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

共 24622 条记录，以下是271-280 订阅

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

引用

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

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Context is Everything: Implicit Identification for Dynamics Adaptation 39

Context is Everything: Implicit Identification for Dynamics ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Evans, Ben Thankaraj, Abitha Pinto, Lerrel NYU New York NY 10003 USA

ISBN: (纸本)9781728196817

Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be precisely measured or inferred, even during training. We propose Implicit Identification for Dynamics Adaptation (IIDA), a simple method to allow predictive models to adapt to changing environment dynamics. IIDA assumes no access to the true variations in the world and instead implicitly infers properties of the environment from a small amount of contextual data. We demonstrate IIDA's ability to perform well in unseen environments through a suite of simulated experiments on MuJoCo environments and a real robot dynamic sliding task. In general, IIDA significantly reduces model error and results in higher task performance over commonly used methods. Our code, video of the method, and latest paper is available here https://***/icra-iida/

关键词： Training Adaptation models Codes automation Predictive models Task analysis Robots

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Comparative Analysis of Autonomous Indoor Exploration Strategies: Floodfill algorithm vs. Frontier-Based Method 24

Comparative Analysis of Autonomous Indoor Exploration Strate...

引用

24th international conference on Control, automation and Systems

作者： Naufaldo Wu, Hsiu-Ming Natl Taipei Univ Technol Grad Inst Mechatron Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Dept Intelligent Automat Engn Taipei 10608 Taiwan

ISBN: (纸本)9798331517939;9788993215380

This paper investigates the efficiency of autonomous indoor exploration utilizing simulation testing environments in Gazebo. Two exploration methods, Floodfill algorithm and Frontier-based algorithm, using the 2D LiDAR sensor are compared. The Floodfill algorithm employs a systematic traversal approach, while the Frontier-based method dynamically detects and navigates towards frontiers. Results indicate that the Frontier-based approach outperforms Floodfill Algorithm in terms of efficiency and map completeness, particularly in complex environments. The study underscores the importance of the Frontier-based strategy for autonomous indoor exploration and paves the way for enhanced robotic applications in diverse domains.

关键词： Autonomous exploration Indoor robotics LiDAR technology Floodfill algorithm Frontier-based exploration

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An Effective Method for Creating Virtual Doors and Borders to Prevent Autonomous Mobile Robots from Entering Restricted Areas 9

An Effective Method for Creating Virtual Doors and Borders t...

引用

9th international conference on automation, robotics and Applications (ICARA)

作者： Omer, Karameldeen Monteriu, Andrea Univ Politecn Marche Dept Informat Engn Via Brecce Bianche 12 I-60131 Ancona Italy

ISBN: (纸本)9781665489218

Autonomous mobile robotics are becoming a common sight in smart homes and industries, where they are used for tasks such as cleaning and material handling. In some cases, it is necessary to restrict the movement of these robots to certain areas or rooms during specific times or for specific tasks, in order to ensure safety and prevent interference. Traditional methods for creating virtual walls and borders to accomplish this often have limitations in terms of ease of use, universality, and remote accessibility. In order to overcome these issues, this work presents a method for creating virtual walls, doors, and borders using the Robotic Operating System (ROS) and point cloud data. This method can be used to restrict the movement of autonomous mobile robots in human-centered environments, including smart homes and warehouses, and can be activated and deactivated remotely. It is a flexible and cost-effective solution that does not require specialized sensors and can be used with single or multi-robot systems. The effectiveness of our method has been demonstrated through testing in simulation based on the reconstruction of real environments.

关键词： virtual walls virtual doors virtual borders human-centered environments no-go zones AAL robotics warehouse robotics logistics robots

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Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning 17

Day-Ahead Forecasting for the Tropics with Numerical Weather...

引用

17th international conference on Control, automation, robotics and Vision (ICARCV)

作者： Ng, Nigel Yuan Yun Gopalan, Harish Raghavan, Venugopalan S. G. Ooi, Chin Chun Natl Univ Singapore Dept Phys Singapore Singapore Inst High Performance Comp Dept Fluid Dynam Singapore Singapore Ctr Frontier AI Res Singapore Singapore

ISBN: (纸本)9781665476874

Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models, each with multiple possible parameterizations. In this study, a hybrid numerical-statistical approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then post-processed using ML to provide a final prediction. Normalized root-mean-square error from WRF is reduced by up to 40-50% with this ML error correction model. Results obtained using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after this correction, negating the need for WRF parameterization tuning. Other models incorporating nearby locations and an ensemble set-up are also evaluated, although they produced much smaller improvements.

关键词： Solar forecasting Numerical weather prediction Machine learning Regression methods Radiation schemes

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Robust Imaging Sonar-based Place Recognition and Localization in Underwater Environments

Robust Imaging Sonar-based Place Recognition and Localizatio...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kiml, Hogyun Kang, Gilhwan Jeong, Seokhwan Ma, Seungjun Cho, Younggun Inha Univ Dept Elect & Comp Engn Incheon South Korea

ISBN: (纸本)9798350323658

Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping (SLAM) in underwater environments. This paper proposes a robust and efficient imaging SONAR-based place recognition, SONAR context, and loop closure method. Unlike previous methods, our approach encodes geometric information based on the characteristics of raw SONAR measurements without prior knowledge or training. We also design a hierarchical searching procedure for fast retrieval of candidate SONAR frames and apply adaptive shifting and padding to achieve robust matching on rotation and translation changes. In addition, we can derive the initial pose through adaptive shifting and apply it to the iterative closest point (ICP)based loop closure factor. We evaluate the SONAR context's performance in the various underwater sequences such as simulated open water, real water tank, and real underwater environments. The proposed approach shows the robustness and improvements of place recognition on various datasets and evaluation metrics. Supplementary materials are available at https://***/sparolab/sonar_***.

