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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

共 1211 条记录，以下是21-30 订阅

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Integrating Retrospective Framework in Multi-Robot Collaboration

Integrating Retrospective Framework in Multi-Robot Collabora...

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international conference on Autonomous Robots and Agents, ICARA

作者： Jiazhao Liang Hao Huang Yu Hao Geeta Chandra Raju Bethala Congcong Wen Yi Fang NYUAD Center for Artificial Intelligence and Robotics (CAIR) Abu Dhabi UAE Embodied AI and Robotics (AIR) Lab NYU Abu Dhabi UAE New York University Abu Dhabi UAE New York University US

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Recent advancements in Large Language models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve efficient collaboration and decision-making in dynamic and uncertain environments, which are common in real-world multi-robot scenarios. To address these challenges, we propose a novel retrospective actor-critic framework for multi-robot collaboration. this framework integrates two key components: (1) an actor that performs real-time decision-making based on observations and task directives, and (2) a critic that retrospectively evaluates the outcomes to provide feedback for continuous refinement, such that the proposed framework can adapt effectively to dynamic conditions. Extensive experiments conducted in simulated environments validate the effectiveness of our approach, demonstrating significant improvements in task performance and adaptability. this work offers a robust solution to persistent challenges in robotic collaboration.

关键词： Robot kinematics Large language models Decision making Collaboration Robustness Real-time systems Planning Multi-robot systems Resource management Tuning

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829598

the proceedings contain 104 papers. the special focus in this conference is on Computer Aided Systems theory. the topics include: Influence of Spike Encoding, Neuron models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for Learning Medicine through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine Learning methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine Learning Based Parameter Estimation of Energy models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning methods;machine Learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement Learning Algorithms in Production Systems;Using automationML for Advanced Simulation in Industrial automation;AR Digital Twin Demonstrator for Industrial robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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Field Oriented Control of BLDC Motors for Unmanned Underwater Vehicle (UUV)

Field Oriented Control of BLDC Motors for Unmanned Underwate...

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international conference on Autonomous Robots and Agents, ICARA

作者： Basavaraj S Talikoti Devindra M Chitguppa Rahul Kumar Mohan Karnik Automation Solutions PDIC Bharat Electronics Limited Bengaluru India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

this paper delves into the domain of Brushless Direct Current (BLDC) Motors that have become significantly popular in modern day applications like automotive, aerospace, robotics (unmanned vehicles) etc. because of the edge they have over their brushed counterpart. Unlike brushed DC motors, which use brush and commutator segments for commutation, BLDC motors use electronic commutation which eliminates wear and tear of mechanical parts, frictional loss etc. this paper discusses mathematical modelling of a BLDC motor, presents the insights about design and implementation of Field Oriented Control (FOC), initial calculation of Proportional Integral Differential (PID) gains and final implementation of FOC algorithm for BLDC Motors of UUV. this technique is far superior than other traditional methods, like Trapezoidal control and sinusoidal control, and implements independent control of torque and flux with the help of various transforms. It is a very useful and precise technique which results in a superior dynamic response, less torque ripple and high efficiency. the key objective is to dispense a holistic approach for designing PID controller for a BLDC motor. the performance of the controller was tested for no load and load condition. the final results demonstrated the anticipated superior dynamic response.

关键词： Dynamic response Motor drives Accuracy Heuristic algorithms Commutation Motors Real-time systems Mathematical models Tuning Robots

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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Zero-Shot Object Navigation with Vision-Language models Reasoning 27th

Zero-Shot Object Navigation with Vision-Language Models Reas...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Wen, Congcong Huang, Yisiyuan Huang, Hao Huang, Yanjia Yuan, Shuaihang Hao, Yu Lin, Hui Liu, Yu-Shen Fang, Yi Lab New York University Abu Dhabi Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi United Arab Emirates Tandon School of Engineering New York University New York United States University of Science and Technology of China Anhui China School of Software Tsinghua University Beijing China

ISBN: (纸本)9783031784552

Object navigation is crucial for robots, but traditional methods require substantial training data and cannot be generalized to unknown environments. Zero-shot object navigation (ZSON) aims to address this challenge, allowing robots to interact with unknown objects without specific training data. Language-driven zero-shot object navigation (L-ZSON) is an extension of ZSON that incorporates natural language instructions to guide robot navigation and interaction with objects. In this paper, we propose a novel Vision Language model with a Tree-of-thought Network (VLTNet) for L-ZSON. VLTNet comprises four main modules: vision language model understanding, semantic mapping, tree-of-thought reasoning and exploration, and goal identification. Among these modules, Tree-of-thought (ToT) reasoning and exploration module serves as a core component, innovatively using the ToT reasoning framework for navigation frontier selection during robot exploration. Compared to conventional frontier selection without reasoning, navigation using ToT reasoning involves multi-path reasoning processes and backtracking when necessary, enabling globally informed decision-making with higher accuracy. Experimental results on PASTURE and RobothOR benchmarks demonstrate the outstanding performance of our model in LZSON, particularly in scenarios involving complex natural language as target instructions. Videos are available at https://***/. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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LAV-ACT: Language-Augmented Visual Action Chunking with Transformers for Bimanual Robotic Manipulation

LAV-ACT: Language-Augmented Visual Action Chunking with Tran...

