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共 24666 条记录，以下是311-320 订阅

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GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control 39

GPU-Accelerated Policy Optimization via Batch Automatic Diff...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Taheri, Abdolreza Pajarinen, Joni Ghabcheloo, Reza Tampere Univ Fac Engn & Nat Sci Tampere Finland Aalto Univ Dept Elect Engn & Automat Espoo Finland Tech Univ Darmstadt Intelligent Autonomous Syst Darmstadt Germany HIAB Control Syst R&D Hudiksvall Sweden

ISBN: (纸本)9781728196817

The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed.

关键词： Training Monte Carlo methods Scalability Optimization methods Gaussian processes Search problems Sampling methods

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A LSTM-Based Trajectory Prediction Method For Aerial Target 4

A LSTM-Based Trajectory Prediction Method For Aerial Target

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Peng, Shuang Sun, Songtao Zhao, Gaopeng Yan, Quanmin Nanjing University of Science and Technology School of Automation Nanjing China Aerospace System Engineering Shanghai Shanghai China

ISBN: (纸本)9798331531225

The dynamic and accurate prediction of the target trajectory about UAVs is the key point of airspace management. For the task of air target trajectory prediction dominated by a variety of UAVs, traditional methods can not deal with conditions such as complex modeling, high computational cost, and poor model reusability. This paper proposes an air target trajectory prediction method based on sliding window Long Short-Term Memory (LSTM). The sliding window method is adopted to segment the target historical trajectory data. The LSTM can memorize historical information and effectively process long time series to predict the trajectory data of air targets at future moments. The prediction error comparison simulation experiment is carried out between the polynomial fitting model method and the method proposed, which proves that the proposed method improves the trajectory prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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Preliminary Study on the Rolling Locomotion of Variable Topology Truss Robot Using Dynamic Characteristics 2

Preliminary Study on the Rolling Locomotion of Variable Topo...

引用

2nd international conference on Mechatronics, Control and robotics, ICMCR 2024

作者： Kim, Hanbom Bae, Jangho Yim, Mark Seo, TaeWon Hanyang University Department of Mechanical Engineering Seoul04763 Korea Republic of University of Pennsylvania Department of Mechanical Engineering and Applied Mechanics PhiladelphiaPA19146 United States

ISBN: (纸本)9798350396096

This paper presents a preliminary study on the development of dynamic rolling locomotion for Variable Topology Truss (VTT) robots. Rolling locomotion allows the robot to move in any direction, which is advantageous for various terrains. However, they are constantly affected by the relatively slow speed and impact. We propose the 2D and 3D dynamic models with a contact force simulation. Based on the calculation, the simulation model could show the movement of VTT. © 2024 IEEE.

关键词： Dynamic models

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Data-Driven Control for a Milli-Scale Spiral-Type Magnetic Swimmer using MPC 39

Data-Driven Control for a Milli-Scale Spiral-Type Magnetic S...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhao, Haoran Lu, Yitong Becker, Aaron T. Leclerc, Julien Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA

ISBN: (纸本)9781728196817

This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9mm to 4:6mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods.

关键词： Adaptation models Solid modeling Magnetic multilayers Three-dimensional displays Computational modeling Photonic crystals Predictive models

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

引用

2023 international conference on robotics and automation in Industry, ICRAI 2023

作者： Ali, Asad Gul, Sarah Mahmood, Tallat Ullah, Anayat Control Automotive and Robotics Lab Rawalpindi Pakistan Balochistan University of IT Engineering and Management Sciences Department of Electrical Engineering Quetta Pakistan

ISBN: (纸本)9781665464727

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods. © 2023 IEEE.

关键词： Mobile robots

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Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes 20

Investigating Low Data, Confidence Aware Image Prediction on...

引用

IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Shinde, Nikhil U. Liang, Xiao Richter, Florian Herbert, Sylvia Yip, Michael C. Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350358513;9798350358520

The ability to predict future states is crucial to informed decision-making while interacting with dynamic environments. With cameras providing a prevalent and information-rich sensing modality, the problem of predicting future states from image sequences has garnered a lot of attention. Current state-of-the-art methods typically train large parametric models for their predictions. Though often able to predict with accuracy these models often fail to provide interpretable confidence metrics around their predictions. Additionally these methods are reliant on the availability of large training datasets to converge to useful solutions. In this paper, we focus on the problem of predicting future images of an image sequence with interpretable confidence bounds from very little training data. To approach this problem, we use non-parametric models to take a probabilistic approach to image prediction. We generate probability distributions over sequentially predicted images, and propagate uncertainty through time to generate a confidence metric for our predictions. Gaussian Processes are used for their data efficiency and ability to readily incorporate new training data online. Our method's predictions are evaluated on a smooth fluid simulation environment. We showcase the capabilities of our approach on real world data by predicting pedestrian flows and weather patterns from satellite imagery.

