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arXiv 2021年

作者： Cvišić, Igor Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics 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 benchmark 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 proposed 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. © 2021, CC BY.

关键词： Stereo image processing

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Learn to Coordinate: a Whole-Body Learning from Demonstration Framework for Differential Drive Mobile Manipulators

Learn to Coordinate: a Whole-Body Learning from Demonstratio...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuqiang Yang Darong Huang Chen Chen Chao Zeng Yanong He Chenguang Yang Automation Science and Engineering South China University of Technology Guangzhou China Application Innovate Laboratory Huawei Technologies Co. Ltd Shenzhen China Technical Aspects of Multimodal Systems Universitat Hamburg Hamburg Germany Bristol Robotics Laboratory University of the West of England Bristol UK

This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github † † https://***/yuqiang-yang/SMC2023-Video.

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Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

Demonstrating Autonomous 3D Path Planning on a Novel Scalabl...

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

作者： Eric Sihite Filip Slezak Ioannis Mandralis Adarsh Salagame Milad Ramezani Arash Kalantari Alireza Ramezani Morteza Gharib The Department of Aerospace Engineering California Institute of Technology Pasadena USA Department of Electrical and Computer Engineering The Silicon Synapse Labs Northeastern University Boston USA The Robotics and Autonomous Systems DATA61 CSIRO Brisbane QLD Australia The Jet Propulsion Laboratory Pasadena USA

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

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Development and Initial Evaluation of a Multi-Purpose Spraying Robot Prototype

Development and Initial Evaluation of a Multi-Purpose Sprayi...

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International Conference on Advanced robotics (ICAR)

作者： Christos Chrysoulakis John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory School of Engineering Hellenic Mediterranean University Heraklion Crete Greece

ISBN: (纸本)9781665436854

We present a versatile mobile robotic sprayer system, primarily aimed for pesticide application in greenhouses and for disinfection of indoor spaces. The prototype is built around a skid-steered wheeled platform driven by a pair of DC motors, and is equipped with an electronically controlled sprayer unit that is mounted on a pan/tilt robot arm for directing the spray. The robot can be operated either manually or in AGV mode, utilizing a magnetic guide sensor to track a predefined path, along with a depth camera and ultrasonic sensors for obstacle detection. The robot's control architecture employs a central Single Board Computer running ROS, with peripheral functions allocated to three 8-bit microcontrollers. Closed loop flow regulation schemes have been developed to provide precision variable application rate of the sprayed agents, combined with motion control strategies of the pan/tilt unit to implement rectangular coverage spraying. The robot's capabilities are demonstrated through a series of tests, involving laboratory experiments as well as application scenarios trials.

关键词： Robot control Robot vision systems Spraying Prototypes Robot sensing systems Regulation Mobile robots

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Attitude observers for three-vehicle heterogeneous formations based on the Lagrange-d’Alembert principle

Attitude observers for three-vehicle heterogeneous formation...

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European Control Conference (ECC)

作者： Pedro Cruz Pedro Batista Amit Sanyal Institute for Systems and Robotics Laboratory for Robotics and Engineering Systems at Instituto Superior Técnico Universidade de Lisboa Portugal Syracuse University Syracuse NY United States

This paper considers the problem of attitude estimation in three-vehicle heterogeneous formations with no line of sight between two of the vehicles. Each vehicle measures different inertial reference vectors and directions to other vehicles. The relative direction between the two vehicles with no line of sight cannot be measured. Moreover, rate gyros measure the angular velocity of each vehicle. An attitude observer is designed based on the Lagrange-d’Alembert principle of variational mechanics, considering only kinematic models. This design is driven by the angular velocity measurement and a reconstructed attitude computed from the direction measurements. The attitude reconstruction follows a deterministic algorithm, which has a unique solution under appropriate assumptions. The attitude observer is locally exponentially stable and the estimation error is shown to converge to zero for almost all initial conditions. The discrete-time form of the observer is obtained for practical implementation. Lastly, numerical simulations validate the stability and convergence characteristics of the observer.

关键词： Europe Kinematics Observers Position measurement Numerical simulation Angular velocity Stability analysis

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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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Organic Heterojunction Enabled Near 1% H2o2 Photoproduction with Ultrafast Charge Separation

SSRN

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SSRN 2024年

作者： Liu, Jingjing Hu, Qiushi Liu, Shang Lin, Meng Zhang, Xiaosa Xie, Ying Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Key Laboratory for Rare Earth Chemistry and Application of Liaoning Province College of Science Shenyang University of Chemical Technology Liaoning Shenyang China

Hydrogen peroxide (H2O2) finds wide application in disinfection, chemical production, and bleaching owing to its sustainability, stability, and ease of storage compared to hydrogen. However, the current industrial engineering practices for producing photocatalysts used in H2O2 generation face challenges such as low yields with organic catalysts and high costs associated with inorganic catalysts. In light of these challenges, we investigate a viable method utilizing a simple condensation reflux technique to synthesize pure organic heterojunction photocatalysts featuring a π-π stacked structure derived from graphitized carbon nitride (melem) and meso-tetrakis (4-carboxyphenyl) porphyrin. This approach enables efficient H2O2 production from water, seawater, and oxygen without the necessity of sacrificial agents. In this study, the synthesized catalyst demonstrates an impressive H2O2 production rate of 106 mmol/h.g.L under seawater conditions and 77.67 mmol/h.g.L under pure water conditions in ambient air. Following a reaction time of 4.5 hours, the H2O2 concentration reaches 0.88 wt% with a volume of 5.0 mL. Transient absorption spectroscopy validates the formation of a heterojunction between melem and porphyrin. Notably, this heterogeneous catalyst efficiently facilitates water oxidation and oxygen reduction while suppressing both H2O2 decomposition and subsequent photodecomposition reactions, thereby addressing the challenges associated with organic catalyst yield. © 2024, The Authors. All rights reserved.

关键词： Absorption spectroscopy

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Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

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International Conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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Ultrafast dynamics in perovskite-based optoelectronic devices

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Cell Reports Physical Science 2023年第9期4卷

作者： Gong, Shaokuan Huang, Yuling Yu, Xuemeng Hu, Qiushi Liu, Jingjing Meng, Jiazhi Wen, Yifan Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen 518055 China

Halide-perovskite-based materials are rising stars for optoelectronic applications. The key for optoelectronic device development is the photogenerated carrier dynamics, which directly describes the motion of photogenerated electrons and holes. A fundamental understanding of the carrier dynamics can help researchers gain deeper insight into the light-matter interaction. The generation and movement of charge carriers can be on the order of femto- and pico-second (ultrafast) timescales, which pose challenges for detection methods. Recent development in transient spectroscopic techniques enable the understanding of charge-carrier dynamics. In this perspective, we introduce several ultrafast spectroscopic methods that investigate total carrier dynamics, hot-carrier cooling dynamics, interfacial carrier transport dynamics, and carrier spin relaxation dynamics in perovskite optoelectronics. Through these investigations, we provide our perspective on the future development of perovskite-based optoelectronics. © 2023 The Author(s)

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