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IEEE-RAS International Conference on Humanoid Robots

作者： Adnan Saood Yang Liu Heng Zhang Adriana Tapus U2IS ENSTA Paris Institut Polytechnique de Paris Paris France LTCI Laboratory Telecom Paris Institut Polytechnique de Paris Paris France Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris France

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Using non-verbal cues such as breathing and heartbeat signals as media for Human-Robot Interaction (HRI) enables subtle exchanges between humans and robots beyond explicit verbal or visual cues. This study describes the development of a novel pneumatic haptic interface that replicates human breathing and heartbeat animations for a humanoid robot and investigates its application for enhancing non-verbal HRI. The experimental results validate the effectiveness of this haptic device and highlight the significance of non-verbal cues in conveying emotional states. Introducing the haptic interface as a means for delivering involuntary, non-verbal emotional signals significantly enhances human-robot interaction compared to using gestures alone.

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Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy

Recalibrating the KITTI Dataset Camera Setup for Improved Od...

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European Conference on Mobile Robots (ECMR)

作者： Igor Cvišić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

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Dynamics Modeling using Visual Terrain Features for High-Speed autonomous Off-Road Driving

arXiv

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arXiv 2024年

作者： Gibson, Jason Alavilli, Anoushka Tevere, Erica Theodorou, Evangelos A. Spieler, Patrick Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30332 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States

Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encode fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://***/watch?v=dycTXxEosMk © 2024, CC BY.

关键词： Off road vehicles

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics (ACFM 2023)

作者： X. -H. Xu J. -C. Liu H. -R. Hou Q. -H. Meng Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin 300072 People's Republic of China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin 300072 People's Republic of China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k- ε turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles.

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A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems

arXiv

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arXiv 2022年

作者： Prudencio, Rafael Figueiredo Maximo, Marcos R.O.A. Colombini, Esther Luna The Laboratory of Robotics and Cognitive Systems Computing Institute University of Campinas Campinas13083-852 Brazil The Autonomous Computational Systems Laboratory Computer Science Division Aeronautics Institute of Technology São José dos Campos12228-900 Brazil

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field. © 2022, CC BY.

关键词： Reinforcement learning

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Vehicle Perception from a Single Image for autonomous Driving Using Deformable Model Representation and Deep Learning

SSRN

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SSRN 2022年

作者： Lu, Feixiang Liu, Zongdai Song, Xibin Zhou, Dingfu Li, Wei Miao, Hui Liao, Miao Zhang, Liangjun Zhou, Bin Yang, Ruigang Manocha, Dinesh State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Robotics and Autonomous Driving Laboratory Baidu Research China University of Maryland College Park United States Peng Cheng Laboratory Shenzhen China

We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving. Our approach combines the strengths of deep learning and the elegance of traditional techniques from part-based deformable model representation to produce high-quality 3D models in the presence of severe occlusions. We present a new part-based deformable vehicle model that is used for instance segmentation and automatically generate a dataset that contains dense correspondences between 2D images and 3D models. We also present a novel end-to-end deep neural network to predict dense 2D/3D mapping and highlight its benefits. Based on the dense mapping, we are able to compute precise 6-DoF poses and 3D reconstruction results at almost interactive rates on a commodity GPU. We have integrated these algorithms with an autonomous driving system. In practice, our method outperforms the state-of-the-art methods for all major vehicle parsing tasks: 2D instance segmentation by 4.4 points (mAP), 6-DoF pose estimation by 9.11 points, and 3D detection by 1.37. Moreover, we will release all of the source code, dataset, and the trained model on Github. © 2022, The Authors. All rights reserved.

关键词： autonomous vehicles

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Mathematical modeling of stable position of manipulator mounted on unmanned aerial vehicle 15th

Mathematical modeling of stable position of manipulator moun...