关键词： Prediction models

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Machine Learning-Driven Demand Prediction for Off-Grid PV-Integrated EV Charging Stations 3

Machine Learning-Driven Demand Prediction for Off-Grid PV-In...

引用

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

作者： Shib, Sowrov Komar Roy Dipto, Durjoy Rahman, Md Tanvir Rahman, Md Mostafizur Islam, Riadul Shufian, Abu American International University-Bangladesh Dept. of Electrical & Electronic Engg. Dhaka Bangladesh Lamar University Dept. of Management Information System TX United States Lamar University Dept. of Electrical & Electronic Engg. TX United States

ISBN: (纸本)9798331534400

The rapid growth of electric vehicles (EVs) has led to an increasing demand for charging infrastructure, particularly in off-grid locations. Developing off-grid EV charging stations powered by photovoltaic (PV) systems is crucial to support this expansion. However, the variability of solar power presents challenges in maintaining efficient and reliable charging services. This study focuses on developing off-grid EV charging stations and optimizing load demand prediction using machine learning models to enhance the operational efficiency of these charging stations. Various algorithms, including Random Forest, Decision Tree, SVM, Logistic Regression, and Naïve Bayes, were employed to model and predict load demand based on historical data and real-time inputs. The results indicate that the Random Forest and Decision Tree models achieved the highest accuracy, at 93% and 92%, respectively. The effectiveness of machine learning in dynamically adjusting power distribution according to predicted demand is highlighted, reducing reliance on traditional energy grids and supporting the sustainable development of EV charging solutions in remote areas. Integrating these predictive models allows for better management of energy resources, ensuring a consistent and cost-effective power supply and facilitating the broader adoption of renewable energy in EV infrastructure. © 2024 IEEE.

关键词： Charging stations

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INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USING AI TECHNOLOGY 2

INTELLIGENT ENHANCEMENT SOLUTIONS FOR INDUSTRIAL ROBOTS USIN...

引用

2nd international conference on Mechatronic automation and Electrical Engineering, ICMAEE 2024

作者： Wu, Di School of International Education Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9781837242672

The integration of artificial intelligence (AI) into industrial robotics has revolutionized manufacturing by improving various aspects such as precision, speed, and energy efficiency. This paper delves into AI-driven solutions aimed at enhancing the performance of industrial robots, focusing on the application of advanced algorithms, machine learning models, and AI frameworks. Through a comprehensive performance comparison between AI-enhanced robotic systems and traditional methods, this study evaluates several key performance indicators (KPIs), including accuracy, flexibility, operational efficiency, and sustainability. The results underscore AI's transformative role in increasing overall system performance, achieving faster processing times, reducing energy consumption, and enhancing adaptability in complex manufacturing environments. Additionally, this paper explores the implications of these AI advancements for industrial automation, highlighting areas for future research, such as the optimization of AI algorithms, scalability across diverse industries, and improved integration with existing robotic systems. These findings emphasize the long-term potential of AI to reshape industrial automation, driving forward innovation in smart manufacturing and fostering sustainable development across the sector. © The Institution of Engineering & Technology 2024.

关键词： Smart manufacturing

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A Data-Driven Multiple Model Framework for Intention Estimation 39

A Data-Driven Multiple Model Framework for Intention Estimat...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Qin, Yongming Kumon, Makoto Furukawa, Tomonari Univ Virginia VICTOR Lab Charlottesville VA 22903 USA Kumamoto Univ Fac Adv Sci & Technol 2-39-1KurokamiChuo Ku Kumamoto 8608555 Japan Kumamoto Univ Int Res Org Adv Sci & Technol 2-39-1KurokamiChuo Ku Kumamoto 8608555 Japan

ISBN: (纸本)9781728196817

This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorporates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing models over the conventional approach that assumes one-to-one relations.

关键词： Deep learning automation Probabilistic logic Numerical models Bayes methods High frequency State estimation

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Causal-based Time Series Domain Generalization for Vehicle Intention Prediction 39

Causal-based Time Series Domain Generalization for Vehicle I...

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IEEE international conference on robotics and automation (ICRA)

作者： Hu, Yeping Jia, Xiaogang Tomizuka, Masayoshi Zhan, Wei Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781728196817

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Therefore, generalization capability to unseen domains is crucial for prediction models when autonomous vehicles are deployed in the real world. In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed. We construct a structural causal model for vehicle intention prediction tasks to learn an invariant representation of input driving data for domain generalization. We further integrate a recurrent latent variable model into our structural causal model to better capture temporal latent dependencies from time-series input data. The effectiveness of our approach is evaluated via real-world driving data. We demonstrate that our proposed method has consistent improvement on prediction accuracy compared to other state-of-the-art domain generalization and behavior prediction methods.

关键词： automation Navigation Time series analysis Data visualization Predictive models Data models Behavioral sciences

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