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10th robotics: science and systems, rss 2014

the proceedings contain 57 papers. the special focus in this conference is on robotics: science and systems. the topics include: Automatic generation of reduced CPG control networks for locomotion of arbitrary modular robot structures;tell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions;Hierarchical Semantic Labeling for Task-Relevant RGB-D Perception;LOAM: Lidar Odometry and Mapping in Real-time;Self-Calibration and Visual SLAM with a Multi-Camera System on a Micro Aerial Vehicle;articulated Pose Estimation via Over-parametrization and Noise Projection;an Analysis of Deceptive Robot Motion;online Trajectory Planning in Dynamic Environments for Surgical Task Automation;correct High-level Robot Behavior in Environments with Unexpected Events;six-Degrees-of-Freedom Remote Actuation of Magnetic Microrobots;combining 3D Shape, Color, and Motion for Robust Anytime Tracking;Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs;manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping;the Multi-Agent Navigation Transformation: Tuning-Free Multi-Robot Navigation;a Novel Type of Compliant, Underactuated Robotic Hand for Dexterous Grasping;state Representation Learning in robotics: Using Prior Knowledge about Physical Interaction;enhanced 3D Kinematic Modeling of Wheeled Mobile Robots;fully Decentralized Task Swaps with Optimized Local Searching;modeling robot discrete movements with state-varying stiffness and damping: A framework for integrated motion generation and impedance control;scene Signatures: Localised and Point-less Features for Localisation;asking for Help Using Inverse Semantics;modeling and Controlling Friendliness for An Interactive Museum Robot;control of Robotic Mobility-On-Demand systems: a Queueing-theoretical Perspective;bio-Artificial Synergies for Grasp Posture Control of Supernumerary Robotic Fingers;DART: Dense Articulated Real-Time Tracking.

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Modular Platform for Teaching robotics 10th

Modular Platform for Teaching Robotics

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Proceedings of the 10th Machine Intelligence and Digital Interaction Conference, MIDI 2022

作者： Nagajek, Damian Rąpala, Michal Woloszyn, Kamil Turchan, Krzysztof Piotrowski, Krzysztof Space Research Centre of the Polish Academy of Sciences Zielona Góra Poland Germany

ISBN: (纸本)9783031376481

the Fourth Industrial Revolution causes changes in the economy and shifts the job market demand towards workforce with high technical skills. To keep an undisturbed economic growth we have to encourage more young people to develop competences in STEAM (science, technology, engineering, arts and mathematics). this has met with a response from some education systems which have prepared special programmes focusing on developing technical skills among students. One of the desired field is robotics, which involves constructing and programming. We have already conducted some workshops for high school students in this subjects and we would like to find the correct teaching tools to attract primary school students. Our idea is to create a modular platform, which elements could be used as black boxes, to teach robotics to young children. We have noticed that Arduino based kits are a bit too complicated and we decided to test a LEGO Technic set equipped with an external microcontroller. We have verified the interest level of children and the difficulty and time needed for a teacher to master the whole teaching platform. According to our study, LEGO attracts students much more than Arduino and is easier in operation and less time consuming during classes for teachers. © 2023, the Author(s).

关键词： Microcontrollers

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robotics Education To and through College 10th

Robotics Education To and Through College

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10th International Conference on robotics in Education (RiE)

作者： Page, Brian R. Ziaeefard, Saeedeh Knop, Lauren Rastgaar, Mo Mahmoudian, Nina Purdue Univ W Lafayette IN 47907 USA Ohio State Univ Columbus OH 43210 USA Michigan Technol Univ Houghton MI 49931 USA

ISBN: (纸本)9783030269456;9783030269449

robotics education has made great strides to enable the next generation of engineers and workers with early education and outreach. this early education effort is able to engage students and promote interest, however an integrated pathway to and through college is needed. this pathway needs to build upon early experiences with opportunities to advance across age groups. this paper presents the authors experience developing robotics curriculum across age groups. Middle and high school education has been implemented in a summer camp environment utilizing two co-robotic platforms, a water sensing robot called GUPPIE and an assistive robot named Neu-pulator, engaging 201 total students between Summer 2014-2017. the university course is a senior level technical elective introducing autonomous systems through a mobile robotic platform, a smart car, with 72 total students in Spring 2017 and 2018. In this work, the survey results gathered from Summer 2017 pre-college and Spring 2018 college level activities are presented. Overall observations and lessons learned across age groups are also discussed to better create a pathway from young learners to practicing engineers. the key to success of robotics programs at any age are hands-on, exciting activities with sufficient expert support so that students are able to learn in a frustration free environment.