关键词： Iterative methods

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Contingency-Aware Task Assignment and Scheduling for Human-Robot Teams

Contingency-Aware Task Assignment and Scheduling for Human-R...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Dhanaraj, Neel Narayan, Santosh, V Nikolaidis, Stefanos Gupta, Satyandra K. Univ Southern Calif Viterbi Sch Engn Los Angeles CA 90007 USA

ISBN: (纸本)9798350323658

We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the human-robot team to handle uncertainty due to changing task requirements, potential failures, and delays to maintain task completion efficiency. We make two contributions: (1) we account for the complex interaction of uncertainty that stems from the tasks and the agents using a multi-agent concurrent MDP framework, and (2) we use Mixed Integer Linear Programs and contingency sampling to approximate action values for task assignment. Our results show that our online algorithm is computationally efficient while making optimal task assignments compared to a value iteration baseline. We evaluate our method on a 24-task representative assembly and a real-world 60-task satellite assembly, and we show that we can find an assignment that results in a near-optimal makespan.

关键词： Iterative methods

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Computational methods to Support Prototyping of an Adaptive Robot Joystick Controller for Children with Upper Limb Impairments

Computational Methods to Support Prototyping of an Adaptive ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Jouaiti, Melanie Azizi, Negin Dautenhahn, Kerstin Univ Waterloo Dept Elect & Comp Engn Waterloo N2L3G5 ON Canada

ISBN: (纸本)9798350323658

Between 2% to 5% of children are affected by Developmental Coordination Disorders in Canada and have been diagnosed with upper limb impairments, which affect their daily lives and reduces their autonomy. Motor impairments can be part of progressive disorders, so despite regular therapy, progress remains fleeting. Affected individuals therefore consistently face many barriers, including entertainment opportunities, as availability of off-the-shelf inclusive technology is very limited. Our long-term goal is to develop a play-mediator robot, which would facilitate play between children with motor impairments and their peers or family members. Here, games that the robot can play are remotely controlled by the participants, using appropriate interfaces (e.g. joysticks). In this paper, we take the first step towards that goal and develop an adaptive joystick controller that can compensate for individual deficits. We monitor movement statistics to determine if re-calibration of the controller is necessary. Moreover, we propose a computational model of data 'distortion', as a tool for developers to test their technology in the very early stages of prototype development, without requiring access to participants. This work is validated with data from healthy adults and children with upper limb impairments.

关键词： Computational methods

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Experimental Validation of Functional Iterative Learning Control on a One-Link Flexible Arm

Experimental Validation of Functional Iterative Learning Con...

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IEEE international conference on robotics and automation (ICRA)

作者： Drost, Sjoerd Pustina, Pietro Angelini, Franco De Luca, Alessandro Smit, Gerwin Della Santina, Cosimo Delft Univ Technol Dept Cognit Robot Delft Netherlands Sapienza Univ Rome Dept Comp Control & Management Engn Rome Italy Univ Pisa Res Ctr Enrico Piaggio Pisa Italy Univ Pisa Dept Informat Engn Pisa Italy German Aerosp Ctr DLR Inst Robot & Mechatron Obelpfaffenhofen Germany

ISBN: (纸本)9798350323658

Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed up the process. Functional iterative learning control is a novel technique that promises to solve several limitations of classic ILC. It operates by merging the input space into a large functional space, resulting in an over-determined control task in the iteration domain. In this way, it can deal with systems having more outputs than inputs and accelerate the learning process without resorting to model discretizations. However, the framework lacks so far a validation in experiments. This paper aims to provide such experimental validation in the context of robotics. To this end, we designed and built a one-link flexible arm that is actuated by a stepper motor, which makes the development of an accurate model more challenging and the validation closer to the industrial practice. We provide multiple experimental results across several conditions, proving the feasibility of the method in practice.

关键词： Iterative methods

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Sequence-Agnostic Multi-Object Navigation

Sequence-Agnostic Multi-Object Navigation

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IEEE international conference on robotics and automation (ICRA)

作者： Gireesh, Nandiraju Agrawal, Ayush Datta, Ahana Banerjee, Snehasis Sridharan, Mohan Bhowmick, Brojeshwar Krishna, Madhava IIIT Hyderabad Robot Res Ctr Hyderabad Telangana India Tata Consultancy Serv TCS Res Mumbai Maharashtra India Univ Birmingham Intelligent Robot Lab Birmingham England

ISBN: (纸本)9798350323658

The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for MultiON have viewed this as a direct extension of Object Navigation (ON), the task of localising an instance of one object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. This paper describes a deep reinforcement learning framework for sequence-agnostic MultiON based on an actor-critic architecture and a suitable reward specification. Our framework leverages past experiences and seeks to reward progress toward individual as well as multiple target object classes. We use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state of the art ON method extended to MultiON.

关键词： Deep reinforcement learning Multi-object navigation Assistive robot Cognitive robotics

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