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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Peringal, Anees Mohiuddin, Mohammed Basheer Haddad, Abdel Gafoor Muthusamy, Praveen Kumar Center for Autonomous Robotic Systems Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The remaining useful life (RUL) prediction is crucial for maintaining aircraft engines’ safety and operational efficiency, requiring precise forecasting in their complex and dynamic environments. This paper presents a novel approach for predicting RUL of aircraft engines using an advanced long short-term memory (LSTM) network. The novelty of this approach lies in the integration of a specially designed loss function and the introduction of a new evaluation metric called the Average Safe Underestimation Error (ASUE). The evaluation scheme utilizes the comprehensive C-MAPSS dataset from NASA, which includes detailed run-to-failure operational data of aircraft engines. The LSTM network, specifically adapted for time-series analysis in RUL prediction, underwent a meticulous preprocessing phase to tailor the dataset for effective LSTM analysis. The network was then trained and optimized to predict RUL with high accuracy. A key challenge in RUL prediction is balancing the risk of overestimating RUL, which can lead to unexpected failures, and under predicting it, which can result in unnecessary maintenance costs. To address this, a modified loss function is incorporated in the LSTM model that specifically penalizes overestimation, thus reducing the likelihood of unexpected engine failures. Furthermore, the ASUE metric is introduced to quantitatively evaluate the model’s performance in maintaining a delicate balance between ensuring operational safety and minimizing maintenance costs. The proposed approach, combining the modified loss function and the ASUE metric, steers the LSTM model towards more accurate, reliable, and cost-effective RUL predictions. This enhances safety and operational efficiency in aviation maintenance, contributing significantly to the field of predictive maintenance in the aerospace industry. The study demonstrates that our methodology not only improves the accuracy of RUL predictions but also plays a vital role in aligning them with economic conside

关键词： Aerospace industry

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Resilient Ground Vehicle autonomous Navigation in GPS-Denied Environments

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Guidance, Navigation and Control 2022年第4期2卷 1-17页

作者： Kleio Baxevani Indrajeet Yadav Yulin Yang Michael Sebok Herbert G.Tanner Guoquan Huang Center for Autonomous&Robotic Systems University of DelawareNewarkDE19716USA

Co-design and integration of vehicle navigation and control and state estimation is key for enabling field deployment of mobile robots in GPS-denied cluttered environments,and sensor calibration is critical for successful operation of both *** paper demonstrates the potential of this co-design approach with field tests of the integration of a reactive receding horizon-based motion planner and controller with an inertial aided multi-sensor calibration *** reported method provides accurate calibration parameters that improve the performance of the state estimator,and enable the motion controller to generate smooth and continuous minimal-jerk trajectories based on local LiDAR *** simulations in Unity,and real-world experimental results from the field corroborate the claims of e±cacy for the reported autonomous navigation computational pipeline.

关键词： Field robots calibration and identification motion and path planning

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Bifurcating vector fields driven by time-scale separated motivational dynamics

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IFAC-PapersOnLine 2023年第2期56卷 3930-3935页

作者： Kleio Baxevani Herbert G. Tanner Center for Autonomous and Robotic Systems University of Delaware USA

Dynamical systems can be designed to exhibit a range of distinct behaviors, which all arise from the same set of continuous dynamics when the latter bifurcates, triggered by a switch in one of its scalar parameters. Building on recent advances that introduce motivation and value dynamics as an efficient way to design multi-behavioral systems, this paper lifts some of the existing restrictions on what kind of planar vector fields can be combined to produce bifurcations. This relaxation enriches the class of dynamical systems that such an approach applies, and gives rise to new behaviors. The paper identifies new analytical conditions under which this new set of planar vector fields can undergo Hopf bifurcations and result in a multi-behavioral system. Numerical simulations and experimental results confirm the theoretical predictions for the existence of the Hopf bifurcations and the applicability of the theory in real systems.

关键词： Control of bifurcation and chaos Adaptive control Switching stability and control Artificial vector fields

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Evaluating Visual-Selective Visual-Inertial Odometry: An End-to-End Multi-Modal Pose Estimation Framework for Underwater Environments 21

Evaluating Visual-Selective Visual-Inertial Odometry: An End...

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21st International Conference on Advanced robotics, ICAR 2023

作者： Sudevan, Vidya Zayer, Fakhreddine Javed, Sajid Karki, Hamad De Masi, Giulia Dias, Jorge Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342291

This paper presents an evaluation of the performance of Visual-Selective Visual-Inertial Odometry (VS-VIO), a hybrid learning-based multi-modal pose estimation framework, in the challenging underwater domain. The assessment is based on Root Mean Square (RMSE) scores for translation and rotation vectors, compared to their reference values. The underwater environment, characterized by low lighting and high turbidity due to suspended particles, poses significant challenges for pose estimation. Understanding how hybrid learning-based multi-modal frameworks perform in such conditions is crucial for improving underwater navigation and exploration. In this study, we thoroughly analyze the performance of VS-VIO and its baseline model at the sub-sequence level, focusing on pose error. Additionally, we assess various technical aspects during the inference phase, including inference speed, power consumption, GPU utilization, GPU memory usage, and temperature. All evaluations are conducted using the AQUALOC dataset. Our findings reveal that the policy network within VS-VIO exhibits the ability to dynamically reduce the utilization of the visual modality while maintaining pose estimation accuracy. However, our analysis shows no statistically significant reduction in the percentage of visual modality usage when altering the penalty factor. These insights provide valuable guidelines for enhancing the performance of hybrid learning-based multimodal pose estimation frameworks in challenging underwater environments, contributing to advancements in underwater navigation and exploration technologies. © 2023 IEEE.

