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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

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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

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

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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

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DRONECOM: Dynamic Relay Operations for Network Efficient Communication in Mines

DRONECOM: Dynamic Relay Operations for Network Efficient Com...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Akash Patel Scott Fredriksson Samuel Nordström Emanuele Pagliari Ilias Tevetzidis Christoforos Kanellakis George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

As the routine operations are starting to become highly automated, it is crucial to develop autonomous solutions that are infrastructure independent. Achieving this is challenging due to the ever-changing landscape of mines, which complicates infrastructure development. In response, this paper introduces a robust framework employing drones to gather data from hard-to-access areas in mines and deliver the data back to the base station for routine monitoring purposes. These tasks include gathering data from operational vehicles (mine trucks, loaders etc.), as well as various sensors (e.g. monitoring rock bolts) and relaying the data to the mine’s base station for monitoring purposes. The proposed framework is based on autonomous navigation using a known point cloud map of the mine, proximity detection via Ultra WideBand (UWB) radios and the data transfer is accomplished through the IEEE 802.15.4 communication standard, operating in the 868 MHz ISM band, with the aim to guarantee long range operation. On the mission level, the drones act as data mules capable of autonomously extracting data from operating vehicles, storing the data onboard and eventually delivering the data to the base station, which is enabled through a Point and Click (PAC) autonomy framework based on global planning, reactive navigation, communication link and behavior management. The efficacy of this framework has been demonstrated through real-world experiments conducted at a test mine in Sweden, validating the overall architecture of the proposed solution.

关键词： Base stations Automation Navigation Fasteners Rocks Sensors Data mining Monitoring Drones Videos

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

Volumetric Mapping with Panoptic Refinement using Kernel Den...

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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

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

arXiv

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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

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Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

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computers, Materials & Continua 2022年第12期73卷 4465-4487页

作者： Zia Ullah Muhammad Ismail Mohmand Sadaqat ur Rehman Muhammad Zubair Maha Driss Wadii Boulila Rayan Sheikh Ibrahim Alwawi Department of Computer Science The Brains InstitutePeshawar25000Pakistan School of Natural and Computing Sciences University of AberdeenAberdeenUK Department of Neurosciences KU Leuven Medical SchoolLeuven3000Belgium Security Engineering Laboratory CCISPrince Sultan UniversityRiyadh12435Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science Robert Gordon UniversityAberdeenUK

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it *** overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature ***,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature ***,the three feature extraction models are utilized using Ensemble Learning techniques for final expression *** representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising *** evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression *** proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and ***,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.

关键词： Ensemble learning emotion recognition feature fusion occlusion

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Enhanced Multi-Occupant Tracking Via Fusion of Low-Intrusive Radar and Wearable Ultra-Wideband Devices Using Autoencoders

SSRN

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SSRN 2024年

作者： Polo-Rodríguez, Aurora Anguita-Molina, Miguel Ángel Ruiz, Ignacio Rojas Medina-Quero, Javier Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Department of Computer Science University of Jaén Jaén23071 Spain

This work explores the integration of millimetre wave (mmWave) Radar and minimal configuration of Ultra-Wideband (UWB) devices for enhanced tracking of multiple occupants in real domestic environments. Using a low-cost, non-intrusive, and quickly deployable device setup, our approach addresses the challenges of multi-occupant tracking with a focus on privacy and ease of installation. Initially, mmWave Radar identifies the presence of occupants with high precision but lacks individual recognition and limited sensitivity. By incorporating a minimal configuration of UWB (wearable tags and ambient anchors), we include identification through signal strength measurements, bypassing complex localisation needs. In addition, we propose several autoencoder models to fuse data from these diverse sources. Experiments carried out in two real-world domestic settings that involve up to three inhabitants per room showcase the ability of the proposal to combine mmWave and UWB technologies for indoor multioccupant tracking, suggesting promising applications in smart homes, ambient assisted living, and eHealth. © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

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Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions

Conformal Decision Theory: Safe Autonomous Decisions from Im...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jordan Lekeufack Anastasios N. Angelopoulos Andrea Bajcsy Michael I. Jordan Jitendra Malik Department of Statistics UC Berkeley Department of Electrical Engineering and Computer Science UC Berkeley Robotics Institute Carnegie Mellon University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans and robot manufacturing.

关键词： Robot motion Pedestrians Machine learning algorithms Decision theory Switches Prediction algorithms Throughput

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Cook2LTL: Translating Cooking Recipes to LTL Formulae using Large Language Models

Cook2LTL: Translating Cooking Recipes to LTL Formulae using ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Angelos Mavrogiannis Christoforos Mavrogiannis Yiannis Aloimonos Department of Computer Science University of Maryland College Park MD USA Department of Robotics University of Michigan Ann Arbor MI

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Cooking recipes are challenging to translate to robot plans as they feature rich linguistic complexity, temporally-extended interconnected tasks, and an almost infinite space of possible actions. Our key insight is that combining a source of cooking domain knowledge with a formalism that captures the temporal richness of cooking recipes could enable the extraction of unambiguous, robot-executable plans. In this work, we use Linear Temporal Logic (LTL) as a formal language expressive enough to model the temporal nature of cooking recipes. Leveraging a pretrained Large Language Model (LLM), we present Cook2LTL, a system that translates instruction steps from an arbitrary cooking recipe found on the internet to a set of LTL formulae, grounding high-level cooking actions to a set of primitive actions that are executable by a manipulator in a kitchen environment. Cook2LTL makes use of a caching scheme that dynamically builds a queryable action library at runtime. We instantiate Cook2LTL in a realistic simulation environment (AI2-THOR), and evaluate its performance across a series of cooking recipes. We demonstrate that our system significantly decreases LLM API calls (−51%), latency (−59%), and cost (−42%) compared to a baseline that queries the LLM for every newly encountered action at runtime.

关键词： Runtime Grounding Large language models Formal languages Linguistics Feature extraction Libraries

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