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共 24622 条记录，以下是351-360 订阅

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Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation 39

Symphony: Learning Realistic and Diverse Agents for Autonomo...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Igl, Maximilian Kim, Daewoo Kuefler, Alex Mougin, Paul Shah, Punit Shiarlis, Kyriacos Anguelov, Dragomir Palatucci, Mark White, Brandyn Whiteson, Shimon Waymo Res Mountain View CA 94043 USA

ISBN: (纸本)9781728196817

Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on the road. However, existing LfD methods are typically insufficient, yielding policies that frequently collide or drive off the road. To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. However, it can also harm diversity, i.e., how well the agents cover the entire distribution of realistic behaviour, as pruning can encourage mode collapse. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. The use of such goals ensures that agent diversity neither disappears during adversarial training nor is pruned away by the beam search. Experiments on both proprietary and open Waymo datasets confirm that Symphony agents learn more realistic and diverse behaviour than several baselines.

关键词： Training automation Roads Diversity reception Search problems Trajectory Automobiles

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Two Points Classical Identification methods on the Example of a Three-Tank Interacting and Non-Interacting System 26

Two Points Classical Identification Methods on the Example o...

引用

26th international conference on methods and models in automation and robotics

作者： Czemplik, Anna Jagodzinski, Jacek Wroclaw Univ Sci & Technol Dept Control Syst & Mechatron Wroclaw Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

This paper mainly focuses on various two-point methods of identifying the First Order Plus Time Delay (FOPTD) model, and their impact on control quality. The following criteria were used to match the identification method: ISE between the response of the real system and the model and the normalized delay time. The work used two different tuning techniques: QDR and pidtune. A three-tank interacting and non-interacting system was used as a model for the tests. All tests were simulated in Matlab environment, ISE and settling time were used to assess the quality of control.

关键词： First Order process Plus Time Delay (FOPTD) two-point methods identification interacting and non-interacting system

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Modeling of viscoelastic dielectric elastomer actuators based on the sparse identification method 39

Modeling of viscoelastic dielectric elastomer actuators base...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Li, Jisen Wang, Hao Zhu, Jian Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China

ISBN: (纸本)9781728196817

Dielectric elastomer actuators (DEAs) have been widely employed to drive various soft robots, due to their quiet fast muscle-like behavior. It is significant but challenging to model and control these soft actuators, due to their viscoelastic property, irregular geometry, complex structure, etc. In this paper, we propose a data-driven sparse identification method to discover the hidden governing equations of DEAs. These equations can help us interpret the nonlinear properties of DEAs. Due to their low computational cost, we can further use these equations to explore classic model-based control methods for real-time accurate control of viscoelastic DEAs. The experiments show that the proposed method can model the viscoelastic behavior of the DEAs with reasonable accuracy. A feedforward controller is finally developed to validate the effectiveness of the proposed method. It is expected that this modeling method can pave the way for accurate control of soft actuators/robots with structural and material nonlinearities.

关键词： Geometry Uncertainty Computational modeling Predictive models Soft robotics Mathematical models Real-time systems

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Regularization Continual Learning Based on Bayesian Uncertainty Modeling for P300 Brain-Computer Interface 6

Regularization Continual Learning Based on Bayesian Uncertai...

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6th World Robot conference (WRC) / Symposium on Advanced robotics and automation (SARA)

作者： Huang, Qianqi Zhi, Hongyi Zhang, Hao Gu, Zhenghui South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9798331506100

