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检索条件"主题词=Robotics and automation"

共 11731 条记录，以下是101-110 订阅

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VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference

VERSE: Virtual-Gradient Aware Streaming Lifelong Learning wi...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Soumya Banerjee Vinay K. Verma Avideep Mukherjee Deepak Gupta Vinay P. Namboodiri Piyush Rai IIT Kanpur India Amazon India University Of Bath UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Lifelong learning or continual learning is the problem of training an AI agent continuously while also preventing it from forgetting its previously acquired knowledge. Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning in a dynamic non-stationary environment without forgetting. We introduce a novel approach to lifelong learning, which is streaming (observes each training example only once), requires a single pass over the data, can learn in a class-incremental manner, and can be evaluated on-the-fly (anytime inference). To accomplish these, we propose a novel virtual gradients based approach for continual representation learning which adapts to each new example while also generalizing well on past data to prevent catastrophic forgetting. Our approach also leverages an exponential-moving-average-based semantic memory to further enhance performance. Experiments on diverse datasets with temporally correlated observations demonstrate our method’s efficacy and superior performance over existing methods.

关键词： Training Representation learning Continuing education Semantics Task analysis robotics and automation Artificial intelligence

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Boundary Factors for Seamless State Estimation between Autonomous Underwater Docking Phases

Boundary Factors for Seamless State Estimation between Auton...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Aldo Terán Espinoza Antonio Terán Espinoza John Folkesson Peter Sigray Jakob Kuttenkeuler

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Autonomous underwater docking is of the utmost importance for expanding the capabilities of Autonomous Underwater Vehicles (AUVs). Due to a historical focus on underwater docking to only static targets, the research gap in underwater docking to dynamically active targets has been left relatively untouched. We address the state estimation problem that arises when trying to rendezvous a chaser AUV with a dynamic target by modeling the scenario as a factor graph optimization-based Simultaneous Localization and Mapping problem. We present a set of boundary factors that aid the inference process by seamlessly transitioning the target’s state between the different observability stages, intrinsic to any dynamic docking scenario. We benchmark the performance of our approach using the Stonefish simulated environment.

关键词： Autonomous underwater vehicles Simultaneous localization and mapping Benchmark testing Vehicle dynamics State estimation robotics and automation Observability

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Cycle Time Range Determination based on Minimum Difference Method and Weighted Score Method

Cycle Time Range Determination based on Minimum Difference M...

引用

IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Xiaomao Xia Haomiao Li Bosch Automotive Products (Changsha) Co. Ltd. Changsha China Jacobs Technion-Cornell Institute Cornell University New York United States

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

This study presents two innovative methods for determining the cycle time range in industrial production lines: the Minimum Difference Method (MDM) and the Weighted Score Method (WSM). These methods are designed to enhance accuracy in establishing cycle time ranges, crucial for optimizing production efficiency. MDM focuses on minimizing the variance between upper and lower cycle time limits, facilitating better process control and predictability. WSM, on the other hand, integrates multiple parameters, including density and falling probability, to derive a more comprehensive and tailored cycle time range. The effectiveness of both methods is compared and analyzed in diverse industrial settings. Results indicate that accurate cycle time range determination is pivotal in streamlining production planning and resource allocation, significantly impacting process efficiency and cost-effectiveness. The study contributes to the field by offering a detailed comparative analysis of these methods, providing a practical framework for industries to enhance their production line efficiency.

关键词： Industries Hands Accuracy Mechatronics Production planning Process control Production Resource management robotics and automation Weighted sum model

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From Detection to Diagnosis: Elevating Track Fault Identification with Transfer Learning

From Detection to Diagnosis: Elevating Track Fault Identific...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Sahil Jatoi Bushra Abro Noorulain Mushtaq Ali Akbar Shah Syed Sanam Narejo Mahaveer Rathi Nida Maryam Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Electronic Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Research Scientist Dublin City University Dublin Ireland Department of Computer Science Government College University Hyderabad Sindh Pakistan Department of Systems Engineering PIEAS University Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a new application of transfer learning (TL) in improving the accuracy of track fault detection. Due to the diverse and complex nature of track faults it is hard to classify. There are various types of track faults such as Cracks, Flakings, Grooves, Joints, Shellings, Spallings, Squats. Classification of track fault could not be easily achieved using any simple machine learning (ML) algorithm. In this regard, this study uses pretrained models that are tuned to fit the peculiar nature of track fault. After acquiring track fault data from a widely cited repository of Mendeley from a research work already represented in ‘Data in Brief’. This application of TL achieves an unbeaten accuracy of 92%, which surpasses most previously generalized models with some explicit data augmentation strategies.

关键词： Accuracy Fault detection Transfer learning Wheels Fault location Data augmentation Data models Rail transportation Real-time systems robotics and automation

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Diagnosability of the Lexicographic Product of Paths and Paths under PMC Model

Diagnosability of the Lexicographic Product of Paths and Pat...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Bu Chen Feng Li Computer College Qinghai Normal University Xi'Ning China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

In a multiprocessor system, if the number of faulty processors is less than t, and the system can identify all faulty processors directly without any exchanging, then the system’s diagnosability is t. The multiprocessor system’s interconnection network plays a crucial role in determining it’s diagnosability. Evaluating the diagnosability of multiprocessor systems necessitates a comprehensive analysis of their interconnection networks. As the size of multiprocessor systems’ interconnection networks continues to increase, any processor failures or physical connection failures can significantly impact the systems’ stability and efficiency. This paper focuses on studying the networks obtained by lexicographic product of paths and paths, and obtains its diagnosability as well as its h-edge tolerable diagnosability under the PMC model.