关键词： Project-based learning and robotics robotics curricula robotics education Marine robots Assistive robots Mobile robots

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Binocular depth estimation using convolutional neural network with siamese branches

Binocular depth estimation using convolutional neural networ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. the first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. the second is a fully convolutional deep network, which reconstructions the depth map to original resolution. the two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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A novel port-Hamiltonian based design of stabilizing controller for DC-DC buck converters 10th

A novel port-Hamiltonian based design of stabilizing control...

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10th International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2017

作者： Tomassini, Juan Donaire, Alejandro Junco, Sergio LAC Laboratorio de Automatización y Control Departamento de Control Escuela de Ingeniería Electrónica Facultad de Ciencias Exactas e Ingeniería Universidad Nacional de Rosario Argentina Institute for Future Environments School of Electrical Engineering and Computer Science Science Queensland University of Technology Australia

ISBN: (纸本)9781510847682

In this work we present a port-Hamiltonian supported control system design aimed at stabilizing a DC-DC Buck converter driving a nonlinear dissipative load. A desired closed-loop dynamics in the form of a port-Hamiltonian system is proposed, whose parameterization enforces the asymptotic stability of the desired equilibrium point. Moreover, the closed loop incorporates a first order dynamic extension allowing to reject constant disturbances on the load side. We prove that the closed loop is ISS respect to unmatched disturbances. To extend this property to disturbances acting on the supply side we add a standard PI output regulator to the previous closed loop. the performance of the closed loop is verified via simulation. Copyright © 2017 by CAL-TEK S.r.l. All rights reserved.

关键词： Asymptotic stability

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Nonlinear factor recovery for long-term SLAM

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2016年第1-3期35卷 50-72页

作者： Mazuran, Mladen Burgard, Wolfram Tipaldi, Gian Diego Univ Freiburg Dept Comp Sci D-79110 Freiburg Germany

For long-term operations, graph-based simultaneous localization and mapping (SLAM) approaches require nodes to be marginalized in order to control the computational cost. In this paper, we present a method to recover a set of nonlinear factors that best represents the marginal distribution in terms of Kullback-Leibler divergence. the proposed method, which we call nonlinear factor recovery (NFR), estimates both the mean and the information matrix of the set of nonlinear factors, where the recovery of the latter is equivalent to solving a convex optimization problem. NFR is able to provide either the dense distribution or a sparse approximation of it. In contrast to previous algorithms, our method does not necessarily require a global linearization point and can be used with any nonlinear measurement function. Moreover, we are not restricted to only using tree-based sparse approximations and binary factors, but we can include any topology and correlations between measurements. Experiments performed on several publicly available datasets demonstrate that our method outperforms the state of the art with respect to the Kullback-Leibler divergence and the sparsity of the solution.

关键词： Mobile robotics SLAM localization mapping graphical models nonlinear optimization

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Five-degree-of-freedom manipulation of an untethered magnetic device in fluid using a single permanent magnet with application in stomach capsule endoscopy

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2016年第1-3期35卷 129-147页

作者： Mahoney, Arthur W. Abbott, Jake J. Univ Utah Sch Comp Salt Lake City UT 84112 USA Univ Utah Dept Mech Engn Salt Lake City UT 84112 USA

this paper demonstrates magnetic three-degree-of-freedom (3-DOF) closed-loop position and 2-DOF open-loop orientation control of a mockup magnetic capsule endoscope in fluid with a single permanent magnet positioned by a commercial 6-DOF robotic manipulator, using feedback of only the 3-DOF capsule position measured by a localization system, with application in capsule endoscopy of a fluid-distended stomach. We analyze the kinematics of magnetic manipulation using a single permanent magnet as the end-effector of a serial-link robot manipulator, and we formulate a control method that enables the capsule's position and direction to be controlled when the robot manipulator is not in a kinematic singularity, and that sacrifices control over the capsule's direction to maintain control over the capsule's position when the manipulator enters a singularity. We demonstrate the method's robustness to a reduced control rate of 25Hz, reduced localization rates down to 30Hz, deviation in the applied magnetic field from that expected, and the presence of manipulator singularities. Five-DOF manipulation of an untethered magnetic device has been previously demonstrated by electromagnetic systems only.