关键词： Data fusion

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Hybrid Visual Inertial Odometry for Robust Underwater Estimation

Hybrid Visual Inertial Odometry for Robust Underwater Estima...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Joshi, Bharat Bandara, Chanaka Poulakakis, Ioannis Tanner, Herbert G. Rekleitis, Ioannis University of South Carolina Department of Computer Science and Engineering United States University of Delaware Center for Autonomous and Robotic Systems United States

ISBN: (纸本)9798218142186

Vision-based state estimation is challenging in underwater environments due to color attenuation, low visibility and floating particulates. All visual-inertial estimators are prone to failure due to degradation in image quality. However, underwater robots are required to keep track of their pose during field deployments. We propose robust estimator fusing the robot's dynamic and kinematic model with proprioceptive sensors to propagate the pose whenever visual-inertial odometry (VIO) fails. To detect the VIO failures, health tracking is used, which enables switching between pose estimates from VIO and a kinematic estimator. Loop closure implemented on weighted posegraph for global trajectory optimization. Experimental results from an Aqua2 autonomous Underwater Vehicle field deployments demonstrates the robustness of our approach over different underwater environments such as over shipwrecks and coral reefs. The proposed hybrid approach is robust to VIO failures producing consistent trajectories even in harsh conditions. © 2023 The Marine Technology Society (MTS).

关键词： autonomous underwater vehicles

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Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification

Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction S...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Rizwana Kausar Fakhreddine Zayer Jaime Viegas Jorge Dias Center for Autonomous Robotic Systems Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.

关键词： Adaptation models Machine learning algorithms Spiking neural networks Robot sensing systems Rendering (computer graphics) Probabilistic logic Robustness

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Optimal ASV Path-Following for Improved Marine Survey Data Quality

Optimal ASV Path-Following for Improved Marine Survey Data Q...

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OCEANS

作者： Kleio Baxevani Grantt E. Otto Arthur C. Trembanis Herbert G. Tanner Mechanical Engineering Department Center of Autonomous and Robotic Systems at the University of Delaware School of Marine Science and Policy Center of Autonomous and Robotic Systems at the University of Delaware

Marine geophysical surveys are one of the methods utilized to map the seabed or water column for a variety of use cases. Multibeam echosounders (MBES), sidescan sonar (SSS), and subbottom profilers (SBP) are examples of instruments used to accurately quantify or georeference seafloor characteristics for applications such as navigation safety/keel clearance, offshore wind or coastal scientific studies. autonomous vehicles can collect similar quality data to that collected by a large vessel at a fraction of the cost and human involvement and with a much smaller carbon footprint. This paper builds upon previous work [1] and proposes a new path-following control approach for an autonomous surface vehicle (ASV) using a more realistic kinematics model that includes lateral drift. The performance of the new controller is verified through simulations and field testing showcasing the improvement in the vessel’s motion and the acquired sonar data.

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MADDPG Based Radar Interference Resource Allocation Decision

MADDPG Based Radar Interference Resource Allocation Decision

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2024 China Automation Congress, CAC 2024

作者： Jiang, Sen Wang, Bo School of HDU-ITMO Research Center for Intelligent Systems and Robotics Hangzhou Dianzi University Hangzhou310018 China School of Automation International Joint Research Laboratory for Autonomous Robotic Systems Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9798350368604

For the problem of jamming penetration under the one-to-many scenario with jammer, a real-time radar interference resource allocation method based on the Multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed. This method describes the interference resource allocation model as a Markov Decision Process (MDP), and designed the state space, action space and reward function for interference resource allocation problem. The jammer is divided into three heterogeneous agents, each responsible for selecting jamming targets, jamming patterns, and jamming power, respectively. These three agents coordinate to enhance the convergence ability of the high-dimensional action space algorithm. Simulation results indicate that the jamming resource allocation method designed in this paper provides better jamming effects on radar positions, higher stability, and effectively improves the operational performance of the jammer. © 2024 IEEE.

关键词： Radar interference

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Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification

arXiv

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arXiv 2024年

作者： Kausar, Rizwana Zayer, Fakhreddine Viegas, Jaime Dias, Jorge Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

关键词： Feature extraction

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Enhanced Gesture Recognition Through Graph-Based Multimodal Fusion 14

Enhanced Gesture Recognition Through Graph-Based Multimodal ...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Rehman, Mobeen Ur Ilyas, Talha Seneviratne, Lakmal Hussain, Irfan Khalifa University KU Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates Clayton 3168 Monash University Monash Medical AI Group Faculty of Engineering and Computer Science VIC Australia

ISBN: (纸本)9798350366556

This study introduces an advanced framework for recognizing hand gestures from a first-person view, leveraging the integration of multimodal data including optical flow, pose, depth, and RGB video recordings. Adeptly navigating the challenges and opportunities presented by the integration of multimodal data. At its core, the framework employs two pivotal components: a cross-attention based adaptive graph convolutional network and relational graph interactions for modality fusion. The former is designed to extract features from skeleton-based gesture data, ensuring a nuanced capture of hand movements by emphasizing the interconnections within the hand's skeletal structure. The latter component innovatively models each output modality feature as a node in a fully connected relational graph, facilitating the fusion of heterogeneous data types through dynamic interactions between modalities. This approach allows for the leveraging of each data type's strengths and the mitigation of their weaknesses, significantly enhancing the system's classification accuracy and robustness. Tested on a public benchmark dataset, the framework achieved a remarkable accuracy of 98.48%, demonstrating its efficacy. Moreover, it proves resilient, maintaining strong performance (93.48% accuracy) even in scenarios where only one modality is available, highlighting its potential for real-world applications. This advancement sets a new benchmark in hand gesture recognition, promising future developments in multimodal data fusion. © 2024 IEEE.

关键词： Musculoskeletal system

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