The P300 is a specific component of the event-related potentials (ERPs) and has been extensively utilized in brain-computer interface (BCI) applications. Numerous neural network models have been implemented to detect P300 and achieve outstanding results in intra-subject scenarios. However, in real-world situations where data streams from different subjects arrive sequentially, intra-subject models are time-consuming and resource-intensive. This necessitates research into cross-subject scenarios. To address this issue, we propose a continual learning (CL) method, named Elastic Weight Consolidation with Bayesian Neural Network (EWC-BCNN), for cross-subject P300 decoding. Specifically, EWC-BCNN comprises two main modules: EWC and BCNN. EWC employs a regularization term to penalize significant changes in parameters deemed important. Additionally, BCNN quantifies parameter uncertainty, identifying and protecting crucial knowledge. By integrating BCNN, EWC can make more informed decisions about which weights to preserve and to what extent, thus enhancing its ability to balance the retention of past knowledge with the adaptation to new information. We evaluated our method on two public P300 datasets. Our experimental results demonstrate that EWC-BCNN achieves better P300 detection performance than point-estimate networks. Furthermore, EWC-BCNN outperforms other state-of-the-art CL methods.

关键词： Neural network models

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Completely Rational SO(n) Orthonormalization

Completely Rational SO(n) Orthonormalization

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wu, Jin Sarabandi, Soheil Jiao, Jianhao Huang, Huaiyang Xue, Bohuan Geng, Ruoyu Wang, Lujia Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Pisa Sch Engn Res Ctr E Piaggio Pisa Italy HKUST Guangzhou Guangzhou 511400 Guangdong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res I Shenzhen Peoples R China

ISBN: (纸本)9798350323658

The rotation orthonormalization on the special orthogonal group SO(n), also known as the high dimensional nearest rotation problem, has been revisited. A new generalized simple iterative formula has been proposed that solves this problem in a completely rational manner. Rational operations allow for efficient implementation on various platforms and also significantly simplify the synthesis of large-scale circuitization. The developed scheme is also capable of designing efficient fundamental rational algorithms, for example, quaternion normalization, which outperforms long-exisiting solvers. Furthermore, an SO(n) neural network has been developed for further learning purpose on the rotation group. Simulation results verify the effectiveness of the proposed scheme and show the superiority against existing representatives. Applications show that the proposed orthonormalizer is of potential in robotic pose estimation problems, e.g., hand-eye calibration.

关键词： Iterative methods

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APF-RL: Safe Mapless Navigation in Unknown Environments 39

APF-RL: Safe Mapless Navigation in Unknown Environments

引用

IEEE international conference on robotics and automation (ICRA)

作者： Bektas, Kemal Bozma, H. Isil Bogazici Univ Intelligent Syst Lab Elect & Elect Engn Istanbul Turkey Bogazici Univ Syst & Control Engn Istanbul Turkey

ISBN: (纸本)9781728196817

This paper is focused on safe mapless navigation of mobile robots in unknown and possibly complex environments containing both internal and dynamic obstacles. We present a novel modular approach that combines the strengths of artificial potential functions (APF) with deep reinforcement learning. Differing from related work, the robot learns how to adjust the two input parameters of the APF controller as necessary through soft actor-critic algorithm. Environmental complexity measures are introduced in order to ensure that the robot's training covers a range of learning scenarios that vary in regard to maneuvering difficulty. Our experimental results show that differing from the classical navigation methods and end-to-end models, the robot can navigate successfully on its own even in complex scenarios with moving entities without requiring any maps.

关键词： Training Three-dimensional displays automation Navigation Heuristic algorithms Reinforcement learning Complexity theory

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A Calibration Method based on Heteroscedastic Evolutionary Bayesian Optimization for Simulation System of HVAC 6

A Calibration Method based on Heteroscedastic Evolutionary B...

