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Autonomous robotic re-alignment for face-to-face underwater human-robot interaction*

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Autonomous robotic re-alignment for face-to-face underwater ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Demetrious T. Kutzke Ashwin Wariar Junaed Sattar Department of Computer Science & Engineering Minnesota Robotics Institute University of Minnesota–Twin Cities Minneapolis MN USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The use of autonomous underwater vehicles (AUVs) to accomplish traditionally challenging and dangerous tasks has proliferated thanks to advances in sensing, navigation, manipulation, and on-board computing technologies. Utilizing AUVs in underwater human-robot interaction (UHRI) has witnessed comparatively smaller levels of growth due to limitations in bi-directional communication and significant technical hurdles to bridge the gap between analogies with terrestrial interaction strategies and those that are possible in the underwater domain. A necessary component to support UHRI is establishing a system for safe robotic-diver approach to establish face-to-face communication that considers nonstandard human body pose. In this work, we introduce a stereo vision system for enhancing UHRI that utilizes threedimensional reconstruction from stereo image pairs and machine learning for localizing human joint estimates. We then establish a convention for a coordinate system that encodes the direction the human is facing with respect to the camera coordinate frame. This allows automatic setpoint computation that preserves human body scale and can be used as input to an image-based visual servo control scheme. We show that our setpoint computations tend to agree both quantitatively and qualitatively with experimental setpoint baselines. The methodology introduced shows promise for enhancing UHRI by improving robotic perception of human orientation underwater.

关键词： Visualization Navigation Robot kinematics Human-robot interaction Machine learning Robot sensing systems Stereo vision

Volumetric Mapping with Panoptic Refinement using Kernel Density Estimation for Mobile Robots

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Volumetric Mapping with Panoptic Refinement using Kernel Den...

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

作者： Khang Nguyen Tuan Dang Manfred Huber Department of Computer Science and Engineering Learning and Adaptive Robotics Laboratory University of Texas at Arlington Arlington TX USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on RGB images and then localize them via depth maps; however, they often encounter out-of-distribution scenarios where masks over-cover the objects. In this paper, we address the problem of panoptic segmentation quality in 3D scene reconstruction by refining segmentation errors using non-parametric statistical methods. To enhance mask precision, we map the predicted masks into a depth frame to estimate their distribution via kernel densities. The outliers in depth perception are then rejected without the need for additional parameters in an adaptive manner to out-of-distribution scenarios, followed by 3D reconstruction using projective signed distance functions (SDFs). We validate our method on a synthetic dataset, which shows improvements in both quantitative and qualitative results for panoptic mapping. Through real-world testing, the results furthermore show our method’s capability to be deployed on a real-robot system. Our source code is available at: https://***/mkhangg/refined_panoptic_mapping.

关键词： Image segmentation Three-dimensional displays Statistical analysis Source coding Semantics Mobile robots Kernel Image reconstruction Testing Synthetic data

One-Shot Gesture Recognition for Underwater Diver-To-Robot Communication

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arXiv 2025年

作者： Joshi, Rishikesh Sattar, Junaed Department of Computer Science and Engineering Minnesota Robotics Institute University of Minnesota – Twin Cities MinneapolisMN United States

Reliable human-robot communication is essential for effective underwater human-robot interaction (U-HRI), yet traditional methods such as acoustic signaling and predefined gesture-based models suffer from limitations in adaptability and robustness. In this work, we propose One-Shot Gesture Recognition (OSG), a novel method that enables real-time, pose-based, temporal gesture recognition underwater from a single demonstration, eliminating the need for extensive dataset collection or model retraining. OSG leverages shape-based classification techniques, including Hu moments, Zernike moments, and Fourier descriptors, to robustly recognize gestures in visually-challenging underwater environments. Our system achieves high accuracy on real-world underwater video data and operates efficiently on embedded hardware commonly found on autonomous underwater robots (AUVs), demonstrating its feasibility for deployment on-board robots. Compared to deep learning approaches, OSG is lightweight, computationally efficient, and highly adaptable, making it ideal for diver-to-robot communication. We evaluate OSG’s performance on an augmented gesture dataset and real-world underwater video data, comparing its accuracy against deep learning methods. Our results show OSG’s potential to enhance U-HRI by enabling the immediate deployment of user-defined gestures without the constraints of predefined gesture languages. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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

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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)

A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

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A Scalable Real-time Semantic Segmentation Network for Auton...

