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arXiv 2018年

作者： Sauer, Axel Savinov, Nikolay Geiger, Andreas Computer Vision and Geometry Group ETH Zürich Chair of Robotics Science and System Intelligence Technical University of Munich Autonomous Vision Group MPI for Intelligent Systems and University of Tübingen

Most existing approaches to autonomous driving fall into one of two categories: Modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Start State Selection for Control Policy Learning from Optimal Trajectories

Start State Selection for Control Policy Learning from Optim...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christoph Zelch Jan Peters Oskar von Stryk Department of Computer Science TU Darmstadt Simulation Systems Optimization and Robotics Group Darmstadt Germany Department of Computer Science Intelligent Autonomous Systems Group TU Darmstadt Darmstadt Germany

Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.

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Traffic Sign Classification Using Deep and Quantum Neural Networks

arXiv

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arXiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2022, CC BY.

关键词： Traffic signs

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Traffic Sign Classification Using Deep and Quantum Neural Networks

TechRxiv

引用

TechRxiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

关键词： Traffic signs

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A generalized framework for opening doors and drawers in kitchen environments

A generalized framework for opening doors and drawers in kit...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Thomas Rühr Jürgen Sturm Dejan Pangercic Michael Beetz Daniel Cremers Intelligent Autonomous Systems group Computer Science Department Technische Universität München Germany Computer Vision and Pattern Recognition group Computer Science Department Technische Universität München Germany

In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.

关键词： Robot sensing systems Kinematics Robot kinematics Grippers Solid modeling Trajectory

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms robotics

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Heterogeneous sensor fusion framework for autonomous mobile robot obstacle avoidance

Heterogeneous sensor fusion framework for autonomous mobile ...

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International Conference on intelligent Systems Design and Applications (ISDA)

作者： Ali Zia Tauseef Gulrez Tayyab Chaudhry Department of Computer Science Intelligent Machines & Robotics Research Group COMSATS Institute of Information Technology Lahore Pakistan

This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approach we reparameterize the 2D gaussian distribution of the laser free-configuration eigenspaces by vision saliency gaussian kernel function. The approach uses bi-sensor paradigm to achieve greater effective mapping of the environment and improved accuracy in obstacle position estimation. Where the laser lower dimensional manifolds provide an eigenvector which corresponds to the free configuration space of the high order geometric representation of the environment and vision based edge detection followed by the saliency mapping provides the road detection and existance of dynamic obstacles on the road. We have shown that while the vectorial combination of eigenvectors at discrete time scan-frames of laser data manifest a trajectory, and once followed and fused with the vision sensor data, enables mobile robot to build an efficient and accurate online environment map free of obstacles. We demonstrated this process using real-time NAVLAB CMU (Autonomous Jeep's) data-set which is a good representation of autonomous mobile robot's navigation in an urban environment.

关键词： Robot sensing systems Laser modes Collision avoidance Mobile robots Gaussian distribution

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Underwater multi-robot convoying using visual tracking by detection

arXiv

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arXiv 2017年

作者： Shkurti, Florian Chang, Wei-Di Henderson, Peter Islam, Md Jahidul Higuera, Juan Camilo Gamboa Li, Jimmy Manderson, Travis Xu, Anqi Dudek, Gregory Sattar, Junaed Centre for Intelligent Machines School of Computer Science McGill University Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota- Twin Cities Element AI

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i.e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot’s independent motion. Copyright © 2017, The Authors. All rights reserved.

关键词： Multipurpose robots

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Multi-robot forest coverage for unweighted terrain

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AIP Conference Proceedings 2013年第1期1558卷 2083-2086页

作者： Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russia

Different planning problems for mobile robots are of considerable interest for many years. Frequently, such problems require to solve different hard computational problems. Multi-robot forest coverage is one of such computational problems. In this paper, we consider an efficient approach to solve the problem of multi-robot forest coverage for unweighted terrain. In particular, we consider an explicit reduction from the decision version of the problem of multi-robot forest coverage for unweighted terrain to the satisfiability problem. For different satisfiability algorithms, the results of computational experiments are presented.

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SIFT texture description for understanding breast ultrasound images

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12th International Workshop on Breast Imaging, IWDM 2014

作者： Massich, Joan Meriaudeau, Fabrice Sentís, Melcior Ganau, Sergi Pérez, Elsa Puig, Domenec Martí, Robert Oliver, Arnau Martí, Joan Computer Vision and Robotics Group University of Girona Spain Laboratoire Le2i-UMR CNRS University of Burgundy Le Creusot France Department of Breast and Gynecological Radiology UDIAT-Diagnostic Center Parc Taulí Corporation Sabadell Spain Department of Radiology Hospital Josep Trueta of Girona Spain Department of Computer Engineering and Mathematics University Rovira I Virgili Tarragona Spain

ISBN: (纸本)9783319078861

Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images. © 2014 Springer International Publishing.

关键词： Ultrasonics

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