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6th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Tan, Zhe Liu, Bo School of Automation Wuhan University of Technology Wuhan China

ISBN: (纸本)9798331541699

In recent years, increasing the energy efficiency of buildings has become one of the objectives of facility managers. Advanced control methods can be used to improve the efficiency of Heating, Ventilation, and Air Conditioning (HVAC). However, advanced control methods cannot be effectively applied due to inconvenient commissioning. Therefore, simulating and modeling the HVAC system becomes particularly important for verifying various control algorithms. Modeling and calibrating the HVAC simulation system becomes crucial for the application of various control strategies based on actual operational data, while current calibration methods are usually based on experience. In this research, a method for the calibration of an HVAC model based on Heteroscedastic Evolutionary Bayesian Optimization (HEBO) is presented. Firstly, the HVAC model is built using the Modelica language in the Dymola environment. Secondly, real operation and weather data are fed to the HVAC model to predict the energy consumption of various equipment. Thirdly, the Root Mean Squared Error (RMSE) between predicted and real values is minimized by HEBO so that the optimal parameters of the equipment can be obtained. Finally, a case study is conducted to verify the effectiveness of the proposed method. The results show that the CV(RMSE) of calibrated models meets the guideline requirements of ASHRAE, IPMVP, and FEMP. © 2024 IEEE.

关键词： HVAC

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Forecasting models for Polish coal mining accidents 26

Forecasting models for Polish coal mining accidents

引用

26th international conference on methods and models in automation and robotics

作者： Kraszewska, Marta Kashpruk, Nataliia Baranowski, Jerzy Kapusta, Mariusz AGH Univ Sci & Technol Dept Automat Control & Robot Krakow Poland AGH Univ Sci & Technol Fac Civil Engn & Resource Management Krakow Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

In paper, forecasting models using exponential smoothing for Polish coal mining safety accidents are presented. Prior to this, data is analyzed and approach for building forecasting models in Tableau is described in details. Three forecasting models are revealed, respectively for all accidents in coal mines, fatal accidents and employment. Received results are promising and confidence intervals cover the predictions well. Improved forecast accuracy with presented models might provide coal mine enterprises more precise data, supporting safety management in those organizations.

关键词： safety accidents fatal accidents forecasting exponential smoothing time series coal mining

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A study of obstacle avoidance problem for robots based on basic line-circle combinations 3

A study of obstacle avoidance problem for robots based on ba...

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3rd international conference on robotics, automation and Intelligent Control, ICRAIC 2023

作者： Jiawen, Kang Northeastem University School of Mechanical Engineering and Automation Shenyang China

ISBN: (纸本)9798350360813

Aiming at the obstacle avoidance path optimization problem for a robot to reach a specified goal point in an area on the same plane, this paper analyzes different path optimization models, and at the same time divides the obstacles into two categories of vertices and no vertices, and divides them into different exclusion zones, and optimizes them according to the goals. The single-objective point shortest path model is divided into three models: no-goal zone vertex, turning radius, and turning center at the vertex shortest path model, and according to the path turning situation is divided into turning once and many times model, and at the same time, the shortest path time model is established and calculated. © 2023 IEEE.

关键词： Nonlinear programming

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An Innovative Approach to Glass Classification Using Deep Neural Network 3

An Innovative Approach to Glass Classification Using Deep Ne...

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3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shohanur Sultana, Tania Islam, Wahid Tausif Rifat Ahmed Khan, Md. Daffodil International University Department of CSE Dhaka Bangladesh American International University Bangladesh Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798331534400

A novel method for classifying glasses using deep neural networks is presented. The study uses data from the USA Forensic Science Service to classify six different varieties of glass according to their oxide level. The dataset is carefully cleaned up using preprocessing methods like Min-Max Scaling and Label Encoding to improve data quality and make it more neural network architecture compatible. The deep neural network model performs admirably in this difficult task, correctly categorizing glass samples with an accuracy of 81.40%. The analysis of the results shows that different varieties of glass behave differently, pointing out both areas of strength and improvement. In the future, the work may entail investigating more sophisticated deep learning techniques including transfer learning and attention processes, as well as broadening the dataset to include a greater range of glass samples. Moreover, validation tests conducted in the actual world and the model's implementation in industrial settings may offer important new perspectives on its usefulness. In general, this study advances automated glass categorization methods, which may have consequences for activities related to environmental sustainability, product creation, and quality assurance in a variety of sectors. © 2024 IEEE.

关键词： Data quality

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