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arXiv 2024年

作者： Walker, Kyle L. Della Santina, Cosimo Giorgio-Serchi, Francesco The Institute for Integrated Micro and Nano Systems University of Edinburgh Edinburgh United Kingdom The Cognitive Robotics Department TU Delft Delft Netherlands The National Robotarium Heriot Watt University Boundary Road North Edinburgh United Kingdom

Inspired by the octopus and other animals living in water, soft robots should naturally lend themselves to underwater operations, as supported by encouraging validations in deep water scenarios. This work deals with equipping soft arms with the intelligence necessary to move precisely in wave-dominated environments, such as shallow waters where marine renewable devices are located. This scenario is substantially more challenging than calm deep water since, at low operational depths, hydrodynamic wave disturbances can represent a significant impediment. We propose a control strategy based on Nonlinear Model Predictive Control that can account for wave disturbances explicitly, optimising control actions by considering an estimate of oncoming hydrodynamic loads. The proposed strategy is validated through a set of tasks covering set-point regulation, trajectory tracking and mechanical failure compensation, all under a broad range of varying significant wave heights and peak spectral periods. The proposed control methodology displays positional error reductions as large as 84% with respect to a baseline controller, proving the effectiveness of the method. These initial findings present a first step in the development and deployment of soft manipulators for performing tasks in hazardous water environments. © 2024, CC BY.

关键词： Disturbance rejection

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A Probabilistic Approach for Complete Coverage Path Planning with low-cost systems

A Probabilistic Approach for Complete Coverage Path Planning...

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European Conference on Mobile Robots (ECMR)

作者： Nils Rottmann Robin Denz Ralf Bruder Elmar Rueckert Institute for Robotics and Cognitive Systems University of Luebeck Luebeck Germany

Domestic robots, such as vacuum cleaners or lawn mowers, are mostly based on a low-cost design to make them affordable for the consumer. This often results in such robots being equipped with only simple sensors, such as in-/outside area detectors for lawn mowers. Intelligent navigation and planning strategies, however, usually require additional sensors like LiDAR sensors, cameras or time of flight sensors. Thus, there is a lack of intelligent approaches for the complete coverage of the workspace under consideration of only minimal sensing *** this work, we propose a probabilistic planning method for low-cost robots with limited sensing capabilities to completely cover an enclosed environment. Our planning approach thereby utilizes Monte Carlo Localization for estimating coverage probabilities based on the particle distribution. These coverage probabilities are stored in a grid map on the basis of which an intelligent path planning approach determines the next locations to be visited. We demonstrate our approach in different simulation scenarios for a realistic autonomous lawn mower with only in-/outside area detection capabilities. As comparison benchmark we use the common random walk mowing pattern.

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Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series

Ultrasonic Aspirator for Tissue Contact Detection: An Online...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Niclas Bockelmann Bennet Kahrs Denise Kesslau Daniel Schetelig Matteo Mario Bonsanto Steffen Buschschlüter Floris Ernst Institute for Robotics and Cognitive Systems University of Lübeck Lübeck Germany Söring GmbH Quickborn Germany Department of Neurosurgery University Hospital Schleswig-Holstein Lübeck Germany

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient’s neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning *** decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

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Model Predictive Wave Disturbance Rejection for Underwater Soft Robotic Manipulators

Model Predictive Wave Disturbance Rejection for Underwater S...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Kyle L. Walker Cosimo Della Santina Francesco Giorgio-Serchi Institute for Integrated Micro and Nano Systems University of Edinburgh Edinburgh U.K. National Robotarium Boundary Road North Heriot Watt University Edinburgh U.K. Cognitive Robotics Department TU Delft Delft Netherlands

Inspired by the octopus and other animals living in water, soft robots should naturally lend themselves to underwater operations, as supported by encouraging validations in deep water scenarios. This work deals with equipping soft arms with the intelligence necessary to move precisely in wave-dominated environments, such as shallow waters where marine renewable devices are located. This scenario is sub-stantially more challenging than calm deep water since, at low operational depths, hydrodynamic wave disturbances can represent a significant impediment. We propose a control strategy based on Nonlinear Model Predictive Control that can account for wave disturbances explicitly, optimising control actions by considering an estimate of oncoming hydrodynamic loads. The proposed strategy is validated through a set of tasks covering set-point regulation, trajectory tracking and mechanical failure compensation, all under a broad range of varying significant wave heights and peak spectral periods. The proposed control methodology displays positional error reductions as large as 84% with respect to a baseline controller, proving the effectiveness of the method. These initial findings present a first step in the development and deployment of soft manipulators for performing tasks in hazardous water environments.

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Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches 21

Privacy-preserving Biometric-driven Data for Student Identit...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Fidas, Christos Belk, Marios Portugal, David Pitsillides, Andreas University of Patras Department of Electrical and Computer Engineering Greece Cognitive Ux GmbH Germany University of Coimbra Institute of Systems and Robotics Portugal University of Cyprus Department of Computer Science Cyprus

ISBN: (纸本)9781450383677

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data. © 2021 Owner/Author.

关键词： Blockchain

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Contact Stability Control of Stepping Over Partial Footholds Using Plantar Tactile Feedback

Contact Stability Control of Stepping Over Partial Footholds...

