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2023 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2023

作者： Abouheaf, Mohammed Gueaieb, Wail Mayyas, Mohammad Bowling Green State University Robotics Engineering Bowling GreenOH43403 United States Ottawa University School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

ISBN: (纸本)9798350308044

This paper introduces an innovative data-driven approach for replicating behaviors in interconnected and heterogeneous dynamic systems. The core concept involves real-time control of dynamic systems to closely mimic reference-model trajectories using model-free techniques. Within this coupled framework, one component possesses complete information about reference-trajectories, although not necessarily their dynamics. In contrast, follower systems, with limited connectivity to reference-model trajectories, exclusively replicate the behavior of the primary process, which retains insight into model-reference dynamics. The adopted strategies are causal, integrating higher-order error dynamics to ensure precise tracking of reference-trajectories. Furthermore, these strategies incorporate variations in reference-model dynamics via a pseudo partial derivative, akin to sensitivity derivatives in model-reference adaptive strategies. To optimize the dynamic behavior of the follower process, the solution employs a reinforcement learning mechanism through adaptive critics. This mechanism approximates the optimal strategy and the associated value function. The actor and critic weights of the adaptive critic structure are tuned using a projection technique to ensure convergence of the adapted strategy. The validation of this solution is demonstrated on a dynamic system with delays, simulating an underwater vehicle scenario. The developed methodology is rigorously compared with another high-order model-free adaptive control approach. The presented approach showcases its capability to effectively replicate behaviors, resulting in improved tracking accuracy. © 2023 IEEE.

关键词： Trajectories

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Sub-Optimal Decentralized Navigation of Multiple Holonomic Agents in Simply-Connected Workspaces

Sub-Optimal Decentralized Navigation of Multiple Holonomic A...

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IEEE Conference on Decision and Control

作者： Dimitrios Kotsinis Charalampos P. Bechlioulis Department of Electrical and Computer Engineering Athena Research Center Robotics Institute Maroussi Greece University of Patras Greece

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a sub-optimal approach for the multi-agent navigation problem in simply-connected workspaces. We design a decentralized control law exhibiting the following three properties: (1) navigation of each agent with the optimal policy towards its destination, (2) avoidance of collision with other nearby agents and the workspace boundary, and (3) knowledge about the current position and not the destination of nearby agents. Moreover, we refer to the sub-optimal approach because the computational complexity and time needed to calculate the global optimal solution become unrealistic as the number of agents increases. In our case, each agent has a predetermined optimal policy calculated by a novel off-policy iterative method, to go towards its destination and it deviates from it in order to avoid collisions. The purpose of the simulation study is to examine how much the sub-optimal greedy trajectory of each agent deviates from the optimal one.

关键词： Navigation Decentralized control Vectors Trajectory Iterative methods Computational complexity Game theory Multi-agent systems

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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A Comprehensive SLAM Dataset for Indoor Exhibition Environments: Data Collection, Processing, and Comparative Evaluation

A Comprehensive SLAM Dataset for Indoor Exhibition Environme...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Yuan, Songyu Zhang, Jing Pan, Chen Zhang, Chunlong Wan, Minhong Zheng, Tao Gu, Jason Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

The field of Simultaneous Localization and Mapping(SLAM) has witnessed significant advancements in recent years. However, due to the difficulty of data obtains and ground-truth makes, there is a lack of real indoor scenarios datasets, especially the scenarios with less-texture, repeat-texture, extreme light condition, causing the slowly progress in indoor SLAM. In this paper, we present a dataset captured in a real indoor exhibition hall with diverse type of sensor data. Which contributes to diverse school of SLAM that using different sensors. We provide ground-truth 6DoF pose captured by motion capture devices, RGB-D data captured by a RealSense D455 camera. Laser-data captured by Ouster. And a high-resolution color point cloud captured by a laser scanner. This dataset offers three distinct sources of ground truth from real-world scenarios. It combines motion capture, ICP based laser localization, and visual localization methods to provide comprehensive and accurate reference data. This dataset is designed for evaluating positioning methods that employ various types of sensors, moving beyond a singular matrix-based evaluation approach. Besides, We show results of several state-of-the-art SLAM method in our datasets, illustrates the utility of our dataset and served as a reference for other researcher. We converge LiDAR-based SLAM with ground truth, made a good closure. An ensemble approach-based Visual SLAM, achieves results of odometry errors of 4% and loop closure position errors of 40 cm. Additionally, visual localization demonstrated an absolute position error of 21 cm using high-resolution point clouds. We highlight the potentials of our indoor datasets in LiDAR SLAM, RGB-D SLAM, and visual localization and combines method. The datasets are available at https://***/IMBAfarmer09/ExhibitionDataset © 2023 IEEE.

