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arXiv 2024年

作者： Ouazghire, Mohamed Mourad Berrio, Julie Stephany Shan, Mao Worrall, Stewart Department of Computer Science Technical University of Darmstadt Germany The Australian Center For Robotics The University of Sydney Australia

Autonomous vehicles are being tested in diverse environments around the world. However, there is a notable gap in the evaluation of datasets representing natural unstructured environments such as forests or gardens. To address this, we present a study on localization at the Mount Annan Botanic Garden. This area encompasses open grassy areas, paved pathways, and densely vegetated sections with trees and other objects. The data set was recorded using a 128-beam LiDAR sensor and GPS and IMU readings to track the ego vehicle. This paper evaluates the performance of two state-of-the-art LiDAR-inertial odometry frameworks, COIN-LIO and LIO-SAM, on this dataset. We analyze trajectory estimates in both horizontal and vertical dimensions and assess relative translation and yaw errors over varying distances. Our findings reveal that while both frameworks perform adequately in the vertical plane, COIN-LIO demonstrates superior accuracy in the horizontal plane, particularly over extended trajectories. In contrast, LIO-SAM shows an increase in drift and yaw errors over longer distances. © 2024, CC BY-NC-ND.

关键词： Trajectories

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PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects

arXiv

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arXiv 2024年

作者： Chen, Guangcheng He, Yicheng He, Li Zhang, Hong Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Robotics and Computer Vision China

Neural implicit surface reconstruction has achieved remarkable progress recently. Despite resorting to complex radiance modeling, state-of-the-art methods still struggle with textureless and specular surfaces. Different from RGB images, polarization images can provide direct constraints on the azimuth angles of the surface normals. In this paper, we present PISR, a novel method that utilizes a geometrically accurate polarimetric loss to refine shape independently of appearance. In addition, PISR smooths surface normals in image space to eliminate severe shape distortions and leverages the hash-grid-based neural signed distance function to accelerate the reconstruction. Experimental results demonstrate that PISR achieves higher accuracy and robustness, with an L1 Chamfer distance of 0.5 mm and an F-score of 99.5% at 1 mm, while converging 4 ∼ 30× faster than previous polarimetric surface reconstruction methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Surface reconstruction

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Variable-Speed Dubins Orienteering Problem

Variable-Speed Dubins Orienteering Problem

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IEEE Latin American robotics Symposium, LARS

作者： L. C. Vinícius Faria C. G. Caio Ribeiro Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

The class of vehicle routing problems plays an important role in optimizing logistics and transportation strategies across various sectors. In this context, the Orienteering Problem (OP) seeks to determine an optimized path through a set of locations with associated rewards, aiming to maximize the total collected reward while respecting constraints such as time or distance limits. Nonholonomic motion planning, particularly considering Dubins vehicles, is crucial in real-world applications due to its ability to accurately capture the constraints and dynamics of many physical vehicles. In this paper, we extend the Dubins Orienteering Problem (DOP), where unlike the classical Dubins model with constant speed, the vehicle can vary its speed during navigation, enabling the discovery of more effective paths. Experiments comparing both the DOP and our novel formulation show that the VS-DOP achieves higher total collected rewards within the same budget compared to the conventional DOP.

关键词： Navigation Vehicle routing Dynamics Transportation Planning Time factors Vehicle dynamics Robots Logistics

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RoboCAP: Robotic Classification and Precision Pouring of Diverse Liquids and Granular Media with Capacitive Sensing

arXiv

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arXiv 2024年

作者： Hu, Yexin Gillespie, Alexandra Padmanabha, Akhil Puthuveetil, Kavya Lewis, Wesley Khokar, Karan Erickson, Zackory The Robotics Institute Carnegie Mellon University United States Department of Computer Science Colby College United States Department of Computer Science University of Virginia United States

Liquids and granular media are pervasive throughout human environments, yet remain particularly challenging for robots to sense and manipulate precisely. In this work, we present a systematic approach at integrating capacitive sensing within robotic end effectors to enable robust sensing and precise manipulation of liquids and granular media. We introduce the paralleljaw RoboCAP Gripper with embedded capacitive sensing arrays that enable a robot to directly sense the materials and dynamics of liquids inside of diverse containers, including some visually opaque. When coupled with model-based control, we demonstrate that the proposed system enables a robotic manipulator to achieve state-of-the-art precision pouring accuracy for a range of substances with varying dynamics properties. Code, designs, and build details are available on the project website. Copyright © 2024, The Authors. All rights reserved.

