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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Diveev, Askhat Federal Research Center 'Computer Science and Control' The Russian Academy of Sciences Department of Robotics Control Vavilova str. 44 Moscow119333 Russia

ISBN: (纸本)9798350311402

The work is devoted to the numerical complete solution of the optimal control problem. The complete solution means the solution of the optimal control problem together with the solution of the control synthesis problem to stabilize the movement of the control object along the found optimal trajectory. To solve this problem, evolutionary computations and symbolic regression are used. First, the optimal control problem by an evolutionary algorithm in the classical formulation is solved, after that the control synthesis problem by a method of symbolic regression is solved. The statement of the complete optimal control problem is presented. The computational experiment considers the solution of the complete optimal control problem for a quadcopter. © 2023 IEEE.

关键词： Numerical methods

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Multiphysics Research and Loss Calculation Considering the Fluid Regime of the Airgap Based on High-Speed PMSM 26

Multiphysics Research and Loss Calculation Considering the F...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Su, Xiangdong Zhao, Hang Li, Fang Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350317589

This paper focuses on the calculation of different kinds of mechanical loss and put forward a complete method to calculate the mechanical loss for high-speed PMSM accurately. Windage loss, bearing loss, and cooling fan loss are calculated concretely, and the flow regime of the airgap is analyzed for windage loss calculation. Then, the equivalent thermal conductivity of the airgap in the turbulent regime is calculated, and it turned out to be much larger than that of stationary air, which cannot be ignored in high-fidelity modeling. Later, the two-way electromagnetic-thermal coupling method is applied in the multiphysics design. The results show the flow regime of the airgap cannot be neglected in windage loss calculation, and the mechanical loss calculation method put forward in this paper is more complete than previous research. Therefore, it can predict the electromagnetic-thermal performance of high-speed PMSM more accurately. © 2023 IEEE.

关键词： Multiphysics

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Design and Performance Optimization of a Novel High Temperature Superconducting Linear Flux-Switching Motor 32

Design and Performance Optimization of a Novel High Temperat...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

ISBN: (纸本)9798350399714

This paper proposes a new topology of a high-temperature superconducting linear flux switching motor (HTS-LFSM). The electromagnetic characteristics of this novel HTS-LFSM, including flux density, back-EMF, thrust force, and ripple, are analyzed. Key topology parameters of the HTS-LFSM are optimized, and its performance is compared with that of the traditional LFSPM. The comparison shows that the proposed HTS-LFSM can generate higher thrust force and smaller thrust ripple. The results indicate that the proposed machine offers more performance advantages for modern traffic. © 2023 IEEE.

关键词： Linear motors

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Machine Learning Control Synthesis by Symbolic Regression for Avoidance of Arbitrary Positioned Obstacles 9

Machine Learning Control Synthesis by Symbolic Regression fo...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Shmalko, Elizaveta Diveev, Askhat Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences The Department of Robotics Control Vavilova str. 44 Moscow119333 Russia

ISBN: (纸本)9798350311402

The problem of control synthesis for a mobile robot with phase constraints in the form of an arbitrarily located obstacle is formulated. To solve the problem, a numerical method of machine learning based on symbolic regression is used. According to the approach, in addition to its state, the object receives information about the distance to the obstacle and the direction of its location. As a result of solving the synthesis problem, we obtain a nonlinear feedback control function that provides a better estimate of the obstacle bypass according to a given criterion. An example for a mobile robot with a differential drive is considered. © 2023 IEEE.

关键词： Machine learning

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A Translation-Tolerant Place Recognition Method by Viewpoint Unification 27

A Translation-Tolerant Place Recognition Method by Viewpoint...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Linwei Hu, Xiangcheng Ma, Fulong Zhao, Guoyang Qi, Weiqing Ma, Jun Liu, Ming The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong

ISBN: (纸本)9798331505929

Place recognition serves as a fundamental component in tasks like loop closure detection and relocalization for mobile robots. Polar coordinate representations, such as Scan Context, which align with the data structure of range sensors, have become the most common data structure for point cloud descriptors in place recognition. While polar representations demonstrate the rotation invariance, they remain susceptible to translation variations. In this study, we introduce a novel approach: shifting the viewpoint of the original point cloud to construct the unified Scan Context, thereby mitigating translation variance. Our key concept focuses on identifying a stable, unified viewpoint for a given place and then pre-translating the point cloud accordingly. This naturally results in a descriptor devoid of translation variance. Importantly, within a given place, the viewpoint unification process tends to relocate the viewpoint to a similar position, irrespective of the original sensor perspective. In other words, the unified Scan Context becomes more closely associated with the place's structural characteristics than the physical location of the sensor. We validate our method through a series of comprehensive experiments encompassing synthetic scenarios and real-world datasets, showcasing its robustness in effectively handling translation variations. © 2024 IEEE.

