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Multi-Agent and Multi-Target Reinforcement Learning for Satellite sensor Tasking

Multi-Agent and Multi-Target Reinforcement Learning for Sate...

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IEEE Aerospace conference

作者： Saeed, Amir K. Holguin, Francisco Yasin, Alhassan S. Johnson, Benjamin A. Rodriguez, Benjamin M. Johns Hopkins Univ Engn Profess Laurel MD 20723 USA

ISBN: (纸本)9798350304626

Advancements in machine learning and artificial intelligence have found broad applications across various domains. However, their integration into space-related scenarios, especially satellite tasking, has been relatively limited. As satellite architectures shift from a few high-end systems to constellations of simpler, more numerous spacecraft, dynamically tasking payload sensors becomes intricate. This complexity underscores the importance of researching effective sensor tasking methods, a challenge ideally suited for Reinforcement Learning (RL). This study expands upon prior work in RL for Low Earth Orbit (LEO) satellite Hyper Spectral Imaging (HSI) sensor tasking, broadening its scope to encompass multi-agent, multi-target scenarios. To enable the effective use of RL in multi-agent systems, we propose an enhanced Python-based modeling and simulation environment. This framework facilitates the development and assessment of coordination and decision-making processes among multiple independent satellites acting as agents in the LEO domain. Through the incorporation of multi-agent reinforcement learning (MARL) methodologies, the framework fosters collaboration and communication among satellites while optimizing their individual tasking goals. Additionally, we extend the RL framework to support the simultaneous tracking of multiple targets. This involves adapting existing algorithms to accommodate multi-target reinforcement learning (MTRL) strategies, where each satellite agent dynamically allocates its sensing resources to diverse targets based on relevance and importance. This work showcases a computationally efficient extension from a single-agent, single-target approach to a multi-agent, multi-target context. We outline steps to streamline the multi-target association into a single-target context, thus expanding RL capabilities with minimal added computational complexity.

关键词： reinforcement learning multi-agent multitarget intelligent systems satellite tasking machine learning aerospace autonomous systems

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Cluster Head Selection Algorithm using Machine Learning 4

Cluster Head Selection Algorithm using Machine Learning

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nidhya, M.S. Gurumoorthi, E. Babu, M. Charumathi, A.C. Naved, Mohd Maram, Balajee School of Computer Applications Dayananda Sagar University Bangalore India School of Engineering Malla Reddy University India Chennai Institute of Technology College Dept of Ai & Ds Chennai India Sona College of Technology Dept of Information Technology India Jaipuria Institute of Management Dept of Business Analytics Noida India School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331523923

Wireless sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.

关键词： Self-supervised learning

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3D Mapping and Landing Zone Identification in Complex Terrains Using DSM and Photogrammetry 8

3D Mapping and Landing Zone Identification in Complex Terrai...

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IEEE 8th International conference on Signal and Image Processing applications (IEEE ICSIPA)

作者： Subramanian, Jayasurya Arasur Asirvadam, Vijanth Sagayan Zulkifli, Saiful A. Shanthi, N. Lagisetty, Ravikumar Univ Teknol PETRONAS Elect & Elect Seri Iskandar 32610 Perak Malaysia Kongu Engn Coll Comp Sci & Engn Erode 638052 India Indian Space Res Org ISRO Satellite Ctr Bangalore 560094 Karnataka India

ISBN: (纸本)9798350352368

Autonomous Aerial Vehicles (AAVs) have evolved into important assets across diverse industries, owing to their remarkable versatility and adeptness in undertaking a broad spectrum of tasks. AAVs capacity for autonomous landing further enhances their utility, allowing for precise and efficient operations in various environments. This paper presents a landing area identification system for AAVs utilizing digital surface maps from onboard cameras. These maps serve as a primary reference for precise trajectory planning and obstacle avoidance during landing and photogrammetry techniques are used to refine spatial awareness, enabling dynamic adjustments. This work incorporates computer vision algorithms for the interpretation of maps and photogrammetric data, making a decision regarding altitude, approach angle, and lateral adjustments during the landing process. Validation through mathematical models demonstrates the system's effectiveness in achieving precise landings across diverse scenarios. The presented system, combining digital surface maps and photogrammetry, offers a viable solution for AAVs, improving landing accuracy in complex environments.

关键词： Autonomous Aerial Vehicles Open Drone Map webODM Digital Surface Maps Autonomous Landing Photogrammetry Obstacle Avoidance Trajectory Planning sensor fusion and computer vision

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Error State Kalman Filter with Implicit Measurement Equations for Position Tracking of a Multi-sensor System with IMU and LiDAR 13

Error State Kalman Filter with Implicit Measurement Equation...

