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检索条件"机构=Robotics and Electrical and Computer Engineering"

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Machine Learning Assisted Locomotion Tracking of Dielectric Elastomer Actuators Based Soft Water Robot

Machine Learning Assisted Locomotion Tracking of Dielectric ...

引用

IEEE International Conference on Soft robotics (RoboSoft)

作者： Md. Asaduzzaman Jabin Berek Ha Mable P. Fok Photonics and Soft Robotics Lab School of Electrical and Computer Engineering College of Engineering University of Georgia Athens GA USA

Locomotion tracking of a soft water robot that is propelled and steerable by dielectric elastomer actuators (DEAs), is proposed and demonstrated using a hybrid machine learning model. Measuring at 6 cm x 17 cm x 5 cm and weighing 28 g, this soft water robot can achieve a speed of 3 cm/s, equivalent to 50% of its body length per second (BL/s), while consuming approximately 1.0 W of power. Its buoyancy is enabled by the hexagonal honeycomb base made up of silicone. The DEA actuators are constructed from conductive carbon grease as the electrodes on the elastomer. The steerable soft water robot can complete a full 90° turn within 18 seconds. A comprehensive finite element analysis (FEA) is conducted on the actuators and the soft water robot to provide an in-depth study of the soft robot's performance including actuation force, travel distance, actuation angle, and steering disposition. For precise locomotion tracking, a novel hybrid machine learning model is developed, comprising two multi-layer perceptron branches in a single in-house model. Two additional bypass layers are added for steering behavior classification. The model attains a real- time accuracy of 98% for both the water robot's displacement and steering behavior prediction.

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RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction

RecNet: An Invertible Point Cloud Encoding through Range Ima...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolaos Stathoulopoulos Mario A.V. Saucedo Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet’s methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments 1 confirming its efficacy and potential for real-world applications.

关键词： Point cloud compression Measurement Image coding Three-dimensional displays Bandwidth Vectors Multi-robot systems

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Autonomous Residual Threshold Detection for Decentralized Radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Moumita Mukherjee Avijit Banerjee Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator’s precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO).

关键词： Location awareness Smoothing methods Radar measurements Radar detection Radar Sensor fusion Robot sensing systems

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Human Detection Combining RGB and Thermal Modalities

Human Detection Combining RGB and Thermal Modalities

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Chaymaa Fakrane Kennedy O. S. Mota Cristiano Premebida Rui P. Rocha Dept. of Electrical and Computer Engineering (DEEC) Institute of Systems and Robotics (ISR) University of Coimbra Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper addresses a late-fusion approach that combines RGB and thermal (long-wave IR) modalities to enhance human detection in indoor environments for robotic perception domains. The study explores multimodal human detection using YOLO series (v5s-6s and v8s-11s models), demonstrating significant improvements in detection accuracy under challenging lighting conditions on a dataset collected by a mobile robot. The use of a Weighted-Mean (WM) as a late-fusion approach demonstrates favorable results over more complex methods such as neural networks or SVM. The WM approach is simple to implement and has a low computational cost. Additionally, it is more interpretable and modular and allows straightforward adjustments to the weights of the YOLO models and thresholds, facilitating adaptation to the target system's specific needs. The experimental results indicate that the implemented late-fusion approach improves the baseline performance in terms of mean average precision.

关键词： YOLO Support vector machines Adaptation models Accuracy Computational modeling Neural networks Lighting Indoor environment Computational efficiency Mobile robots

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Real-Time Foot Pressure Mapping Using a Smart Insole with Sinusoidally Embedded Fiber Bragg Gratings

Real-Time Foot Pressure Mapping Using a Smart Insole with Si...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Steven Binder Thomas Anton Mable Fok Photonics and Soft Robotics Laboratory School of Electrical and Computer Engineering College of Engineering University of Georgia Athens GA USA