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international conference on Autonomous Robots and Agents, ICARA

作者： Dhurba Tripathi Chunting Liu Niraj Pudasaini Yu Hao Anthony Tzes Yi Fang NYU Embodied AI and Robotics Lab New York University Abu Dhabi Abu Dhabi UAE Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Bimanual robotic manipulation, involving the coordinated use of two robotic arms, is essential for tasks requiring complex, synchronous actions. Action Chunking with Transformers (ACT) is a representative framework that enables robots to break down complex tasks into manageable sequences, facilitating autonomous learning of multi-step actions. However, we observe limitations in the ACT framework: it relies solely on visual observations as input, focusing on task-specific action predictions, and it uses a simple ResNet-based feature extractor for image processing, which is often insufficient for complex and multi-view bimanual arm observations. In this paper, we introduce an enhanced language-augmented version of ACT that leverages Voltron-a language-driven representation model-to incorporate both visual observations and language prompts into dense, multi-modal embeddings. these embeddings are used to condition the ResNet backbone feature maps through Featurewise Linear Modulation (FiLM), allowing our model to integrate contextually relevant linguistic information with visual data for more adaptive action chunking. Extensive experiments show that our approach significantly improves the performance of bimanual robot arms in executing complex, multi-step tasks guided by language cues, outperforming traditional ACT methods. Project website: https://***/LAV-ACT/

关键词： Visualization Adaptation models Robot kinematics Modulation Linguistics Transformers Manipulators Feature extraction Data models Context modeling

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A Kinematic Simulation Strategy of a Compliant 4-Bar Linkage with Flexure Hinge Joint

A Kinematic Simulation Strategy of a Compliant 4-Bar Linkage...

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international conference on Autonomous Robots and Agents, ICARA

作者： Pengfei Wang Lailu Li Rui Wang Xin Guo Dongyu Li Shanghai Radio Equipment Research Institute Shanghai China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Compliant mechanisms have drawn a lot of attention in recent years. Discrete elastic rod theory has paved the way in designing and analyzing linkage mechanisms with flexure hinges (compliant linkage mechanism). In order to achieve a same kinematic as the rigid linkage mechanism, this paper gives a general model for the kinematic of a compliant linkage mechanism: crank and rocker mechanism: Firstly, modeling of a compliant linkage mechanism based on discrete elastic rod theory, taking a typical example of a cranker and rod mechanism; Secondly, kinematic modeling of the rigid cranker and rod mechansim; thirdly, optimization flow is introduced, including BFGS optimization method, cubic hermix spline method and other relevant settings; Lastly, the simulation results demonstrate that this model could be used in the analysis and design of the compliant linkage mechanism.

关键词： Couplings Analytical models Interpolation Manufacturing processes Simulation Optimization methods Kinematics Fasteners Splines (mathematics) Robots

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Edge AI to Edge robotics: Enhancing Human Pose Estimation with High-Performance TPU Computing 4th

Edge AI to Edge Robotics: Enhancing Human Pose Estimation wi...

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Sharma, Vijeta Mupenda, Didier Numbi thorvik, Lars Mishra, Deepti NTNU-Norwegian University of Science and Technology Gjøvik2815 Norway

ISBN: (纸本)9783031837821

the convergence of Edge AI and high-performance computing (HPC) is revolutionising intelligent systems by enabling real-time data processing and decision-making at the network’s edge. the demand for high-performance computing in resource-constrained environments opens opportunities for applications such as autonomous vehicles, smart cities, and industrial automation. this study presents the terminology that evidences the evolution of edge AI to the edge robotics landscape, and then we present the novel approach to leveraging Tensor Processing Units (TPUs) to enhance the inference capabilities of robotic systems at the edge. Using a Reachy robot as a case study, we demonstrate real-time human pose estimation powered by TPUs. the research investigates the intersection of robotics, Edge AI, and HPC, focusing on accelerating inference tasks with TPUs. We implemented deep learning-based human pose estimation models—PoseNet-ResNet50, PoseNet-MobileNet V1, MoveNet Lightning, and MoveNet thunder—on the Robot Operating System (ROS) and benchmarked their performance. Results show that MoveNet thunder achieved the fastest inference time (48 ms) with a pose score of 98%, while PoseNet-ResNet50 was the slowest (701 ms) with a score of 93%. Our findings highlight the significant improvements in inference speed and accuracy using TPUs over CPUs. We discuss the implications of TPU-accelerated AI in robotics and explore future research directions in this evolving field. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tensors

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Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinfo...

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international conference on Autonomous Robots and Agents, ICARA

作者： Xibo Li Shruti Patel Christof Büskens Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany Department for Autonomous Systems TOPAS Industriemathematik Innovation gGmbH Bremen Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware.

关键词： Training Trajectory planning Predictive models Deep reinforcement learning Prediction algorithms Planning Trajectory Logic Autonomous vehicles Robots

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