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13th International conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Ernst, Dominik Vogel, Soren Neumann, Ingo Alkhatib, Hamza Geodetic Institute Leibniz University Hannover Hannover Germany

ISBN: (纸本)9798350320114

With many applications requiring individuals to share spaces with autonomous systems, not only do accurate positioning solutions become crucial, but also understanding of the uncertainty associated with these systems. For applications like public transportation or logistics, position tracking algorithms need to be evaluated on accuracy and consistency. The selection of the appropriate algorithms heavily relies on the specific requirements of the applications, thus demanding novel algorithms for these new use *** paper introduces a novel error state Kalman filter with implicit measurement equations, presenting a solution for new applications. The filter facilitates the fusion of inertial measurement unit (IMU) sensor data with other sensors using arbitrary measurement models. To showcase its effectiveness, the filter is demonstrated by fusing simulated IMU and LiDAR observations for position tracking (complete code on Github). Specifically, the LiDAR points are employed in the update step by minimizing the distances to known planes. Furthermore, the performance of the filter is enhanced by motion compensation through pose interpolation, utilizing the available timestamps. The results are thoroughly discussed in terms of accuracy and consistency, revealing significant improvements by employing pose interpolation. However, the consistency analysis indicates slightly pessimistic results, suggesting the need for further optimizations. © 2023 IEEE.

关键词： Error state Kalman filter IMU LiDAR multi-sensor system position tracking simulation

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A Novel Localization Method for Indoor Mobile Robot Based on Adaptive Weighted fusion 35

A Novel Localization Method for Indoor Mobile Robot Based on...

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35th Chinese Control and Decision conference (CCDC)

作者： Sun, Jincheng Luo, Yang Jiang, Yunxiang Ye, Dan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China

ISBN: (纸本)9798350334722

Localization is a critical issue in autonomous robot navigation and path planning. In this paper, a novel localization method based on adaptive weighted fusion (AWF) is proposed. The method incorporates visual recognition of artificial landmarks and employs different adaptive fusion algorithms based on the pose and position of the mobile robot. Precisely, the method consists of three steps. First, a discrete motion model of the mobile robot is established. Second, the NDT-based laser rangefinder localization method is employed and visual odometry is replaced with artificial landmark recognition, with an analysis of the error model of the IMU and odometer. Finally, Adaptive Weighted fusion (AWF) algorithm is utilized to fuse the sensor information mentioned above and correct the position and direction of the mobile robot. Experimental results demonstrate that the proposed AWF localization method can significantly improve the localization accuracy of the mobile robot. This research direction is promising and is expected to have broader applications in practical settings.

关键词： Mobile robot adaptive weighted fusion multi-sensors fusion localization

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Resource-Efficient sensor fusion via System-Wide Dynamic Gated Neural Networks 21

Resource-Efficient Sensor Fusion via System-Wide Dynamic Gat...

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21st Annual IEEE International conference on Sensing, Communication, and Networking, SECON 2024

作者： Singhal, C. Wu, Y. Malandrino, F. de Guevara Contreras, S. Ladron Levorato, M. Chiasserini, C.F. INRIA France UC Irvine United States CNR-IEIIT Italy CNIT Italy Politecnico di Torino Italy Chalmers University of Technology Sweden

ISBN: (纸本)9798331519186

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%. © 2024 IEEE.

关键词： Constrained optimization

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Deep Learning Approaches for Real-Time Data Analytics in IoT sensor Networks 3rd

Deep Learning Approaches for Real-Time Data Analytics in IoT...

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3rd International conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Mary, P. Arockia Sharma, Avinash Dekka, Satish Gowda, V. Dankan Singh, Mandeep Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Director of Computer Application Chandigarh Group of Colleges Sahibzada Ajit Singh Nagar India Department of Computer Science and Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of University Institute of Computing Chandigarh University Mohalli Punjab Gharuan India

ISBN: (纸本)9789819751457

The rise of the Internet of Things (IoT) has made sensor networks ubiquitous, generating vast amounts of real-time data. However, the challenge lies not only in collecting this data but also in swiftly extracting meaningful insights. Traditional data processing methods often fall short in handling real-time IoT data streams. This research delves into deep learning algorithms as a solution for real-time analytics in IoT sensor networks. We examine suitable architectures for time-series data, such as 1D CNNs, GRUs, and LSTMs. The study also emphasizes the importance of efficient model training, rapid inference techniques, and data preprocessing. Our tests demonstrate that deep learning outshines traditional methods in accuracy and speed. Hence, when applied effectively, deep learning can offer dependable real-time analytics for IoT sensor networks, paving the way for smarter decisions in various IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data Analytics

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Human-Based Gait Authentication Using Multi-modal sensors at the Tactical Edge Device: Smartphone 6

Human-Based Gait Authentication Using Multi-modal Sensors at...