关键词： Granular materials

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Autonomous Sensor Exchange and Calibration for Cornstalk Nitrate Monitoring Robot

arXiv

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arXiv 2024年

作者： Lee, Janice Seungyeon Detlefsen, Thomas Lawande, Shara Ghatge, Saudamini Shanthi, Shrudhi Ramesh Mukkamala, Sruthi Kantor, George Kroemer, Oliver Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

Interactive sensors are an important component of robotic systems but often require manual replacement due to wear and tear. Automating this process can enhance system autonomy and facilitate long-term deployment. We developed an autonomous sensor exchange and calibration system for an agriculture crop monitoring robot that inserts a nitrate sensor into cornstalks. A novel gripper and replacement mechanism, featuring a reliable funneling design, were developed to enable efficient and reliable sensor exchanges. To maintain consistent nitrate sensor measurement, an on-board sensor calibration station was integrated to provide in-field sensor cleaning and calibration. The system was deployed at the Ames Curtis Farm in June 2024, where it successfully inserted nitrate sensors with high accuracy into 30 cornstalks with a 77% success rate. © 2024, CC BY-SA.

关键词： Agricultural robots

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D-MARL: A Dynamic Communication-Based Action Space Enhancement for Multi Agent Reinforcement Learning Exploration of Large Scale Unknown Environments

D-MARL: A Dynamic Communication-Based Action Space Enhanceme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gabriele Calzolari Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. In general, communication between autonomous systems is crucial when exploring large and unstructured environments. In such real-world scenarios, data transmission is limited and relies heavily on inter-agent proximity and the attributes of the autonomous platforms. In the proposed approach, each agent’s policy is optimized by utilizing the heterogeneous-agent proximal policy optimization algorithm to autonomously choose whether to communicate or explore the environment. To accomplish this, multiple novel reward functions are formulated by integrating inter-agent communication and exploration. The investigated approach aims to increase efficiency and robustness in the mapping process, minimize exploration overlap, and prevent agent collisions. The D-MARL policies trained on different reward functions have been compared to understand the effect of different reward terms on the collaborative attitude of the homogeneous agents. Finally, multiple simulation results are provided to prove the efficacy of the proposed scheme.

关键词： Autonomous systems Simulation Merging Employment Collaboration Reinforcement learning Robustness Data communication Optimization Intelligent robots

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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Adaptive Deep Reinforcement Learning for Robotic Manipulation in Dynamic Environments

Adaptive Deep Reinforcement Learning for Robotic Manipulatio...

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Data science and Network Security (ICDSNS), International Conference on

作者： Priyanka Avhad Gaddam Prathik Kumar Amogha Thalihalla Sunil Monesh Rallapalli Brahmadevu Hritesh Kumar Vinod Kumar Computer Science Engineering VJTI Mumbai India Computer Science The George Washington University Washington DC USA Robotics University of Maryland College Park USA Computer Science Engineering JAIN University Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Robotic manipulation tasks in dynamic and unstructured environments present significant challenges for traditional control procedures. Deep reinforcement learning (DRL) has appeared as a powerful technique to enable robots to learn complex manipulation tasks. However, the application of DRL in rapidly changing environments introduces additional complexities, such as ensuring adaptability and robustness in the face of shifting conditions. The framework combines DRL with adaptive learning strategies, allowing robots to continuously adjust their policies based on real-time environmental & response changes. The approach leverages advanced state and reward adaptation techniques and model ensembles to enhance robustness. Through extensive experimentation, the efficacy of the adaptive DRL method is established in achieving higher performance and generalization in various manipulation tasks. These results highlight the potential of adaptive DRL to enable robots to perform manipulation tasks more reliably and efficiently, tiling the technique for ampler flexible & capable robotic systems.

关键词： Adaptive learning Adaptive systems Network security Deep reinforcement learning Robustness Real-time systems Iterative methods Robots Manipulator dynamics Optimization

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Deep Multimodal Classification of Musical Genres

Deep Multimodal Classification of Musical Genres

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IEEE Southeastcon

作者： Bassem Jandoubi Moulay A. Akhloufi Dept. of Computer Science Robotics and Intelligent Machines (PRIME) Université de Moncton Moncton NB Canada

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Music genre classification is a challenging problem due to the subjective and complex nature of music perception. This project explores three complementary approaches to genre classification: audio-based classification using Mel-spectrograms and Convolutional Neural Networks (CNN), lyrics-based classification using RoBERTa for natural language processing, and a multimodal classification approach that integrates both modalities. The audio-based approach achieved remarkable performance improvements through augmentation techniques, while the lyrics-based model demonstrated effective text comprehension using pre-trained transformers. Finally, a multimodal approach that combines Mel-spectrograms and lyrics through concatenation achieved the highest accuracy, underscoring the advantages of integrating diverse data modalities. These results demonstrate the potential of multimodal deep learning for complex classification tasks, opening the way to improved music retrieval systems.

关键词： Deep learning Accuracy Music Transformers Natural language processing Convolutional neural networks

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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