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

作者： J. Rogelio Guadarrama-Olvera Shuuji Kajita Fumio Kanehiro Gordon Cheng Institute for Cognitive Systems Technical University of Munich Munich Germany Dept. of Robotic Science and Technology Chubu University Aichi Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a novel method to keep stable contact and balance while stepping over partial footholds for biped humanoid robots with flat feet. We exploit plantar tactile feedback to detect the geometry of the terrain and reconstruct online the new supporting polygon after landing every step. Plantar tactile feedback detects early contacts to stop the swing foot motion. Then we compute the convex hull of the cluster of contact points detected by distributed normal force sensors over the foot soles. The centroid of the supporting polygon is then used for retargeting the reference ZMP and DCM positions. Finally, the supporting polygon is used to define constraints for ZMP balance feedback control. These methods were implemented in two biped humanoid robots running different walking controllers.

关键词： Legged locomotion Ankle Shape Tactile sensors Humanoid robots Kinematics Motors Force sensors Robots Foot

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Robust tube-based LPV-MPC for autonomous lane keeping

arXiv

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arXiv 2022年

作者： Nezami, Maryam Abbas, Hossam Seddik Nguyen, Ngoc Thinh Schildbach, Georg Institute for Electrical Engineering in Medicine University of Lübeck Lübeck Germany Institute for Robotics and Cognitive Systems University of Lübeck Lübeck Germany

This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics. Therefore, the control architecture comprises two subsequent controllers. A longitudinal model predictive control (MPC) makes the vehicle track the desired longitudinal speeds that are assumed to be generated by a speed planner. The longitudinal speeds are then passed to a lateral MPC for lane keeping. Due to the dependence of the lateral dynamics on the longitudinal speed, they are represented in a linear parameter-varying (LPV) form, where its scheduling parameter is the longitudinal speed of the vehicle. In order to deal with the imprecise information of the future longitudinal speed (the scheduling parameter), a bound of uncertainty is considered around the nominal trajectory of the future longitudinal velocities. Then, a tube-based LPV-MPC is adopted to control the lateral dynamics for attaining the lane keeping goal. In the end, the effectiveness of the proposed methods is illustrated by carrying out simulation tests. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning 20

A high-accuracy, low-budget Sensor Glove for Trajectory Mode...

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20th International Conference on Advanced robotics, ICAR 2021

作者： Denz, Robin Demirci, Rabia Cansev, M. Ege Bliek, Adna Beckerle, Philipp Rueckert, Elmar Rottmann, Nils Institute For Robotics and Cognitive Systems University of Luebeck Luebeck23562 Germany Chair of Autonomous Systems and Mechatronics Friedrich-Alexander-University Department of Electrical Engineering Erlangen-Nürnberg Germany Chair of Cyber-Physical-Systems Montanuniversität Leoben Leoben Austria

ISBN: (纸本)9781665436847

Sensor gloves are gaining importance in tracking hand and finger movements in virtual reality applications as well as in scientific research. They introduce an unrestricted way of capturing motion without the dependence on direct line of sight as for visual tracking systems. With such sensor gloves, data of complex motion tasks can be recorded and used for modeling probabilistic trajectories or teleoperation of robotic arms. While a multitude of sensor glove designs relying on different functional principles exist, these approaches require either sensitive calibration and sensor fusion methods or complex manufacturing processes. In this paper, we propose a low-budget, yet accurate sensor glove system that uses flex sensors for fast and efficient motion tracking. We evaluate the performance of our sensor glove, such as accuracy and latency, and demonstrate the functionality by recording motion data for learning probabilistic movement models. © 2021 IEEE.

关键词： Budget control

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Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

arXiv

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arXiv 2022年

作者： Buyukgoz, Sera Grosinger, Jasmin Chetouani, Mohamed Saffiotti, Alessandro SoftBank Robotics Europe Paris France Sorbonne University Institute for Intelligent Systems and Robotics CNRS UMR 7222 Paris France AASS Cognitive Robotic Systems Lab School of Science and Technology Orebro University Orebro Sweden

Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human's intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forcasted. In this paper, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to take into account a broader variety of aspects that are needed for proactivity. © 2022, CC BY.

关键词： Human robot interaction

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Simulation of Dynamic Environments for SLAM

arXiv

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arXiv 2023年

作者： Bonetto, Elia Xu, Chenghao Ahmad, Aamir Max Planck Institute for Intelligent Systems Tübingen Germany Institute of Flight Mechanics and Controls University of Stuttgart Stuttgart Germany Faculty of Mechanical Maritime and Materials Engineering Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Simulation engines are widely adopted in robotics. However, they lack either full simulation control, ROS integration, realistic physics, or photorealism. Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. However, when focusing on vision applications, there is usually a lack of information like sensor measurements or time continuity. On the other hand, simulations for most robotics tasks are performed in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we introduced in our previous work a fully customizable framework for generating realistic animated dynamic environments (GRADE) [1]. We use GRADE to generate an indoor dynamic environment dataset and then compare multiple SLAM algorithms on different sequences. By doing that, we show how current research over-relies on known benchmarks, failing to generalize. Our tests with refined YOLO and Mask R-CNN models provide further evidence that additional research in dynamic SLAM is necessary. The code, results, and generated data are provided as open-source at https://***/grade-rr. Copyright © 2023, The Authors. All rights reserved.

关键词： Target tracking

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