers Stability analysis Sensors Safety

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Performance-Aware control of Modular Batteries For Fast Frequency Response

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IEEE Transactions on Sustainable Energy 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Massachusetts Institute of Technology Lab for Information and Decision Systems CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. © 2010-2012 IEEE.

关键词： Battery management systems

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Direction and Trajectory Tracking control for Nonholonomic Spherical Robot by Combining Sliding Mode controller and Model Prediction controller

arXiv

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arXiv 2022年

作者： Liu, Yifan Wang, Yixu Guan, Xiaoqing Hu, Tao Zhang, Ziang Jin, Song Wang, You Hao, Jie Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China Luoteng Hangzhou Techonlogy Co. Ltd. Hangzhou310027 China

Spherical robot is a nonlinear, nonholonomic and unstable system which increases the difficulty of the direction and trajectory tracking problem. In this study, we propose a new direction controller HTSMC, an instruction planning controller MPC, and a trajectory tracking framework MHH. The HTSMC is designed by integrating a fast terminal algorithm, a hierarchical method, the motion features of a spherical robot, and its dynamics. In addition, the new direction controller has an excellent control effect with a quick response speed and strong stability. MPC can obtain optimal commands that are then transmitted to the velocity and direction controller. Since the two torque controllers in MHH are all Lyapunov-based sliding mode controllers, the MHH framework may achieve optimal control performance while assuring stability. Finally, the two controllers eliminate the requirement for MPC's stability and dynamic constraints. Finally, hardware experiments demonstrate the efficacy of the HTSMC, MPC, and MHH. Copyright © 2022, The Authors. All rights reserved.

关键词： controllers

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Spatial Sectorized Neural Network for 2-D DOA Estimation in the Full Azimuth

Spatial Sectorized Neural Network for 2-D DOA Estimation in ...

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European Signal Processing Conference (EUSIPCO)

作者： Feiyang Qian Hang Zheng Chengwei Zhou Zhiguo Shi State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China Jinhua Institute of Zhejiang University Jinhua China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Most existing deep learning-based direction-of- arrival (DOA) estimation methods are realized within a limited range of DOAs per training time, posing great challenges to effective DOA estimation in a full 3-D space of 360°. To address the problem, a spatial sectorized neural network is proposed for 2- D DOA estimation in the full azimuth. In particular, the full angular region is divided into a series of sectors to compress the ranges of both azimuth and elevation angles. Based on that, we formulate the full-azimuth DOA estimation problem into two tasks, namely, sector identification and angles mapping. To this end, the proposed network consists of a classifier and an estimator, thereby addressing the issue of angle discontinuity near the 0° and 360°. The estimated DOAs can be reconstructed from the outputs of the proposed network based on the angles mapping relationship, leading to an effective 2-D DOA estimation in the full azimuth. Our simulation results indicate that the proposed network outperforms the conventional model-based method and neural-network-based method in estimation accuracy.

关键词： Training Direction-of-arrival estimation Accuracy Azimuth Simulation Neural networks Estimation Europe Signal processing

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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