1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

Collision-Free Landing of Multiple UAVs on Moving Ground Vehicles Using Time-Varying Control Barrier Functions

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Collision-Free Landing of Multiple UAVs on Moving Ground Veh...

American Control Conference (ACC)

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

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs' motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV's nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets.

关键词： Tracking Autonomous aerial vehicles Rendering (computer graphics) Land vehicles Safety Matlab

Barriers on the EDGE: A scalable CBF architecture over EDGE for safe aerial-ground multi-agent coordination

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arXiv 2024年

作者： Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi 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 address the problem of designing a scalable control architecture for a safe coordinated operation of a multi-agent system with aerial (UAVs) and ground robots (UGVs) in a confined task space. The proposed method uses Control Barrier Functions (CBFs) to impose constraints associated with (i) collision avoidance between agents, (ii) landing of UAVs on mobile UGVs, and (iii) task space restriction. Further, to account for the rapid increase in the number of constraints for a single agent with the increasing number of agents, the proposed architecture uses a centralized-decentralized Edge cluster, where a centralized node (Watcher) activates the relevant constraints reducing the need for high onboard processing and network complexity. The distributed nodes run the controller locally to overcome latency and network issues. The proposed Edge architecture is experimentally validated using multiple aerial and ground robots in a confined environment performing a coordinated operation. © 2024, CC BY.

关键词： Antenna grounds

Problem of Optimal Area Monitoring and Universal Motion Stabilisation System for its Practical Realisation

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Problem of Optimal Area Monitoring and Universal Motion Stab...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Askhat Diveev Elena Sofronova Department of Robotics Control Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The area monitoring problem is considered as optimization task. The problem belongs to the class of global optimization. First, the problem of finding optimal trajectories is solved so that the control objects, when moving along these trajectories, observe the entire area, do not collide with each other and do not violate phase constraints. One of the main quality criteria when searching for trajectories is their minimum total length, and the closeness of the trajectory lengths of each control object. Subsequently, the optimal control problem is solved in order to ensure the movement of the object along the obtained optimal trajectory. To solve the problem in class of implemented in real object control functions the stabilisation system of control object motion along a given trajectory is synthesized. When solving the optimal control problem, the same criteria are considered as when finding optimal trajectories, with the addition of the accuracy of passing points on trajectories. An illustrative example of solving the optimal area monitoring problem by two quadcopters with one circular phase constraint is provided. Symbolic regression is employed to synthesise a system for stabilising the movement of an object along the optimal trajectory.

关键词： Accuracy Perturbation methods Optimal control Search problems Trajectory Feedback control Information technology Monitoring Optimization Quadrotors

CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis

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arXiv 2024年

作者： Alberb, Muhammad Elbatel, Marawan Elgebaly, Aya Montoya-Del-Angel, Ricardo Li, Xiaomeng Martí, Robert Computer Vision and Robotics Institute University of Girona Spain The Hong Kong University of Science and Technology Hong Kong

Digital Breast Tomosynthesis (DBT) is an advanced breast imaging modality that offers superior lesion detection accuracy compared to conventional mammography, albeit at the trade-off of longer reading time. Accelerating lesion detection from DBT using deep learning is hindered by limited data availability and huge annotation costs. A possible solution to this issue could be to leverage the information provided by a more widely available modality, such as mammography, to enhance DBT lesion detection. In this paper, we present a novel framework, CoMoTo, for improving lesion detection in DBT. Our framework leverages unpaired mammography data to enhance the training of a DBT model, improving practicality by eliminating the need for mammography during inference. Specifically, we propose two novel components, Lesion-specific Knowledge Distillation (LsKD) and Intra-modal Point Alignment (ImPA). LsKD selectively distills lesion features from a mammography teacher model to a DBT student model, disregarding background features. ImPA further enriches LsKD by ensuring the alignment of lesion features within the teacher before distilling knowledge to the student. Our comprehensive evaluation shows that CoMoTo is superior to traditional pretraining and image-level KD, improving performance by 7% Mean Sensitivity under low-data setting. Our code is available at https://***/Muhammad-Al-Barbary/CoMoTo. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation