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arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Chen, Haoqing Wang, Xiaoling Zhu, Fengping Li, Yuxin Wang, Xiangfeng Jin, Bo School of Computer Science and Technology East China Normal University Shanghai200062 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Huashan Hospital Fudan University Shanghai200040 China School of Computer Science and Technology East China Normal University Shanghai AI Laboratory Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalitie

关键词： Redundancy

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An EMG-Based Objective Function for Human-in-the-Loop Optimization

An EMG-Based Objective Function for Human-in-the-Loop Optimi...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： María Alejandra Díaz Sander De Bock Philipp Beckerle Jan Babič Tom Verstraten Kevin De Pauw Human Physiology and Sports Physiotherapy Research Group Brussels Human Robotic Research Center Vrije Universiteit Brussel Brussels Belgium Department of Electrical Engineering and the Department of Artificial Intelligence in Biomedical Engineering Institute of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Automation Biocybernetics and Robotics Laboratory for Neuromechanics and Biorobotics Jožef Stefan Institute Ljubljana Slovenia Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Robotics and Multibody Mechanics Research Group Vrije Universiteit Brussel and Flanders Make Brussels Belgium

Advancements in wearable robots aim to improve the users' motion, performance, and comfort by optimizing, mainly, energetic cost (EC). However, EC is a noisy measurement with a physiological delayed response that requires long evaluation periods and wearing an uncomfortable mask. This study aims to estimate and minimize an EMG-based objective function that describes the natural energetic expenditure of individuals walking. This objective is assessed by combining multiple electromyography (EMG) variables from the EMG intensity and muscle synergies. To evaluate this objective function simply and repeatedly, we prescribed step frequency (SF) via a metronome and optimized this frequency to minimize muscle activity demands. Further, a linear mixed-effects model was fitted for EC, with the EMG variables as fixed-effects and a random intercept that varies by participant. After the model was fitted to the data, a cubic polynomial was used to identify the optimal SF that reduces the overall EMG-based objective function. Our analysis outlines that the proposed objective function is comparable to the EC during walking, the primary objective function used in human-in-the-loop optimization. Thus, this EMG-based objective function could be potentially used to optimize wearable robots and improve human-robot interaction.

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Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility

Online 3D Frontier-Based UGV and UAV Exploration Using Direc...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Jason Williams Shu Jiang Matthew O’Brien Glenn Wagner Emili Hernandez Mark Cox Alex Pitt Ron Arkin Nicolas Hudson CSIRO Data61 Robotics and Autonomous Systems Group Georgia Tech Mobile Robot Laboratory Emesent

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

While robots have long been proposed as a tool to reduce human personnel's exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.

关键词： Exploration Robot Environments Three Dimensions drone honey bees Unmanned Aerial Vehicle subsurface environment Scalability underground Danger

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Monocular visual autonomous landing system for quadcopter drones using software in the loop

arXiv

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arXiv 2021年

作者： Saavedra-Ruiz, Miguel Pinto-Vargasy, Ana Maria Romero-Cano, Victor Mila - Quebec Institute of Artificial Intelligence Universite de Montreal Canada Alternova Tech SAS Medellín Colombia Robotics and Autonomous Systems Laboratory Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia

autonomous landing is a capability that is essential to achieve the full potential of multi-rotor drones in many social and industrial applications. The implementation and testing of this capability on physical platforms is risky and resourceintensive;hence, in order to ensure both a sound design process and a safe deployment, simulations are required before implementing a physical prototype. This paper presents the development of a monocular visual system, using a softwarein- the-loop methodology, that autonomously and efficiently lands a quadcopter drone on a predefined landing pad, thus reducing the risks of the physical testing stage. In addition to ensuring that the autonomous landing system as a whole fulfils the design requirements using a Gazebo-based simulation, our approach provides a tool for safe parameter tuning and design testing prior to physical implementation. Finally, the proposed monocular vision-only approach to landing pad tracking made it possible to effectively implement the system in an F450 quadcopter drone with the standard computational capabilities of an Odroid XU4 embedded processor. Copyright © 2021, The Authors. All rights reserved.

关键词： Simulation platform

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State and parameter estimation of suspended load using quadrotor onboard sensors

State and parameter estimation of suspended load using quadr...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Vicko Prkačin Ivana Palunko Ivan Petrović Laboratory for intelligent autonomous systems (LARIAT) Department of Electrical and Computer Engineering University of Dubrovnik Dubrovnik Croatia Laboratory for autonomous systems and mobile robotics (LAMOR) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In this paper, we address the problem of state and parameter estimation of a suspended load using quadrotor onboard sensors. Flying with a suspended load alters the quadrotor flight dynamics, sometimes to a large extent, making it a challenging and hazardous task. Monitoring the state of the suspended load is vital for safe flight operations while parameter estimation decouples the control design from specific parameter-dependent solutions. We take advantage of the fact that the forces and torques the suspended load exerts on the quadrotor can be detected in the aircraft IMU measurements as a low frequency harmonic. Thus, by combining the available measurements and system mass we are able to estimate the state of the suspended load. Since our approach stems from understanding the aircraft-load interaction, we start off by delineating the full system model of the quadrotor with a suspended load. To isolate the natural frequency of the suspended load and determine the length of suspension cable, we employ the Fast Fourier Transform (FFT). The proposed estimation algorithms are validated through extensive numerical simulations and experimentally.