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15th International Conference on Electromechanics and robotics Zavalishin’s Readings, ERZR 2020

作者： Nguyen, Vinh Vu, Quyen Ronzhin, Andrey Department of Electromechanics and Robotics SUAI Bolshaya Morskaya Str. 67 Saint-Petersburg190000 Russia Laboratory of Autonomous Robotic Systems SPIIRAS 14-th Line of V.I. 39 Saint-Petersburg199178 Russia

ISBN: (纸本)9789811555794

The stability of the manipulator is important issue especially in the domain of unmanned aerial vehicle (UAV). Any movements of manipulator greatly affects UAV stability. In particular, the horizontal shift of the center of gravity requires of the UAV the powerful controller, that change forced the propellers of quadrotor to bring the UAV to a stable state. In this paper, we have studied the movements of manipulator and its center of gravity at least horizontally when UAV is in hover mode. As a result, we developed mathematical model and software for calculation of deflection angles between successive links of the manipulator, which provides deviation of the center of mass (COM) of the UAV horizontally less than 1 mm. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Manipulators

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Human Localization in Warehouse Environments based on a Wearable Camera Sensor Suite and Dynamic Ultra-Wide Band Nodes

Human Localization in Warehouse Environments based on a Wear...

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International Conference on Advanced robotics (ICAR)

作者： Goran Popović Igor Cviić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665436854

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments.

关键词： Location awareness Visualization Robot vision systems Cameras Distance measurement Safety Reliability

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Event-Based Kalman Filtering Exploiting Correlated Trigger Information

Event-Based Kalman Filtering Exploiting Correlated Trigger I...

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International Conference on Information Fusion

作者： Benjamin Noack Clemens Ö hl Uwe D. Hanebeck Autonomous Multisensor Systems Group Institute for Intelligent Cooperating Systems Otto von Guericke University Magdeburg Magdeburg Germany Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

In networked estimation architectures, event-based sensing and communication can contribute to a more efficient resource allocation in general, and improved utilization of communication resources, in particular. In order to tap the full potential of event-based scheduling, the design of transmission triggers and estimators need to be closely coupled while two directions are promising: First, the remote estimator can exploit the absence of transmissions and translate it into implicit information about the sensor data. Second, an intelligent trigger mechanism at the sensor that predicts future sensor readings can decrease transmission rates while rendering the implicit information more valuable. Such an intelligent trigger has been developed in a recent paper based on a Finite Impulse Response filter, which requires the sensor to transmit an additional estimate alongside the measurement. In the present paper, the communication demand is further reduced by only transmitting the estimate. The remote estimator exploits correlations to incorporate the received information. In doing so, the estimation quality is also improved, which is confirmed by simulations.

关键词： Finite impulse response filters Computational modeling Estimation Rendering (computer graphics) Information filters Time measurement Sensors

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Universal Adaptive Fault-Tolerant Control of a Multicopter UAV ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9340-9347页

作者： Duc-Tien Nguyen David Saussié Lahcen Saydy Mobile Robotics and Autonomous Systems Laboratory Polytechnique Montreal Canada

This paper presents a universal adaptive fault-tolerant control (FTC) design for multicopter unmanned aerial vehicles (UAVs). The proposed architecture consists of a two-loop control structure: a fault-tolerant controller generates normalized virtual control inputs to track the desired trajectory subject to actuator faults, and an adaptive augmentation controller deals with system uncertainties and also balances the design requirements for specific platform. The FTC approach is based on gain-scheduling control in the framework of structured H ∞ synthesis. In order to implement the overall control system on most types of multicopter UAVs, an adaptive mapping algorithm is proposed. High fidelity simulations and experimental results, performed on various multicopters with different payload and configuration, show the effectiveness and robustness of the proposed approach in accommodating different levels of actuator degradation including total failures of the rotors as well as unknown mass and inertia, all using a single controller with fixed coefficients.

关键词： Multicopter Fault-Tolerant Control Robust Adaptive Control Gain-Scheduling Structured H-Infinity Control

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