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arXiv 2021年

作者： Shen, Bingqi Chen, Yuyin Yang, Huiyong Zhan, Jianbo Sun, Yichao Xiong, Rong Dai, Shuwei Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Mechanical and Vehicle Engineering Hunan University Changsha China Hangzhou Iplus Technology Co. Ltd Hangzhou China

The automatic road roller, as a popular type of construction robot, has attracted much interest from both the industry and the research community in recent years. However, when it comes to tunnels where the degeneration issues are prone to happen, it is still a challenging problem to provide an accurate positioning result for the robot. In this paper, we aim to deal with this problem by fusing LiDAR and UWB measurements based on optimization. In the proposed localization method, the directions of non-degeneration will be constrained and the covariance of UWB reconstruction will be introduced to improve the accuracy of localization. Apart from these, a method that can extract the feature of the inner wall of tunnels to assist positioning is also presented in this paper. To evaluate the effectiveness of the proposed method, three experiments with real road roller were carried out and the results show that our method can achieve better performance than the existing methods and can be applied to automatic road roller working inside tunnels. Finally, we discuss the feasibility of deploying the system in real applications and make several recommendations. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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DiSCO: Differentiable Scan Context with Orientation

arXiv

引用

arXiv 2020年

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

Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the environmental change. In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation. The orientation can further be used as the initial value for the down-stream local optimal metric pose estimation, improving the pose estimation especially when a large orientation between the current scan and retrieved scan exists. Our key idea is to transform the feature learning into the frequency domain. We utilize the magnitude of the spectrum as the place signature, which is theoretically rotation-invariant. In addition, based on the differentiable phase correlation, we can efficiently estimate the global optimal relative orientation using the spectrum. With such structural constraints, the network can be learned in an end-to-end manner, and the backbone is fully shared by the two tasks, achieving interpretability and light weight. Finally, DiSCO is validated on the NCLT and Oxford datasets with long-term outdoor conditions, showing better performance than the compared methods. 1 Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency domain analysis

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External forces resilient safe motion planning for quadrotor

arXiv

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arXiv 2021年

作者： Wu, Yuwei Ding, Ziming Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

Adaptive autonomous navigation with no prior knowledge of extraneous disturbance is of great significance for quadrotors in a complex and unknown environment. The mainstream approach that considers external disturbance is to implement disturbance-rejected control and path tracking. However, the robust control to compensate for tracking deviations is not well-considered regarding energy consumption, and even the reference path will become risky and intractable with disturbance. As recent external forces estimation advances, it is possible to incorporate a real-time force estimator to develop more robust and safe planning frameworks. This paper proposes a systematic (re)planning framework that can resiliently generate safe trajectories under volatile conditions. Firstly, a front-end kinodynamic path is searched with force-biased motion primitives. Then we develop a nonlinear model predictive control (NMPC) as a local planner with Hamilton-Jacobi (HJ) forward reachability analysis for error dynamics caused by external forces. It guarantees collision avoidance by constraining the ellipsoid of the quadrotor body expanded with the forward reachable sets (FRSs) within safe convex polytopes. Our method is validated in simulations and real-world experiments with different sources of external forces. Copyright © 2021, The Authors. All rights reserved.

关键词： Collision avoidance

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Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map

Collaborative Localization of Aerial and Ground Mobile Robot...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Xuecheng Xu Zexi Chen Jiaxin Guo Yue Wang Yunkai Wang Rong Xiong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems who have large scale variance of view points of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software “Metashape”, map matching with a proposed template matching algorithm, weighted normalized cross correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with a feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset [1].

关键词： Mobile robots Robots Cameras Robot vision systems Simultaneous localization and mapping Collaboration Correlation

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Event-Triggered Adaptive control for a Class of Nonlinear systems with Unknown Time-Varying Parameters

Event-Triggered Adaptive Control for a Class of Nonlinear Sy...

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Wenxiu Zhuang Jing Zhou Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we investigate the event-triggered output tracking problem for a class of nonlinear systems with unknown time-varying parameters. To reduce the communication burden, an event-triggered adaptive control method is proposed by introducing hyperbolic tangent functions in the controller to compensate for the effects of the time-varying parameters. New estimation laws are developed to estimate the bounds of time-varying parameters, where no any prior information about the bounds of unknown time-varying parameters is required. An additional term is introduced in the parameter estimation law to compensate for the effects of unknown time-varying parameters. The proposed control scheme can effectively reduce the communication burden while maintain global stability of the closed-loop system, which means that all the signals are bounded. And the tracking error converges towards an adjustable set, which can be exactly expressed with the user-defined parameters. Simulation results are given to show the performance of the proposed method.