关键词： Estimation Aircraft Load modeling Sensors Atmospheric modeling Parameter estimation Numerical models

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Walking on TacTip toes: A tactile sensing foot for walking robots

Walking on TacTip toes: A tactile sensing foot for walking r...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Elizabeth A. Stone Nathan F. Lepora David A.W. Barton EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory University of Bristol UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Little research into tactile feet has been done for walking robots despite the benefits such feedback could give when walking on uneven terrain. This paper describes the development of a simple, robust and inexpensive tactile foot for legged robots based on a high-resolution biomimetic TacTip tactile sensor. Several design improvements were made to facilitate tactile sensing while walking, including the use of phosphorescent markers to remove the need for internal LED lighting. The usefulness of the foot is verified on a quadrupedal robot performing a beam walking task and it is found the sensor prevents the robot falling off the beam. Further, this capability also enables the robot to walk along the edge of a curved table. This tactile foot design can be easily modified for use with any legged robot, including much larger walking robots, enabling stable walking in challenging terrain.

关键词： Legged locomotion Tactile sensors Robot sensing systems Sensors Task analysis Robots Foot

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RAVE CHECKLIST: RECOMMENDATIONS FOR OVERCOMING CHALLENGES IN RETROSPECTIVE SAFETY STUDIES OF AUTOMATED DRIVING systems

arXiv

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arXiv 2024年

作者： Scanlon, John M. Teoh, Eric R. Kidd, David G. Kusano, Kristofer D. Bärgman, Jonas Chi-Johnston, Geoffrey Di Lillo, Luigi Favaro, Francesca Flannagan, Carol Liers, Henrik Lin, Bonnie Lindman, Magdalena McLaughlin, Shane Perez, Miguel Victor, Trent Waymo LLC 1600 Amphitheatre Parkway Mountain ViewCA United States Insurance Institute for Highway Safety ArlingtonVA United States Chalmers University of Technology Sweden Cruise LLC CA United States Swiss Reinsurance Company Ltd Reinsurance Solutions Switzerland Autonomous Systems Laboratory Stanford University CA United States MA United States MI United States Germany If P&C Insurance Sweden Torc Robotics BlacksburgVA United States BlacksburgVA United States

Objective The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety impact evaluations, whereby the observational data from both an ADS and a representative benchmark fleet are compared to quantify safety performance. Methods In January 2024, a working group of experts across academia, insurance, and industry came together in Washington, DC to discuss the current and future challenges in performing such evaluations. A subset of this working group then met, virtually, on multiple occasions to produce this paper. Results This paper presents the RAVE (Retrospective Automated Vehicle Evaluation) checklist, a set of fifteen recommendations for performing and evaluating retrospective ADS performance comparisons. The recommendations are centered around the concepts of (1) quality and validity, (2) transparency, and (3) interpretation. Conclusion Over time, it is anticipated there will be a large and varied body of work evaluating the observed performance of these ADS fleets. Establishing and promoting good scientific practices benefits the work of stakeholders, many of whom may not be subject matter experts. This working group's intentions are to: i) strengthen individual research studies and ii) make the at-large community more informed on how to evaluate this collective body of work. © 2024, CC BY-NC-SA.

关键词： Vehicle safety

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ODYSSEE: Oyster Detection Yielded by Sensor systems on Edge Electronics

arXiv

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arXiv 2024年

作者： Lin, Xiaomin Mange, Vivek Suresh, Arjun Neuberger, Bernhard Palnitkar, Aadi Campbell, Brendan Williams, Alan Baxevani, Kleio Mallette, Jeremy Vera, Alhim Vincze, Markus Rekleitis, Ioannis Tanner, Herbert G. Aloimonos, Yiannis Maryland Robotics Center University of Maryland College ParkMD20742 United States Center for Autonomous and Robotic Systems University of Delaware NewarkDE19711 United States Automation and Control Institute TU Wien Vienna1040 Austria School of Marine Science and Policy University of Delaware LewesDE19958 United States University of Maryland Center for Environmental Science Horn Point Laboratory CambridgeMD21613 United States College of Engineering and Applied Science University of Cincinnati OH45221 United States Montreal QC Canada

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with photorealistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform;Aqua2, an autonomous Underwater Vehicle (AUV), achieving a state-of-the-art 0.657 mAP@50 for oyster detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Shellfish

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Learning to Live Life on the Edge: Online Learning for Data-Efficient Tactile Contour Following

Learning to Live Life on the Edge: Online Learning for Data-...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Elizabeth A. Stone Nathan F. Lepora David A.W. Barton EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) Bristol Robotics Laboratory University of Bristol UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Tactile sensing has been used for a variety of robotic exploration and manipulation tasks but a common constraint is a requirement for a large amount of training data. This paper addresses the issue of data-efficiency by proposing a novel method for online learning based on a Gaussian Process Latent Variable Model (GP-LVM), whereby the robot learns from tactile data whilst performing a contour following task thus enabling generalisation to a wide variety of tactile stimuli. The results show that contour following is successful with comparatively little data and is robust to novel stimuli. This work highlights that even with a simple learning architecture there are significant advantages to be gained in efficient and robust task performance by using latent variable models and online learning for tactile sensing tasks. This paves the way for a new generation of robust, fast, and data-efficient tactile systems.

关键词： Training data Gaussian processes Robot sensing systems Data models Sensors Task analysis Intelligent robots

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Grand Challenges in the Verification of autonomous systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of autonomous systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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