A flexible smart insole with sinusoidally embedded fiber Bragg grating sensors is designed and fabricated for delamination-free real time foot pressure mapping. The unique sensor configuration allows for accurate and ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Pain Lasers and electrooptics Fiber gratings Reliability engineering Real-time systems Sensor systems Delamination

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Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation

Belief Scene Graphs: Expanding Partial Scenes with Objects t...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mario A.V. Saucedo Akash Patel Akshit Saradagi Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of Belief Scene Graphs (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of *** a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/hsGlSCa12iY

关键词： Training Solid modeling Histograms Three-dimensional displays Computational modeling Robot sensing systems Search problems

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Constant-time Motion Planning with Anytime Refinement for Manipulation

Constant-time Motion Planning with Anytime Refinement for Ma...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Itamar Mishani Hayden Feddock Maxim Likhachev School of Computer Science Robotics Institute Carnegie Mellon University Pittsburgh PA Electrical and Computer Engineering Department Pittsburgh University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motion planning the bottleneck for achieving reliable and adaptable autonomy. Recently, a class of constant-time motion planners (CTMP) was introduced. These planners employ a preprocessing phase to compute data structures that enable online planning provably guarantee the ability to generate motion plans, potentially sub-optimal, within a user defined time bound. This framework has been demonstrated to be effective in a number of time-critical tasks. However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution. To this end, we propose an anytime refinement approach that works in combination with CTMP algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. Furthermore, functioning as an anytime algorithm, it iteratively refines the solution’s quality within the allocated time budget. This enables our approach to strike a balance between guaranteed fast plan generation and the pursuit of optimization over time. We support our approach by elucidating its analytical properties, showing the convergence of the anytime component towards optimal solutions. Additionally, we provide empirical validation through simulation and real-world demonstrations on a 6 degree-of-freedom robot manipulator, applied to an assembly domain.

关键词： Manipulators Reliability engineering Data structures Real-time systems Planning Time factors Task analysis

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Leveraging Computation of Expectation Models for Commonsense Affordance Estimation on 3D Scene Graphs

Leveraging Computation of Expectation Models for Commonsense...

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

作者： Mario A.V. Saucedo Nikolaos Stathoulopoulos Akash Patel Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object’s inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/BDCMVx2GiQE

关键词： Three-dimensional displays Affordances Computational modeling Semantics Estimation Probability distribution Cognition Planning Indoor environment Optimization

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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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TFMarker: A Tangible Fiducial Pattern for Enabling Camera-assisted Guided Landing in SubT Environments

TFMarker: A Tangible Fiducial Pattern for Enabling Camera-as...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Mario A. V. Saucedo Akash Patel Niklas Dahlquist Yifan Bai Björn Lindqvist Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Visual servoing plays a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to aerial manipulation. This article proposes TFMarker, a novel tangible fiducial pattern for enabling camera-assisted guided landing of UAVs by using the visual features from color markers as the main source of information. TFMarker is structured around a 4-point fiducial marker, allowing for accurate, precise, and consistent pose estimation in different environments and lighting conditions, while also offering resilience to motion blur. The presented detection framework is based on a three-step architecture, where the first step uses Gaussian and color filtering in addition to morphological operation in order to generate a robust detection of the markers. The second step uses the Gift Wrapping Algorithm, to organize the same-color markers based on their relative positioning with respect to the off-color marker. Finally, the Perspective-n-Point optimization problem is solved in order to extract the pose (i.e. position and orientation) of the proposed pattern with respect to the vision sensor. The efficacy of the proposed scheme has been extensively validated in indoor and SubT environments for the task of autonomous landing using a custom-made UAV. The experimental results showcase the performance of the proposed method, which presents a better detection rate in both environments while retaining similar accuracy and precision to the baseline approach. For the video of the experimental evaluation please refer to the following link: https://***/Zh13OObp15Q

关键词： Visualization Accuracy Filtering Color Vision sensors Autonomous aerial vehicles Visual servoing Wrapping Optimization Morphological operations

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