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检索条件"机构=Robotics Automation Control Laboratory"

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Cross-Modal Monocular Localization in Prior LiDAR Maps Utilizing Semantic Consistency

Cross-Modal Monocular Localization in Prior LiDAR Maps Utili...

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IEEE International Conference on robotics and automation (ICRA)

作者： Chi Zhang Hengwang Zhao Chunxiang Wang Xuanlai Tang Ming Yang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China KEENON Robotics Co. Ltd. China

Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization system in prior LiDAR maps, which is based on the cross-modal registration to optimize the camera pose. To align the point clouds from vision and LiDAR map, a point-to-plane Iterative Closest Point algorithm utilizing semantic consistency is designed, and a decoupling optimization strategy is proposed to compute the affine transformation for the monocular scale ambiguity. Experiments on KITTI dataset show that utilizing the semantic consistency and geometric information of the map makes our system competitive with other methods. On the self-collected dataset, experiments on different light intensities demonstrate the robustness of the system in long-term localization tasks, and the ablation study demonstrates the effectiveness of the proposed algorithms.

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Dimension Reduction for Dynamic Analysis of Gene Expression Data Using Variational Autoencoder

Dimension Reduction for Dynamic Analysis of Gene Expression ...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Shuai Yuan Shipeng Chen Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China

Single-cell RNA sequencing technology captures gene expression at the whole-genome scale and single-cell level in many cells. Thus, the cellular states during a physiological process would be captured in detail. These data provide new opportunities and challenges for systems biology. Previously, the models in systems biology dealt with the dynamics of specific signaling pathways. However, it is almost impossible to directly model the dynamics of the whole genome due to the ultrahigh dimensionality of the data. Here, to model its gene regulatory dynamics, the dimensionality of the whole genome was reduced significantly using the latent space of a variational autoencoder, a generative deep learning model. In the low-dimensional latent space, an ordinary differentiation equation (ODE) system has been established to analyze the dynamic characteristics of the whole genome system. To demonstrate the effectiveness of this method, we generated a low-dimensional representation of a systems biology model we previously developed. The variational autoencoder reduced the dimension from 13 to 2 in latent space. An ODE model was developed to analyze the dynamics of this two-dimensional system. The results indicated that the 2-dimensional model captured the essence of the original systems biology model.

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems Computational efficiency

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Low-noise analysis and design for the sensing system of the nanomanipulation robot by AFM

Low-noise analysis and design for the sensing system of the ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yuan, Shuai Wu, Boyu Wang, Kaixuan Yu, Peng School of Information & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665481090

In this paper, through the noise analysis of the signal processing circuit of the photoelectric sensor, two noise reduction methods are proposed by reducing the feedback resistance of the transimpedance amplifier and integrating the overall signal processing circuit. These two noise reduction methods can reduce noise and improve the signal-to-noise ratio while ensuring that the topography signal of the item is unchanged. To verify the method in this paper, the scanning head design was optimized, the feasibility of the proposed scheme was verified by the thermal noise method and the 0-range scanning method, and finally, the roughness specification was analyzed. © 2022 IEEE.

关键词： Operational amplifiers

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A Local Area Point Cloud Feature Extraction Method Based on Octree Retrieval

A Local Area Point Cloud Feature Extraction Method Based on ...

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第34届中国控制与决策会议

作者： Jian Liu Hang Yuan Ting Wang Zili Song School of Information and Control Engineering Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

As one of the key tasks of point cloud data processing,3 D point cloud semantic segmentation plays a vital role in the understanding of the scene in the 3 D *** three-dimensional point cloud is a direct and simple way of representing three-dimensional *** entire point cloud collection can reflect the geometric information of the scene or the *** at how to construct a local area around the key points of the feature,a local area point cloud feature extraction method based on octree retrieval is proposed,which follows the Encoder-Decoder architecture,and uses the skip-link structure in the Decoder stage to integrate shallow and deep *** ensure that each point in the output result can be assigned a reasonable semantic label,training and testing are carried out on different data sets,and the comparison of experimental results shows that the local area feature extraction method based on octree retrieval can achieve better experimental results.

关键词： Three-Dimensional Point Cloud Octree Retrieval Feature Extraction

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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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第35届中国控制与决策会议

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC

Automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to,e.g.,cyber attacks,unexpected blocks of the wireless *** order to ensure AGV running safely,this paper presents a visual surveillance system by making full use of the measurements from the forward and downward ***,the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image ***,the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference *** from that,the AGV poses from the downward camera could correct scale and poses estimated the forward *** proposed method has been extensively performed on the developed *** results proves the effectiveness in using the complementary forwarddownward visual measurements for AGV security surveillance.

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Application of Fuzzy Sliding Mode control in Voice Coil Motor control System 6

Application of Fuzzy Sliding Mode Control in Voice Coil Moto...

