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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Soiffer, Duncan Pyorre, Tuomas Kenney, David Teixeira, Andrew Worcester Polytechnic Institute Department of Computer Science and Mathematical Sciences WorcesterMA United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Chemical Engineering WorcesterMA United States

ISBN: (纸本)9798350308600

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis. © 2023 IEEE.

关键词： Machine learning

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Intelligent Motion Planning for Collision Free Autonomous Docking of Satellite Emulation Platform Using Reinforcement Learning

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IFAC-PapersOnLine 2023年第2期56卷 3354-3359页

作者： D. Athauda A. Banerjee S. Satpute A. Agha-Mohammadi G. Nikolakopoulos Department of Computer Science Electrical and Space Engineering Luleå University of Technology Robotics and AI group Department of Computer Science Electrical and Space Engineering Luleå University of Technology AI for Humanity Inc.

A reinforcement learning (RL) enabled intelligent motion planning for collision-free autonomous docking manoeuvre explicitly designed for a robotic floating satellite emulation platform is presented in this article. The Twin Delayed Deep Deterministic Policy Gradient-based RL algorithm involving deep neural network architecture in the actor-critic framework is considered to obtain the collision-free safe docking policy. The RL agents have been trained to perform a resilient target acquisition, ensuring its terminal position and velocity requirements while enabling the capability to avoid both static and dynamic obstacles. The resulting optimal policy is implemented as a feedback control law to enable computationally efficient onboard reactive motion planning for autonomous safe docking of the robotic floating satellite platform in a complex dense debris environment. The efficacy of the proposed motion planning scheme is validated with numerous simulation studies, where it is depicted that the trained RL-based planner has the potential to address the target acquisition with a sufficient degree of accuracy in the presence of both static and dynamic obstacle scenarios.

关键词： Reinforcement Learning Floating satellite platform Obstacle avoidance Intelligent motion planning Safe autonomous docking

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Analysis of Causal Factor of Driver Fatigue in the Driving in Different Driving Conditions

Analysis of Causal Factor of Driver Fatigue in the Driving i...

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International Joint Conference on Neural Networks (IJCNN)

作者： Masataka Adachi Sou Nobukawa Keiichiro Inagaki Department of Robotics Science and Engineering Chubu University Kasugai Japan Department of Computer Science Chiba Institute of Technology Narashino Japan Department of Artificial Intelligence and Robotics Chubu University Kasugai Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Addressing driving fatigue is crucial among the various factors in traffic accidents. The origins of driver fatigue encompass prolonged driving, sleep deprivation, and the induction of stress inherent in driving. The causes of such driver fatigue were categorized into physical factors, including sleep deprivation and prolonged driving, and perceptional-behavioral factors such as perception of driving conditions. Numerous studies have explored technologies to detect driver fatigue in terms of relevance to sleep states, eye movements, electroencephalography (EEG) activation levels, and their functional connectivity. It was also found differences in the occurrence of driver fatigue and related theta and alpha band activity based on driving conditions. However, the factors contributing to these differences remained unexplored. This study seeks to investigate the causal relationship between driver fatigue induced in different driving environments and the corresponding brain activity. We focus on perceptional load, particularly the perception of traffic objects within the driving condition, and physical loads, such as steering, acceleration, and braking, associated with variations in driving environments. In the experiment, we evaluate the relationship between such differences in physical and mental load and the occurrence of fatigue dependent on the driving condition with considering EEG activation patterns. In the results, we found that the amount of load related to driver perception and operation inherent in driving environments plays a crucial role in driver fatigue.

关键词： Sleep Human factors Fatigue Electroencephalography Biological neural networks Accidents

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Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

arXiv

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arXiv 2024年

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

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A∗ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Transformers for Image-Goal Navigation

arXiv

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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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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Mobile edge fog, Blockchain Networking and Computing-A survey 1

Mobile edge fog, Blockchain Networking and Computing-A surve...

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

作者： Salama, Ramiz Al-Turjman, Fadi Bhatla, Shruti Mishra, Divya 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 Department of Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computer science that have the potential to significantly impact the design and operation of distributed systems. MEC involves the deployment of computing resources and services at the edge of the network, closer to the end users, enabling lowlatency, high-bandwidth applications such as augmented reality and machine learning. Blockchain technology, on the other hand, is a distributed database that allows for secure, transparent, and immutable record-keeping, with potential applications in a wide range of industries. In this project, we explore the integration of MEC and blockchain technology for the management of distributed computing resources at the edge of the network. We review the state of the art in these two areas, including existing projects and prototypes, and describe our materials and method for evaluating their potential for integration. Our results show that the combination of MEC and blockchain has the potential to enable new applications and services that require both low latency and secure, decentralized data management. We conclude by summarizing the main contributions of our work and discussing future directions for research in this area. Our findings have implications for the design and operation of distributed systems, and have the potential to enable new applications and services in a wide range of industries. © 2023 IEEE.

关键词： Blockchain

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Direct Machine Translation Indonesian-Batak Toba 7

Direct Machine Translation Indonesian-Batak Toba

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Togatorop, Frengky Marulitua Ahmad, Imam Puspaningrum, Ajeng Savitri Universitas Lampung Doctoral Program of Mathematics and Natural Sciences Bandarlampung Indonesia Universitas Lampung Department of Computer Science Bandarlampung Indonesia Universitas Lampung Department of Mathematics Bandarlampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350307504

The translation application was developed using Python and Visual Studio Code as the primary programming environment. Technological progress has led to the younger generation forsaking the Batak language in their daily interactions, perceiving it as inconsequential to their social circle and contributing to the decline of regional languages. There is concern that regional languages, namely the Batak language, may lose significance or face extinction if this trend persists. This translation program is being implemented to aid in preserving regional languages, specifically the Batak language, enabling it to conform to contemporary circumstances. The development of this translation engine utilized the Batak language dictionary. The process of designing, coding, and testing is responsible for creating this direct machine translation. The BLEU score is employed to assess the caliber of machine translation. The study's findings indicate that the % BLEU value for ten sentences was 32.3%. This score falls within the category of sentences whose meaning can be understood, but the sentence structure still needs development. The direct machine translation developed in this Web platform is highly proficient and practical. © 2023 IEEE.

关键词： Python

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Voxel Map to Occupancy Map Conversion Using Free Space Projection for Efficient Map Representation for Aerial and Ground Robots

arXiv

引用

arXiv 2024年

作者： Fredriksson, Scott Saradagi, Akshit Nikolakopoulos, George The Robotics and AI group The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps for both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The generated 2D maps can be used for more efficient global navigation for both UAVs and UGVs, in enabling algorithms developed for 2D maps to be useful in 3D applications, and allowing for faster transfer of maps between multiple agents in bandwidth-limited scenarios. The proposed method uses the free space representation in the UFOMap mapping solution to generate 2D occupancy maps. During the 3D to 2D map conversion, the method conducts safety checks and eliminates free spaces in the map with dimensions (in the height axis) lower than the robot’s safety margins. This ensures that an aerial or ground robot can navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts the height of navigable free space and a local estimate of the slope of the floor from the 3D voxel map. The height data is utilized in converting paths generated using the 2D map into paths in 3D space for both UAVs and UGVs. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The effectiveness of the proposed method in enabling computationally efficient navigation for both aerial and ground robots is validated in two different environments, over both static maps and in online implementation in an exploration mission. The methods proposed within this article have been implemented in the popular robotics framework ROS and are open-sourced. The code is available at: https://***/LTURAI/Map-Conversion-3D-Voxel-Map-to-2D-Occupancy-Map. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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