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conference on Big Data VI - Learning, Analytics, and applications

作者： Sandrakumar, Shageenth Khan, Simon Air Force Res Lab Rome NY 13441 USA

ISBN: (纸本)9781510673915;9781510673908

The Department of Defense (DoD) including the US Air force have been seeing increased physical security risks, such as gate runners, active shooter situations, and other use cases (such as Drone ISR, Rapid Deployment, Force Protection, and Security Force), requiring increased coordination and data cooperation. Additionally, technology and operational environments are getting more complex, interconnected and diverse. Recently a team of developers developed a plug-in for the Android Team Awareness Kit (ATAK). This plug-in analyzes sensor data from tactical edge device's (e.g., cell phone) onboard accelerometer and gyroscope to determine the movement of a person when they walk. The plug-in also uses machine learning (ML) algorithms to create a model of that person's gait, and then sends pertinent data through the associated human gait model to authenticate a user. The novelty of our effort lies into enhancing this human gait authentication by using different features extracted from spectral information of the accelerometer and gyroscope signals from the smartphone using a public human activity recognition dataset (WISDM) as a proof of concept, marking a previously unexplored approach. By leveraging spectral data, we seek to enhance the accuracy and robustness of authentication systems in military contexts. After the feature extraction is performed, Kernal Discriminant Analysis (KDA) is utilized to reduce the dimensions of the spectral features to 50;after including the non-spectral features, our total number of final features becomes 65. After adding the total features, we perform feature-level fusion utilizing ML algorithms and the performance shows promising for authentication utilizing 51 users. The SVM-rbf classifiers achieved a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 2% and 97.3%, while the GBM classifiers achieved a mean EER and ACC of 0.4% and 99.1%, and the CNN classifiers achieved a mean EER and ACC of 10% and 90.4% respectively.

关键词： Machine Learning Spectral Feature Extraction Spectogram Gait Welch's Method Envelope Power Spectrum Kernel Discriminant Analysis Feature-level fusion

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GMorph: Accelerating Multi-DNN Inference via Model fusion 24

GMorph: Accelerating Multi-DNN Inference via Model Fusion

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19th European conference on Computer Systems, EuroSys 2024

作者： Yang, Qizheng Yang, Tianyi Xiang, Mingcan Zhang, Lijun Wang, Haoliang Serafini, Marco Guan, Hui University of Massachusetts Amherst AmherstMA United States Adobe Research San JoseCA United States

ISBN: (纸本)9798400704376

AI-powered applications often involve multiple deep neural network (DNN)-based prediction tasks to support application-level functionalities. However, executing multi-DNNs can be challenging due to the high resource demands and computation costs that increase linearly with the number of DNNs. Multi-task learning (MTL) addresses this problem by designing a multi-task model that shares parameters across tasks based on a single backbone DNN. This paper explores an alternative approach called model fusion: rather than training a single multi-task model from scratch as MTL does, model fusion fuses multiple task-specific DNNs that are pre-trained separately and can have heterogeneous architectures into a single multi-task model. We materialize model fusion in a software framework called GMorph to accelerate multi-DNN inference while maintaining task accuracy. GMorph features three main technical contributions: graph mutations to fuse multi-DNNs into resource-efficient multi-task models, search-space sampling algorithms, and predictive filtering to reduce the high search costs. Our experiments show that GMorph can outperform MTL baselines and reduce the inference latency of multi-DNNs by 1.1-3× while meeting the target task accuracy. © 2024 Owner/Author.

关键词： Learning systems

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An Overview of Factors Influencing the Mass Adoption of Self-Driving Vehicles 9

An Overview of Factors Influencing the Mass Adoption of Self...

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IEEE 9th International conference on Power Electronics Systems and applications (PESA)

作者： Li, Zilin Cheng, Ka Wai Eric Chan, Kevin K. W. Hong Kong Polytech Univ Dept Elect Engn Power Elect Res Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665486682

A self-driving vehicle used to be found in science fiction but would promisingly and gradually become a reality in our daily life in the foreseeable future. Self-driving or autonomous vehicles can bring about disruptive changes to the current transportation system, commuting ways, the concept of vehicle ownership, risk management, legal liability, regulations, urban planning, etc. A highly/fully self-driving vehicle is one of the most complex man-made systems and involves various aspects of modern society. Nevertheless, the public seems to lack a clear enough understanding of self-driving vehicles. To further the development of self-driving technologies, it is necessary to develop a comprehensive understanding of multifaceted factors that could hinder the mass adoption of self-driving vehicles. Therefore, this paper intends to present an overview of some technical factors that could influence the advancement of self-driving vehicles. First, this paper reviews state-of-the-art architectures of a self-driving vehicle. Various sensor technologies are followed with a discussion of their limitations. As two essential software subsystems for realizing a self-driving vehicle, the perception and decision-making systems are presented with an overview of their major tasks and challenges. This paper is concluded with a summary of the technical challenges, being a helpful resource for engineers, the public, policy-makers, and social researchers.

关键词： autonomous vehicles architectures camera Lidar overview Radar self-driving car sensor technologies sensor fusion algorithms

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