关键词： Parameter estimation Simulation Stability analysis Nonlinear systems Adaptive control Time-varying systems Tuning

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A Fast Cornering Method Based On Model Predictive control

A Fast Cornering Method Based On Model Predictive Control

引用

Chinese Automation Congress (CAC)

作者： Wei Li Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology and Institute of Cyber-systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In the automatic driving of vehicles, it is a very common way to separate the motion control into two levels, the first is trajectory planning and the next is path tracking. In this paper, we introduce a fast cornering way for the vehicle based on the two steps. First, choosing a better trajectory rather than centerline of the road to follow in order to reduce the passing time. The desired trajectory is described as the optimal linear combination of the shortest path and the fastest speed path. It was expressed as a bounded optimal problem to solve. Once the trajectory has been determined, it is necessary to design a path tracker. We construct a controller based on vehicle's dynamics and environmental conditions using model predictive control (MPC). Then an experiment was implemented on CARSIM and MATlab joint simulation. And the results showed that the presented approach is able to reduce the passing time and keep the vehicle within the lane.

关键词： Trajectory Roads Predictive models Mathematical model Optimization Vehicle dynamics Trajectory planning

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Learning to Compensate for the Drift and Error of Gyroscope in Vehicle Localization

Learning to Compensate for the Drift and Error of Gyroscope ...

引用

IEEE Symposium on Intelligent Vehicle

作者： Xiangrui Zhao Chunfang Deng Xin Kong Jinhong Xu Yong Liu Institute of Cyber-Systems and Control Zhejiang University Zhejiang China State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

Self-localization is an essential technology for autonomous vehicles. Building robust odometry in a GPS-denied environment is still challenging, especially when LiDAR and camera are uninformative. In this paper, We propose a learning-based approach to cure the drift of gyroscope for vehicle localization. For consumer-level MEMS gyroscope (stability ~10° /h), our GyroNet can estimate the error of each measurement. For high-precision Fiber optics Gyroscope (stability ~0.05° /h), we build a FoGNet which can obtain its drift by observing data in a long time window. We perform comparative experiments on publicly available datasets. The results demonstrate that our GyroNet can get higher precision angular velocity than traditional digital filters and static initialization methods. In the vehicle localization, the FoGNet can effectively correct the small drift of the Fiber optics Gyroscope (FoG) and can achieve better results than the state-of-the-art method.

关键词： Gyroscopes Wheels Neural networks Angular velocity Time measurement Noise measurement Micromechanical devices

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PREGAN: Pose randomization and estimation for weakly paired image style translation

arXiv

引用

arXiv 2020年

作者： Chen, Zexi Guo, Jiaxin Xu, Xuecheng Wang, Yunkai Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the image style from another environment to the one on which models are trained. In this paper, we propose a weakly-paired setting for the style translation, where the content in the two images is aligned with errors in poses. These images could be acquired by different sensors in different conditions that share an overlapping region, e.g. with LiDAR or stereo cameras, from sunny days or foggy nights. We consider this setting to be more practical with: (i) easier labeling than the paired data;(ii) better interpretability and detail retrieval than the unpaired data. To translate across such images, we propose PREGAN to train a style translator by intentionally transforming the two images with a random pose, and to estimate the given random pose by differentiable non-trainable pose estimator given that the more aligned in style, the better the estimated result is. Such adversarial training enforces the network to learn the style translation, avoiding being entangled with other variations. Finally, PREGAN is validated on both simulated and real-world collected data to show the effectiveness. Results on down-stream tasks, classification, road segmentation, object detection, and feature matching show its potential for real applications. Code is available here. Copyright © 2020, The Authors. All rights reserved.

关键词： Stereo image processing

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Distributed Adaptive Cooperative control for a Class of Nonlinear Multi-Agent systems with Input Saturation

Distributed Adaptive Cooperative Control for a Class of Nonl...

引用

IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Ming Xiao Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, the consensus control for a class of nonlinear multi-agent systems with input saturation is considered. A smooth function is used to approximate the saturation function for every agent, then by combining the adaptive backstepping method and Nussbaum function, a new distributed adaptive cooperative control method is proposed for the multi-agent system with input saturation. The consensus tracking errors of the multi-agent system are bounded, and the closed-loop system is globally stable. Simulation results of three-agents with input saturation shows the effectiveness and performance of the proposed method.

关键词： industrial electronics Backstepping Simulation Conferences Stability analysis Tuning Multi-agent systems

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Distributed Adaptive Cooperative control for a Class of Nonlinear Multi-Agent systems via Fully Event-triggered Mechanism

Distributed Adaptive Cooperative Control for a Class of Nonl...

引用

Annual Conference of industrial Electronics Society

作者： Ming Xiao Zhitao Liu Hongye Su Weihua Xu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

In this paper, the distributed cooperative control for a class of nonlinear multi-agent systems connected by undirected graph is investigated. The dynamic of each agent is first order with unknown mismatched parameters and external disturbances. To reduce the communication burden between each agent and decrease the update frequency of the controllers, the fixed threshold event-triggered mechanisms are considered during the states's broadcasting and the control signals. Based on the adaptive method, the event-based cooperative control protocol is constructed. The asymptotically consensus tracking of all agents are guaranteed and the signals in the closed-loop system are bounded, meanwhile the Zeno behavior is excluded. Simulation results of four agents show the effectiveness and performance of the proposed method.

关键词： Broadcasting Multi-agent systems Protocols Stability analysis Nonlinear dynamical systems Measurement errors Lyapunov methods

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