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2022 6th International Conference on Artificial Intelligence, automation and control Technologies, AIACT 2022

作者： Zhao, Bingjie Liu, Yang Jia, Danping Zhang, Tao Zhang, Hualiang Yan, Hongyu Lu, Xu School Of Artificial Intelligence Shenyang University Of Technology Shenyang China Key Laboratory Of Networked Control Systems Chinese Academy Of Sciences Shenyang China Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Institutes For Robotics And Intelligent Manufacturing Chinese Academy Of Sciences Shenyang China

The requirements of modern industry on the response speed and accuracy of voice coil motors has been the gradual growth, and PID control algorithms have become increasingly unable to meet their requirements. The use of sliding mode control can speed up the response time of the motor, and it also can improve the response speed, overshoot and instability problems in some degree. However, the sliding mode control algorithm will bring jitter to the entire system. This paper combines fuzzy control and sliding mode control to adjust the parameters of the sliding mode control algorithm in real time, which greatly reduces the jitter problem caused by the sliding mode. According to the actual parameters of the voice coil motor, the transfer function of the voice coil motor is deduced, a simulation model of fuzzy sliding mode control is built, and the sliding mode control and fuzzy sliding mode control algorithms are compared. Finally, two signals of step and sine are used as inputs for simulation model. It can be seen through the simulation waveform that the fuzzy sliding mode control algorithm can improve the response speed of the control system and reduce the tracking error. © Published under licence by IOP Publishing Ltd.

关键词： Sliding mode control

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An Algorithm for Solving the Pose of Bio 3D Printing based on Neural Network

An Algorithm for Solving the Pose of Bio 3D Printing based o...

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第34届中国控制与决策会议

作者： Jian Liu Zili Song Huixuan Zhu Hang Yuan School of Information and Control Engineering Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

Obtaining the pose state information of the printing nozzle in real time and performing error compensation is an important means to improve the accuracy of biological 3D *** pose solving methods such as Newton iteration method and Analysis method have problems such as difficulty in selecting the initial value and slow calculation *** solve the above problems,a biological 3D printing pose solution algorithm based on the Elman neural network optimized by genetic algorithm is ***,the kinematics model of the biological 3D printing platform is established,then the GA-Elman neural network is established and the inverse solution of the platform is used as a training sample,and the pose information is solved by the method of network learning,and finally the accuracy of the algorithm is verified by *** simulation results show that the algorithm has high calculation accuracy and fast solution speed,and can quickly and accurately solve the pose of biological 3D printing.

关键词： Biological 3D printing Pose solving Neural Network Real time

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Development of a Method for Generating Extremely High Reference Motion Speeds of Mechatronic Objects Moving Alone or in a Group

Development of a Method for Generating Extremely High Refere...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Anton Gubankov Igor Gornostaev Robotics Laboratory Institute of Automation and Control Processes FEB RAS Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Admiral G.I. Nevelskoy Maritime State University Vladivostok Russia Laboratory of Robotic Systems M.D. Ageev Institute of Marine Technology Problems FEB RAS

The paper presents the results of developing a method for generating reference control signals that describe the desired law of movement of various mechatronic objects (unmanned aerial vehicles, mobile land and underwater robots, etc.) along smooth trajectories specified by parametric splines. In order to maintain high dynamic accuracy of control of these mechatronic objects, including when solving group control problems, it is proposed to adjust their reference movement speed, taking into account the specified restrictions on the developed accelerations. Taking into account these restrictions is necessary to reduce the reference speed in sections of the trajectory with increased curvature. To implement the proposed approach, a method was developed and a system was synthesized, the performance of which was confirmed by the results of numerical simulation. The presented system is designed to operate in conditions where the reference trajectory of a mechatronic object is unknown in advance and is formed (or adjusted) directly during the mission without the possibility of preliminary calculations. Also, this system does not require complex calculations and can be implemented on the basis of low-power on-board computers.

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A Fast-trained and Generalized Spiking Neural Network for Robotic Gas Source Localization

A Fast-trained and Generalized Spiking Neural Network for Ro...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Chiyang Liu Xinxing Chen Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Mobile robots for Gas Source Localization (GSL) tasks are a safer alternative than human and animal rescuers in hazardous scenarios. Existing research primarily concentrates on rule-based algorithms or conventional artificial neural networks (ANNs). However, these approaches are either inapplicable in cluttered environments or undeployable due to their high energy consumption and the demand for substantial computational resources. This paper introduces the application of energy-efficient spiking neural networks (SNNs) to address robotic GSL tasks. A pipeline is proposed to train SNNs with deep Q learning and a pretrain-finetune paradigm. To facilitate the training process, a small dataset of gas dispersion is generated utilizing openFoam and GADEN, a high-fidelity simulator for gas dispersion. Data from a simplified plume model are leveraged to pretrain an ANN, the activation function of which gradually transitions from a bounded rectified linear unit (bReLU) to a step function. Subsequently, an SNN initialized with the ANN parameters undergoes finetuning on the GADEN-based dataset. The training pipeline significantly reduces training time compared to direct training of SNNs. The trained SNN is validated within the GADEN simulation environment and compared to three different models, demonstrating promising performance and superior generalization despite limited training data.

关键词： Training Location awareness Q-learning Mechatronics Pipelines Training data Spiking neural networks Data models Mobile robots Dispersion

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