关键词： Data structures

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DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning 38

DiffTORI: Differentiable Trajectory Optimization for Deep Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wan, Weikang Wang, Ziyu Wang, Yufei Erickson, Zackory Held, David Computer Science and Engineering Department University of California San Diego United States Institute for Interdisciplinary Information Sciences Tsinghua University China Robotics Institute Carnegie Mellon University United States

This paper introduces DiffTORI, which utilizes Differentiable Trajectory Optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function. The key to our approach is to leverage the recent progress in differentiable trajectory optimization, which enables computing the gradients of the loss with respect to the parameters of trajectory optimization. As a result, the cost and dynamics functions of trajectory optimization can be learned end-to-end. DiffTORI addresses the "objective mismatch" issue of prior model-based RL algorithms, as the dynamics model in DiffTORI is learned to directly maximize task performance by differentiating the policy gradient loss through the trajectory optimization process. We further benchmark DiffTORI for imitation learning on standard robotic manipulation task suites with high-dimensional sensory observations and compare our method to feed-forward policy classes as well as Energy-Based Models (EBM) and Diffusion. Across 15 model-based RL tasks and 35 imitation learning tasks with high-dimensional image and point cloud inputs, DiffTORI outperforms prior state-of-the-art methods in both domains. © 2024 Neural information processing systems foundation. All rights reserved.

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FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance System

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SN computer science 2024年第5期5卷 609页

作者： Imran, Ahsan Ahmed, Riad Hasan, Md Mehedi Ahmed, M. Helal Uddin Azad, A.K.M. Alyami, Salem A. Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka 1000 Bangladesh Department of Computer Science and Engineering BRAC University Dhaka 1212 Bangladesh Department of Management Information Systems University of Dhaka Dhaka 1000 Bangladesh Department of Mathematics and Statistics College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

The escalating concern over worldwide security and criminal activities has led to the emergence and significance of closed-circuit television video surveillance systems as an essential tool for diverse security purposes. These systems are extensively implemented and serve a crucial function in the surveillance and upkeep of security. The predominant purpose of video surveillance systems is to gather data primarily for evidentiary purposes subsequent to the occurrence of a criminal incident. The demand for video surveillance systems capable of autonomously monitoring and promptly identifying criminals or intruders in real-time is steadily increasing. Nevertheless, the existing facial recognition methods pose difficulties in reliably identifying individuals who are in motion within a video frame. Moreover, conventional approaches necessitate a substantial quantity of photographs in order to achieve precise recognition following the acquisition of an individual’s facial pattern. In order to tackle these concerns, we developed the implementation of input optimisation algorithms alongside a novel framework for real-time face recognition in the context of video surveillance. The input optimization algorithms, integrated with adaptive thresholding techniques, effectively reduce the need for manual outlier removal by actively identifying outliers for each specific case. The application of this optimisation strategy has demonstrated a substantial enhancement in both the efficiency and precision of our system in comparison to alternative baseline methodologies. Through the use of a reduced set of input image, our system is capable of attaining a heightened degree of improvements. Specifically, employing tracking and temporal voting techniques enables our system to accomplish a real-time face recognition accuracy of 90.91%. The findings of this study suggest that our approach has the potential to be a valuable tool in various applications that necessitate rapid and precise fac

关键词： computer vision Deep learning Face recognition Image processing Tracking Video surveillance

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UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects

UniSaT: Unified-Objective Belief Model and Planner to Search...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Nguyen, Hoa Van School of Engineering Federal University of Minas Gerais MG Belo Horizonte Brazil Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Curtin University BentleyWA6102 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unifiedobjective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Object tracking

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Ball sition ature bedded Group Activity Recognition del for Team Sport Games 4

Ball sition ature bedded Group Activity Recognition del for ...

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4th International Conference on Machine Learning and Intelligent Systems, MLIS 2022

作者： Jamsrandorj, Ankhzaya Chao, Vanyi Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook Department of Human Computer Interface & Robotics Engineering University of Science & Technology Korea Republic of Department of Ai Robotics University of Science & Technology Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Korea Republic of

ISBN: (纸本)9781643683560

Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players' motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a 'simple' conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position. © 2022 The authors and IOS Press.

关键词： Sports

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SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset

arXiv

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arXiv 2024年

作者： Hu, Yubin Wen, Kairui Zhou, Heng Guo, Xiaoyang Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University China Horizon Robotics China

Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise Synthetic Street-view 3D Mesh models exported from the CARLA simulator. These mesh models facilitate accurate position evaluation and include normal vectors for evaluating surface normal. To simulate the input data in realistic driving scenarios for 3D reconstruction, we virtually drive a vehicle equipped with six RGB cameras and five LiDAR sensors in diverse outdoor scenes. Leveraging this dataset, we establish a benchmark for state-of-the-art surface reconstruction methods, providing a comprehensive evaluation of the associated challenges. For more information, visit our homepage at https://***. © 2024, CC BY-NC-ND.

关键词： Mesh generation

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