关键词： Fault diagnosis Program processors Costs Multiprocessor interconnection Stability analysis Security robotics and automation Mechanical engineering Multiprocessing systems

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Impact of Different Parameters on Symmetry and Size of Single Step Anodization Nanopores in Porous Alumina

Impact of Different Parameters on Symmetry and Size of Singl...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Nabeela Noor Shah Haider Zaki Uddin Iqra Ashfaq Department of Physics University of Peshawar Peshawar Department of Physics Frontier Degree College for Women Peshawar College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Nanoporous Alumina is used widespread as a template material for nano scaled objects since mid-20th century. These nano scaled objects are in the form of nanowires, nanotubes and nanodots etc. In order to synthesize nanoporous alumina, two step anodization techniques has been reported. We have prepared porous alumina with the distinction that arrays of pores in alumina were created by single step anodization. We fabricate nanoporous alumina in 0.3 M oxalic acid for various anodization times at $2^{\circ}\mathrm{C}$ and $23^{\circ}\mathrm{C}$ under constant voltage of 40 v. EDX graphs revealed that oxide layer of aluminum was formed on the metal surface of aluminum. SEM micrographs however, depicted a good estimate of pore symmetry under low temperature and resulted in small pores as compared to room temperature. Longer anodization time deepens the pores' depth, whereas at room temperature the acid invaded pores' wall. SEM images made the evident that the sample anodized sporadically has lower degree of pore symmetry than continuously anodized sample. It was also observed that at voltage other than self-ordering i.e. at 60 V, hexagonal symmetry of pores decreases.

关键词： Temperature Aluminum Voltage Ions Etching Nanopores Current density robotics and automation Photomicrography Standards

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Multi-UAV 3D Hierarchical Coverage Path Planning in Dynamic Environments for Large and Complex Structures

Multi-UAV 3D Hierarchical Coverage Path Planning in Dynamic ...

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IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Pranav P Dave C Neha Krishna Rakshithvihaan P Badiger N Neha Shikha Tripathi D Sethuram Department of Electronics and Communication Engineering PES University Bangalore India Department of Mechanical Engineering PES University Bangalore India

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

In the field of structural inspection methodologies, Unmanned Aerial Vehicles have emerged as a suitable alternative to conventional inspections led by technicians due to their various advantages. However, suboptimal paths can pose risks to the structure under inspection, personnel, and nearby equipment. This paper addresses the challenge of path planning in dynamic environments, where people and equipment move near the structure during drone inspections, with a focus on minimizing inspection time, a hierarchical approach is proposed, combining Reinforcement Learning (RL) for the Vehicle Routing Problem (VRP) and OR Tools for the Travelling Salesman Problem (TSP). This method leverages Deep Reinforcement Learning (DRL) to adapt to various scenarios by adjusting environmental parameters, ensuring efficiency through parallelizability. The approach shows an average performance gap of 4.82% compared to Google Operations Research (OR) Tools in static settings and is evaluated for adaptability through experiments in various practical scenarios.

关键词： Three-dimensional displays Operations research Mechatronics Vehicle routing Inspection Autonomous aerial vehicles Path planning Personnel Vehicle dynamics robotics and automation

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Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

Safety-Critical Scenario Generation Via Reinforcement Learni...

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IEEE International Conference on robotics and automation (ICRA)

作者： Haolan Liu Liangjun Zhang Siva Kumar Sastry Hari Jishen Zhao University of California La Jolla California Baidu Research Sunnyvale California NVIDIA Corporation Santa Clara California

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches.

关键词： Training Pedestrians Perturbation methods Trajectory Safety Scenario generation robotics and automation

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When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning

When to Replan? An Adaptive Replanning Strategy for Autonomo...

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IEEE International Conference on robotics and automation (ICRA)

作者： Kohei Honda Ryo Yonetani Mai Nishimura Tadashi Kozuno Department of Mechanical Systems Engineering Nagoya University Furo-Cho Chikusa-Ku Nagoya Aichi Japan OMRON SINIC X Corporation Hongo Bunkyo-Ku Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path from the current to goal locations based on the pre-built map, the local planner produces a kinodynamic trajectory to follow the reference path while avoiding perceived obstacles. To account for unforeseen or dynamic obstacles not present on the pre-built map, "when to replan" the reference path is critical for the success of safe and efficient navigation. However, determining the ideal timing to execute replanning in such partially unknown environments still remains an open question. In this work, we first conduct an extensive simulation experiment to compare several common replanning strategies and confirm that effective strategies are highly dependent on the environment as well as the global and local planners. Based on this insight, we then derive a new adaptive replanning strategy based on deep reinforcement learning, which can learn from experience to decide appropriate replanning timings in the given environment and planning setups. Our experimental results show that the proposed replanner can perform on par or even better than the current best-performing strategies in multiple situations regarding navigation robustness and efficiency.

关键词： Navigation Deep reinforcement learning Robustness Timing Trajectory Planning robotics and automation

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Multi-agent Task Decomposition and Resource Scheduling Algorithm Construction

Multi-agent Task Decomposition and Resource Scheduling Algor...

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IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Hang Yi Ruliang Lin Hao Wang Xinyang Liu Beijing Aerospace Wanyuan Technology Co. Ltd Beijing China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

In the context of ubiquitous connectivity and collaborative multi device applications, the functionality of physical device resources is decomposed into discrete atomic models to enhance the flexibility and reusability of device cluster services. In current practical applications, assembling and efficiently scheduling discrete resources in a task-oriented manner has become an urgent issue. Therefore, we propose a task decomposition algorithm based on the capability model, which splits and matches tasks based on the resource capability model. Then, according to the proposed bitmap-based hybrid resource scheduling algorithm, we achieve fine-grained and efficient collaborative scheduling of service-oriented resources.

关键词： Mechatronics Scheduling algorithms Collaboration Clustering algorithms Scheduling robotics and automation Context modeling

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