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arXiv 2020年

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

Compared to the onboard camera and laser scanner, radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization under adverse conditions. However, radar data is sparse and noisy, resulting in challenges for radar mapping. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for exhaustive similarity evaluation against the current radar modal, yielding the regression of the current pose. Finally, we apply this differentiable measurement model to a Kalman Filter (KF) to learn the whole sequential localization process in an end-to-end manner. The whole learning system is differentiable with the network based measurement model at the front-end and KF at the back-end. To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over 90km driving, even in generalization scenarios where the model training is in UK, while testing in South Korea. We also release the source code publicly 1 Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Fast Trajectory Planning for Dubins Vehicles Under Cumulative Probability of Radar Detection

SSRN

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SSRN 2022年

作者： Li, Zhuo You, keyou Sun, Jian Song, Shiji State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Automation and BNRist Tsinghua University Beijing100084 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

This paper studies a minimum-time trajectory planning problem under radar detection, where a Dubins vehicle aims to attack a target with a limited probability of being detected. Since the probability is accumulated along the vehicle's trajectory in an integral form, we have to address a non-convex constrained functional optimization problem. To this end, Pontryagin's minimum principle is first adopted to derive the optimality condition, based on which we obtain a set of parameterized trajectories that contains all the optimal ones. Then, fast algorithms are proposed to approximately compute a minimum-time trajectory among the set. Simulations are performed to validate the effectiveness and efficiency of the proposed planning algorithms. © 2022, The Authors. All rights reserved.

关键词： Probability

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Efficient two step optimization for large embedded deformation graph based SLAM

Efficient two step optimization for large embedded deformati...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingwei Song Fang Bai Liang Zhao Shoudong Huang Rong Xiong Centre for Autonomous Systems University of Technology Sydney Broadway Australia State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the processing time grows rapidly in accordance with the expansion of maps. In this paper, we propose an approach to decouple the nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant. We observe that only partial deformable nodes in the graph are connected to visible points. Based on this fact, the sparsity of the original Hessian matrix is utilized to split the parameter estimation into two independent steps. With this new technique, we achieve faster parameter estimation with amortized computation complexity reduced from O(n 2 ) to almost O(1). As a result, the computational cost barely increases as the map keeps growing. Based on our strategy, the computational bottleneck in large scale embedded deformation graph based applications will be greatly mitigated. The effectiveness is validated by experiments, featuring large scale deformation scenarios.

关键词： Simultaneous localization and mapping Strain Jacobian matrices Optimization Cameras Deformable models Geometry

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Jiajun Miao Yujun Shen Yan Zhang Lingjie Liu Ye Tian Yu Zhao Chunyan Zhou College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs.

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Advanced Robot Programming: a Review

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Current Robotics Reports 2020年第4期1卷 251-258页

作者： Zhou, Zhongxiang Xiong, Rong Wang, Yue Zhang, Jiafan State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control of Zhejiang Province Zhejiang University Hangzhou China Research Center China Robotics and Discrete Automation ABB Shanghai China

The review presents an overview of advanced robot programming approaches which aims to ease robot programming and speed up the deployment of industrial robots, and then some considerations are shared with respect to requirements in new trends of manufacturing. The new trend of customization along with Industry 4.0 is appearing which is a challenge for robotized systems. The bottleneck is mainly the efficiency of deployment of industrial robots because traditional programming methods are not intuitive and always time-consuming. Advanced robot programming techniques are expected to ease robot programming and make it accessible for non-experts. A review on advanced robot programming is here presented, firstly introducing the background of this research, followed by reviewing literatures in four categories: programming by demonstration for low-level motion, programming by demonstration for high-level task, speech recognition-based and augmented reality-based programming approaches, and finishing on discussing future works.

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Roller-Quadrotor: A Novel Hybrid Terrestrial/Aerial Quadrotor with Unicycle-Driven and Rotor-Assisted Turning

arXiv

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arXiv 2023年

作者： Zheng, Zhi Wang, Jin Wu, Yuze Cai, Qifeng Yu, Huan Zhang, Ruibin Tu, Jie Meng, Jun Lu, Guodong Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor configuration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to navigate through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely navigate through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient navigation through challenging terrains while conserving energy. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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Data-driven resilient predictive control under denial-of-service

arXiv

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arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States Department of Control Science and Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method. © 2021, CC BY.

关键词： Model predictive control

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Simultaneous Precision Assembly of Multiple Objects through Coordinated Micro-robot Manipulation

Simultaneous Precision Assembly of Multiple Objects through ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Song Liu Yuyu Jia You-Fu Li Yao Guo Haojian Lu ShanghaiTech Automation and Robotics Center School of Information Science and Technology ShanghaiTech University Shanghai China City University of Hong Kong Kowloon Hong Kong Institute of Medical Robotics Shanghai Jiaotong University Shanghai China State Key Laboratory of Industrial Control and Technology and Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Simultaneous assembly of multiple objects is a key technology to form solid connections among objects to get compact structures in precision assembly and micro-assembly. Dramatically different from traditional assembly of two objects, the interaction among multiple objects is more complicated on analysis and control. During simultaneous assembly of multiple objects, there are multiple mutually effected contact surfaces, and multiple force sensors are needed to perceive the interaction status. In this paper, a coordinated micro-robot manipulation strategy is proposed for simultaneous assembly problem, which is based on microscopic vision and force information. Taking simultaneous assembly of three objects as an instance, the proposed method is well articulated, including calibration of assembly system, force analysis for each contacting surface, and insertion control strategy for assembly process. The proposed method is applicable also to case with more objects. Experiment results demonstrate effectiveness of the proposed method.

关键词： Automation Shape Microscopy Conferences Force Process control Robot sensing systems

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Optimized Configuration of Location and Size for DGs and SCs in Radial Distributed Networks Based on Improved Butterfly Algorithm

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IAENG International Journal of Applied Mathematics 2022年第1期52卷 1页

作者： Chen, Gonggui Zhao, Xinxin Peng, Kang Zhou, Ping Zeng, Xianjun Long, Hongyu Zou, Mi Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China State Grid Chongqing Electric Power Company Chongqing401120 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Abstract—The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Distributed power generation

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