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A CBF-Adaptive Control Architecture for Visual Navigation for UAV in the Presence of Uncertainties

A CBF-Adaptive Control Architecture for Visual Navigation fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Viswa Narayanan Sankaranarayanan Akshit Saradagi Sumeet Satpute George Nikolakopoulos Robotics and Artificial Intelligence Group of the Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which an adaptive controller tracks to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

关键词： Visualization Uncertainty Target tracking Navigation Velocity control Robot vision systems Switches

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Collaborative Aerial 3D Printing: Leveraging UAV Flexibility and Mesh Decomposition for Aerial Swarm-Based Construction

Collaborative Aerial 3D Printing: Leveraging UAV Flexibility...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marios-Nektarios Stamatopoulos Avijit Banerjee George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and Artificial Intelligence Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The article introduces a novel approach to foster collaborative aerial 3D printing by leveraging the dexterous flexibility of multiple UAVs working in tandem towards autonomous construction. This cooperative operation effectively overcomes payload limitations through synchronized deployment. Nevertheless, the transformation of UAVs into aerial construction agents poses a pivotal challenge, demanding efficient coordination of task planning and effective execution among multiple collaboratively working UAVs. In pursuit of addressing this challenge, the proposed innovative framework introduces a novel chunk-decomposition strategy supported by a reactive task assignment mechanism, dynamically allocating additive manufacturing tasks based on a dependency graph derived from decomposed chunks. Furthermore, the framework promotes parallelization by minimizing interdependencies thereby reducing the overall makespan. It also incorporates conflict resolution among UAVs during the assignment process by employing probabilistic fitness scores and penalizing the probability of conflicts. Conflicts that emerge during printing execution are addressed in a decentralized manner through trajectory sharing among UAVs. This entails dynamically determining one UAV suspending its movement until conflicts are resolved. The proposed framework's effectiveness is evaluated through a GAZEBO-based simulation setup, showcasing its potential in deploying multiple UAVs for the simultaneous printing of large-scale 3D structures. - Video overview: https://***/bLnzcLrD1NA

关键词： Solid modeling Three-dimensional displays Systematics Collaboration Three-dimensional printing Autonomous aerial vehicles Trajectory

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A Joint Tracking System: Robot is Online to Access Surveillance Views

A Joint Tracking System: Robot is Online to Access Surveilla...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Lin, Zheyuan Ji, Shanshan Wang, Wen Qin, Mengjie Yang, Rong Wan, Minhong Gu, Jason Li, Te Zhang, Chunlong Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot's field of vision and heavy occlusion by people or objects. © 2023 IEEE.

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Two-Stream Architecture with Contrastive and Self-Supervised Attention Feature Fusion for Error-related Potentials Classification

Two-Stream Architecture with Contrastive and Self-Supervised...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Luís Garrote João Perdiz Mine Yasemin Gabriel Pires Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Portugal

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Error-related potentials (ErrPs) extracted from electroencephalographic signals hold potential for application in Brain-Machine Interfaces, in contexts such as robot teleoperation or shared control in assistive platforms. Due to difficulties in signal classification, in part caused by its non-stationary and noisy nature, their use has not been fully realized *** work proposes a new approach to ErrP classification based on a two-stream deep learning architecture with three training stages. Its first stage is a self-supervised autoencoder architecture with a multi-head attention layer providing relevant latent features. The second stage comprises a supervised contrastive learning approach considering two backbone networks, where one inherits weights from the first stage and the other is updated by considering the feature embeddings distribution. The final stage comprises supervised classification, where the two backbones are fused and used to classify the input EEG signal. At the end of the three stages, a data-driven two-stream ErrP model is ***-five variants of the proposed approach using the Deep Convolutional Network, Shallow Convolutional Network and EEGNet backbones were tested in an ablation study and benchmarked against a large number of classical classification methods, using data from the BNCI dataset intended to assess cross subject generalization capabilities. The proposed approach obtained the best results overall, highlighting the approach’s capabilities in capturing relevant representations of the EEG signal.

关键词： Training Electric potential Deep architecture Contrastive learning Feature extraction Brain modeling Electroencephalography Noise measurement Convolutional neural networks Robots

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Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

arXiv

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arXiv 2024年

作者： Pereira, Ricardo Garrote, Luís Barros, Tiago Lopes, Ana Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Polytechnic Institute of Tomar Portugal

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Despite the significant performance boost in classification tasks achieved in recent years, provided by the use of deep-learning-based methods, limitations such as inter-category ambiguity and intracategory variation have been holding back their performance. To overcome such issues, gathering semantic information has been shown to be a promising source of information towards a more complete and discriminative feature representation of indoor scenes. Therefore, the work described in this paper uses both semantic information, obtained from object detection, and semantic segmentation techniques. While object detection techniques provide the 2D location of objects allowing to obtain spatial distributions between objects, semantic segmentation techniques provide pixel-level information that allows to obtain, at a pixel-level, a spatial distribution and shape-related features of the segmentation categories. Hence, a novel approach that uses a semantic segmentation mask to provide Hu-momentsbased segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed. Moreover, a three-main-branch network, designated by GOS2F2App, that exploits deep-learning-based global features, object-based features, and semantic segmentation-based features is also proposed. GOS2F2App was evaluated in two indoor scene benchmark datasets: SUN RGB-D and NYU Depth V2, where, to the best of our knowledge, state-of-the-art results were achieved on both datasets, which present evidences of the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Object detection

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A CBF-Adaptive Control Architecture for Visual Navigation for UAV in the Presence of Uncertainties

arXiv

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arXiv 2024年

作者： Sankaranarayanan, Viswa Narayanan Saradagi, Akshit Satpute, Sumeet Nikolakopoulos, George Robotics and Artificial Intelligence Group The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which is tracked by an adaptive controller to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera. © 2024, CC BY-NC-ND.

关键词： Unmanned aerial vehicles (UAV)

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