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arXiv 2020年

作者： Chen, Runjian Yin, Huan Jiao, Yanmei Dissanayake, Gamini Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Center for Autonomous System University of Technology Sydney Australia

Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL [1]. However, traditional MCL can only sample from a uniform distribution over the state space. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes. To better deal with these problems, we present a distribution proposal model named Deep Samplable Observation Model (DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the accurate pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability than previous methods in both synthetic and real scenes. Even in real environments with long-term changes, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map. Source code for this paper is available here. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Real-time Double-layer Index Structure for Massive Point Cloud Data

Real-time Double-layer Index Structure for Massive Point Clo...

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International Symposium on autonomous systems (ISAS)

作者： Chenxi Ou Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781665418829

ISBN: (纸本)9781665447997

To handle the problems of point cloud data quantity explosion and urgent need for real-time access to massive point cloud data in collecting and storing phase, this paper proposes an index technology with internal and external storage interactions, which is based on extended 3D grid in memory layer and B-tree in external storage layer. The experiment shows that it is efficient to retrieve massive point cloud data by using this double-layer index structure.

关键词： Three-dimensional displays Memory management Data acquisition Real-time systems Explosions Encoding Indexes

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The Solution of Drone Attitudes on Lie Groups

The Solution of Drone Attitudes on Lie Groups

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaoji Yang Hailong Pei Ministry of Education Key Laboratory of Autonomous Systems and Networked Control Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, we generalize the Extended Kalman Filter algorithm on Lie Groups for the attitude propagation and update of the unmanned helicopters. The data noises obtained by sensors are assumed to be independent as Gaussian distributions, and the posterior distribution of the estimated state is also Gaussian distribution. The attitude of the drone can be obtained from the accelerometers, the gyroscopes, and the magnetometers. These three sensors need to be fused to obtain the optimal estimated value. Among them, the method of multiple sub-samples is used to compensate for the error generated by the long sampling period of the gyroscope, and we use a Chi-square detection method to determine whether the observation information is valid during the updating process.

关键词： Accelerometers Algebra Kalman filters Sensors Gaussian distribution Gyroscopes Magnetometers

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Compliant Motion Adaptation with Dynamical system during Robot-Environment Interaction

Compliant Motion Adaptation with Dynamical System during Rob...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Haohui Huang Chenguang Yang Chun-Yi Su Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Compliant control ability is essential for physical robot-environment interaction. This paper presents a novel framework for robots to achieve active compliant behaviour through Dynamical system (DS). In contrast to the existing research works that aim to improve the compliant performance by introducing an appropriate impedance model or force control scheme, in our framework, no prerequisite for predefined robot impedance parameters or environmental model is required. The proposed method can generate a continuous and adaptive trajectory in terms of the contacting force by designing a globally asymptotically stable DS to guarantee a safe interaction with unknown environment. The method is not only strictly derived from theoretical study, but also verified by extensive simulations and experiments on different shapes of contacting object and different types of DS model.

关键词： Force Robots Adaptation models Tracking Trajectory Task analysis Impedance

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Ranging-aided Aerobridge Navigation Using Dual Quaternion Based Multiplicative Extended Kalman Filter

Ranging-aided Aerobridge Navigation Using Dual Quaternion Ba...

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International Symposium on autonomous systems (ISAS)

作者： Ruican Xia Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (数字)9781665418829

ISBN: (纸本)9781665447997

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on Robotics Toolbox and the experiment results are positive.

关键词： Navigation Fuses Robot kinematics Quaternions Pose estimation Kinematics Kalman filters

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Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

arXiv

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arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

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Analysis on Stability of Three-loop Autopilot with An Acceleration Feedback Augmentation Loop

Analysis on Stability of Three-loop Autopilot with An Accele...

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第40届中国控制会议

作者： Hui Wang Tao Li Daoguang Tang Junfang Fan Yifang Yuan Luyao Zang Wugang Wang Chunyan Xiao School of Aerospace Engineering Beijing Institute of Technology Beijing Key Laboratory of UAV Autonomous Control Beijing Institute of Technology School of Electrical and Control Engineering North University of China Beijing Key Laboratory of High-Dynamic Navigation Technology Beijing Information Science and Technology University China PLA 63961 Unit Beijing System Design Institute of Electro-Mechanic Engineering Northwest Industries Group CO. Ltd.

In order to construct a stable augmentation loop, a novel three-loop autopilot structure with an acceleration feedback is proposed. Based on LQR and analytical methods, the design process of the controller parameters is deduced. For a static unstable missile, the control characteristics of a three-loop autopilot with acceleration feedback are compared with classical three-loop and pseudo-angle-of-attack feedback three-loop autopilots. It is pointed out that the closed-loop gain of the three-loop autopilot with acceleration signal feedback is 1, which is not affected by the control parameters. The autopilot can control the static unstable missile, and the acceleration signal is easier to obtain than the pseudo-angle-of-attack.

关键词： Acceleration feedback Stable augmentation loop Autopilot Static unstable missile

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Stiffness Estimation and Intention Detection for Human-Robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation Robot sensing systems Manipulators Robots

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A Fast Compression Algorithm Based on the Variable Block for 3D Point Cloud Data

A Fast Compression Algorithm Based on the Variable Block for...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Zunran Wang Chenguang Yang Long Cheng Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, a fast compression algorithm based on the variable block for 3D point cloud data is proposed, the key of the method is to reduce the temporal redundancy through finding the temporal correlation between the two consecutive frames. The first, the macroblock is generated by voxelizing the point cloud data. Then, the two consecutive frames are aligned, and the corresponding macroblocks inside the two consecutive frames are preliminarily judged whether they are consistent according to the point number of the macroblock and the color variance. Finally, the ICP algorithm is exploited to further match the two macroblocks. When the result of the ICP algorithm is lower than the Threshold, the macroblock in the current frame is replaced by the motion vectors for compressing the point cloud data. The motion vectors are acquired by the ICP algorithm applied for the corresponding macroblock between two consecutive frames. The Predicted-frame (P frame) consists of the motion vectors and no matched macroblock in the current frame while the Intra-frame (I frame) is the point cloud data. In our method, in order to improve the compression ratio, the size of the macroblock is set as a variable parameter. The experimental results demonstrate that the time delay will be decreased, meantime the compression ratio will be increased in our method. The visual result of our method also will be more smooth and the performance of our method is better than the state-of-the-art method.

关键词： Three-dimensional displays Octrees Image color analysis Iterative closest point algorithm Prediction algorithms Encoding Transforms

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