关键词： Magnetic manipulation capsule endoscopy medical robotics

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Kinematic gait synthesis for snake robots

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2016年第1-3期35卷 100-113页

作者： Gong, Chaohui Travers, Matthew J. Astley, Henry C. Li, Lu Mendelson, Joseph R. Goldman, Daniel I. Choset, Howie Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Georgia Inst Technol Sch Phys Atlanta GA 30332 USA Georgia Inst Technol Sch Biol Atlanta GA 30332 USA Zoo Atlanta Dept Herpetol Atlanta GA USA

Snake robots are highly articulated mechanisms that can perform a variety of motions that conventional robots cannot. Despite many demonstrated successes of snake robots, these mechanisms have not been able to achieve the agility displayed by their biological counterparts. We suggest that studying how biological snakes coordinate whole-body motion to achieve agile behaviors can help improve the performance of snake robots. the foundation of this work is based on the hypothesis that, for snake locomotion that is approximately kinematic, replaying parameterized shape trajectory data collected from biological snakes can generate equivalent motions in snake robots. To test this hypothesis, we collected shape trajectory data from sidewinder rattlesnakes executing a variety of different behaviors. We then analyze the shape trajectory data in a concise and meaningful way by using a new algorithm, called conditioned basis array factorization, which projects high-dimensional data arrays onto a low-dimensional representation. the low-dimensional representation of the recorded snake motion is able to reproduce the essential features of the recorded biological snake motion on a snake robot, leading to improved agility and maneuverability, confirming our hypothesis. this parameterized representation allows us to search the low-dimensional parameter space to generate behaviors that further improve the performance of snake robots.

关键词： Bio-inspired robotics modal decomposition limbless locomotion

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Topological trajectory classification with filtrations of simplicial complexes and persistent homology

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2016年第1-3期35卷 204-223页

作者： Pokorny, Florian T. Hawasly, Majd Ramamoorthy, Subramanian KTH Royal Inst Technol CAS CVAP Ctr Autonomous Syst S-11428 Stockholm Sweden Univ Edinburgh IPAB Sch Informat Edinburgh EH8 9YL Midlothian Scotland

In this work, we present a sampling-based approach to trajectory classification which enables automated high-level reasoning about topological classes of trajectories. Our approach is applicable to general configuration spaces and relies only on the availability of collision free samples. Unlike previous sampling-based approaches in robotics which use graphs to capture information about the path-connectedness of a configuration space, we construct a multiscale approximation of neighborhoods of the collision free configurations based on filtrations of simplicial complexes. Our approach thereby extracts additional homological information which is essential for a topological trajectory classification. We propose a multiscale classification algorithm for trajectories in configuration spaces of arbitrary dimension and for sets of trajectories starting and ending in two fixed points. Using a cone construction, we then generalize this approach to classify sets of trajectories even when trajectory start and end points are allowed to vary in path-connected subsets. We furthermore show how an augmented filtration of simplicial complexes based on an arbitrary function on the configuration space, such as a costmap, can be defined to incorporate additional constraints. We present an evaluation of our approach in 2-, 3-, 4- and 6-dimensional configuration spaces in simulation and in real-world experiments using a Baxter robot and motion capture data.

关键词： Persistent homology motion classification topological robotics

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Understanding object descriptions in robotics by open-vocabulary object retrieval and detection

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2016年第1-3期35卷 265-280页

作者： Guadarrama, Sergio Rodner, Erik Saenko, Kate Darrell, Trevor Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Univ Jena Jena Germany Univ Massachusetts Lowell CS Dept Lowell MA USA

We address the problem of retrieving and detecting objects based on open-vocabulary natural language queries: given a phrase describing a specific object, for example the corn flakes box, the task is to find the best match in a set of images containing candidate objects. When naming objects, humans tend to use natural language with rich semantics, including basic-level categories, fine-grained categories, and instance-level concepts such as brand names. Existing approaches to large-scale object recognition fail in this scenario, as they expect queries that map directly to a fixed set of pre-trained visual categories, for example ImageNet synset tags. We address this limitation by introducing a novel object retrieval method. Given a candidate object image, we first map it to a set of words that are likely to describe it, using several learned image-to-text projections. We also propose a method for handling open vocabularies, that is, words not contained in the training data. We then compare the natural language query to the sets of words predicted for each candidate and select the best match. Our method can combine category- and instance-level semantics in a common representation. We present extensive experimental results on several datasets using both instance-level and category-level matching and show that our approach can accurately retrieve objects based on extremely varied open-vocabulary queries. Furthermore, we show how to process queries referring to objects within scenes, using state-of-the-art adapted detectors. the source code of our approach will be publicly available together with pre-trained models at http://*** and could be directly used for robotics applications.

关键词： Convolutional neural networks natural language processing object retrieval object detection

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