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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

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Invisible Servoing: A Visual Servoing Approach with Return-C...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

A Comparative Study of Lossless Compression Technique in Quantum Secure Direct Communication

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A Comparative Study of Lossless Compression Technique in Qua...

Cyber Security and Computing (CyberComp), International Conference on

作者： Izlin Zuriani Ishak Liew Seow Wooi Nur Ziadah Harun Nur Shahirah Azahari Sofia Najwa Ramli Osman Ghazali Department of Information Technology and Communication Politeknik Seberang Perai Pulau Pinang Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Faculty of AI and Robotics Raffles University Johor Malaysia Department of Information Security and Web Technology Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

This is a comparative study to explore the performance of three lossless compression techniques, namely Run Length Encoding (RLE), Shannon-Fano Coding (SFC), and Huffman Coding (HC) in the context of a Hybrid M-Ary in Braided Single Stage (HMBSS) protocol communication scheme. The previous study on HMBSS using HC found that HC's compression technique has drawbacks, i.e., it is sensitive to errors and noise in the transmission channel and limited to certain data types. These drawbacks did affect the value of the compression rate limit. In this study, we evaluate the effectiveness of RLE, SFC, and HC in terms of data size reduction, coding efficiency, and data transmission time by comparing their results arising from the simulation experiments using Python coding in Jupiter Notebook and developed based on the HMBSS protocol algorithm. This simulation experiment starts with an initial six randomly generated datasets. Each dataset is tested using three compression techniques, which resulted in 18 different datasets. The results from the experiments reveal that SFC and HC consistently achieved a higher compression ratio and coding efficiency than RLE. These two compression techniques' algorithms effectively reduce the data size while preserving the original information. In our study, we also find that the data transmission time is increased with larger text sizes for all three compression techniques. This indicates that the larger the data size, the more transmission time is required with regard to what the compression technique's algorithm is.

关键词： Protocols Quantum computing Jupiter Noise Decoding Complexity theory Data communication Channel coding Huffman coding Testing

NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification

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arXiv 2023年

作者： Zhang, Chuhan Pan, Wei Della Santina, Cosimo Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Department of Computer Science The University of Manchester Manchester United Kingdom

Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a Non-iterative Leaky Integrate-and-Fire (NiLIF) neuron model, overcoming the gradient issues in traditional Spiking Neural Networks (SNNs) that use Iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed Non-iterative SNN with Attention (NiSNN-A) model on two motor imagery EEG datasets, OpenBMI and BCIC IV 2a. Experiment results demonstrate that 1) our model outperforms other SNN models by achieving higher accuracy, 2) our model increases energy efficiency compared to the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control

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arXiv 2025年

作者： Wan, Hanwen Chen, Yifei Wei, Zeyu Li, Dongrui Lin, Zexin Wu, Donghao Cheng, Jiu Zhang, Yuxiang Ji, Xiaoqiang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The School of Computer Science The University of Sydney Australia Faculty of Computer and Mathematical Sciences The Hong Kong Polytechnic University China Graduate School of Fundamental Science and Engineering Waseda University Japan

This paper introduces EmbodiedAgent, a hierarchical framework for heterogeneous multi-robot control. EmbodiedAgent addresses critical limitations of hallucination in impractical tasks. Our approach integrates a next-action prediction paradigm with a structured memory system to decompose tasks into executable robot skills while dynamically validating actions against environmental constraints. We present MultiPlan+, a dataset of more than 18,000 annotated planning instances spanning 100 scenarios, including a subset of impractical cases to mitigate hallucination. To evaluate performance, we propose the Robot Planning Assessment Schema (RPAS), combining automated metrics with LLM-aided expert grading. Experiments demonstrate EmbodiedAgent’s superiority over state-of-the-art models, achieving 71.85% RPAS score. Real-world validation in an office service task highlights its ability to coordinate heterogeneous robots for long-horizon objectives. © 2025, CC BY.

关键词： Multipurpose robots

Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

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arXiv 2023年

作者： Fiedler, David Čertický, Michal Alonso-Mora, Javier Pěchouček, Michal Čáp, Michal Department of Computer Science Faculty of Electrical Engineering CTU in Prague Czech Republic Department of Cognitive Robotics 3ME TU Delft Netherlands

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic. © 2023, CC BY-NC-SA.

关键词： Vehicle routing

Distance-based Formation-Motion Control for Unicycle Agents

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Distance-based Formation-Motion Control for Unicycle Agents

American Control Conference (ACC)

作者： Jin Chen Bayu Jayawardhana Hector Garcia de Marina Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Department of Computer Engineer Automation and Robotics and With CITIC The University of Granada Spain

We present a distance-based distributed formation-motion control law for unicycle agents that are required to move together at a constant reference speed. The distributed control law consists of the standard distance-based formation gradient control law, which is projected to the longitudinal and angular velocity inputs of the unicycle, and of a linear term that depends on the reference speed. The main contribution of this paper is to realize the consistency of unicycle agents’ orientations without orientation measurement, thereby reducing the possibility of significant orientation measurement errors in real-world applications. We prove and show numerically the local exponential convergence of the unicycle agents to the desired formation, where they eventually move in unison at a constant reference speed.

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Scaling up the formation of agents with heterogeneous sensing: mixed distance and bearing-only

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arXiv 2023年

作者： Chen, Jin Jayawardhana, Bayu de Marina, Hector Garcia The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Netherlands Department of Computer Engineer Automation and Robotics CITIC The University of Granada Spain

Unlike the case with identical neighboring agents whose actions are mirrored, the problem of distributed formation control design with heterogeneous sensing is not straightforward. In this paper, we consider the problem of distributed formation control where each agent can only control distances or bearings with its neighbors. We firstly develop a rigidity theory with heterogeneous sensing to ensure that the desired shape is well-posed. Secondly, we propose an iterative method that allows us to scale up the number of agents with heterogeneous sensing. Finally, numerical simulations show the effectiveness of our method. © 2023, CC BY.

关键词： Iterative methods

Comparison of Rating Scale and Pairwise Comparison Methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

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Comparison of Rating Scale and Pairwise Comparison Methods f...

IEEE International Conference on robotics and Automation (ICRA)

作者： Qiao Wang Ziqi Wang Marc G. Carmichael Dikai Liu Chin-Teng Lin Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity *** variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： Surveys Accuracy NASA Collaboration Reliability engineering Particle measurements Task analysis

Convolutional neural network-based cross-corpus speech emotion recognition with data augmentation and features fusion

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Convolutional neural network-based cross-corpus speech emoti...

作者： Jahangir, Rashid Teh, Ying Wah Mujtaba, Ghulam Alroobaea, Roobaea Shaikh, Zahid Hussain Ali, Ihsan Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Science COMSATS University Islamabad Vehari Campus Pakistan Center of Excellence for Robotics Artificial Intelligence and Blockchain Department of Computer Science Sukkur IBA University Sukkur Pakistan Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Mathematics Sukkur IBA University Sukkur Pakistan

Speech emotion recognition (SER) is one of the most challenging and active research topics in data science due to its wide range of applications in human–computer interaction, computer games, mobile services and psychological assessment. In the past, several studies have employed handcrafted features to classify emotions and achieved good classification accuracy. However, such features degrade the classification accuracy in complex scenarios. Thus, recent studies employed deep learning models to automatically extract the local representation from given audio signals. Though, automated feature engineering overcomes the issues of handcrafted feature extraction approach. However, still there is a need to further improve the performance of reported techniques. This is because, in reported techniques, single-layer and two-layer convolutional neural networks (CNNs) were used and these architectures are not capable of learning optimal features from complex speech signals. Thus, to overcome this limitation, this study proposed a novel SER framework, which applies data augmentation methods before extracting seven informative feature sets from each utterance. The extracted feature vector is used as input to the 1D CNN for emotions recognition using the EMO-DB, RAVDESS and SAVEE databases. Moreover, this study also proposed a cross-corpus SER model using the all audio files of common emotions of aforementioned databases. The experimental results showed that our proposed SER framework outperformed existing SER frameworks. Specifically, the proposed SER framework obtained 96.7% accuracy for EMO-DB with all utterances in seven emotions, 90.6% RAVDESS with all utterances in eight emotions, 93.2% for SAVEE with all utterances in seven emotions and 93.3% for cross-corpus with 1930 utterances in six emotions. We believe that our proposed framework will bring significant contribute to SER domain. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of S

关键词： Emotion Recognition