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RANUS: RGB and NIR Urban Scene Dataset for deep Scene Parsing

IEEE robotics AND automation LETTERS 2018年第3期3卷 1808-1815页

作者： Choe, Gyeongmin Kim, Seong-Heum Im, Sunghoon Lee, Joon-Young Narasimhan, Srinivasa G. Kweon, In So Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Adobe Res San Jose CA 95110 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

In this letter, we present a data-driven method for scene parsing of road scenes to utilize single-channel near-infrared (NIR) images. To overcome the lack of data problem in non-RGB spectrum, we define a new color space and decompose the task of deep scene parsing into two subtasks with two separate CNN architectures for chromaticity channels and semantic masks. For chromaticity estimation, we build a spatially-aligned-RGB-NIR image database (40k urban scenes) to infer color information from RGB-NIR spectrum learning process and leverage existing scene parsing networks trained over already available RGB masks. From our database, we sample key frames and manually annotate them (4k ground truth masks) to finetune the network into the proposed color space. Hence, the key contribution of this work is to replace multispectral scene parsing methods with a simple yet effective approach using single NIR images. The benefits of using our algorithm and dataset are confirmed in the qualitative and quantitative experiments.

关键词： deep learning in robotics and automation semantic scene understanding

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Shear, Torsion and Pressure Tactile Sensor via Plastic Optofiber Guided Imaging

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2618-2625页

作者： Baimukashev, Daulet Kappassov, Zhanat Varol, Huseyin Atakan Nazarbayev Univ Inst Smart Syst & Artificial Intelligence Nur Sultan 010000 Kazakhstan Nazarbayev Univ Dept Robot & Mechatron Nur Sultan 010000 Kazakhstan

Object manipulation performed by robots refers to the art of controlling the shape and location of an object through force constraints with robot end-effectors, both robot hands, and grippers. The success of task execution is usually guaranteed by the sense of touch. In this work, we present an optical tactile sensor incorporating plastic optical fibers, transparent silicone rubber, and an off-the-shelf color camera that can detect: translational and rotational shear forces, and contact location and its normal force. Contact localization is possible thanks to the shear strain. Specifically, one of the layers stretches so that its thickness decreases. The decrease in the thickness results in the color change at the point of contact. Elastic behavior of the sensing media provides a robust rotational and translational shear detection mechanism when torque and planar force, respectively, are applied onto the sensing surface. Thanks to the plastic optofibers, signal processing electronics are placed away from the sensing surface making the sensor immune to hazardous environments. Machine learning techniques were used to benchmark the sensing performance of the sensor. By implementing a multi-output CNN model, the contact type was classified into normal and shear or torsional deformation and their corresponding continuous contact features were estimated.

关键词： deep learning in robotics and automation force and tactile sensing soft sensors and actuators

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learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4383-4390页

作者： James, Jasmin Ford, Jason J. Molloy, Timothy L. Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia

The commercial use of unmanned aerial vehicles (UAVs) would be enhanced by an ability to sense and avoid potential mid-air collision threats. In this letter, we propose a new approach to aircraft detection for long-range vision-based sense and avoid. We first train a deep convolutional neural network to learn aircraft visual features using flight data of mid-air head-on near-collision course encounters between two fixed-wing aircraft. We then propose an approach that fuses these learnt aircraft features with hand-crafted features that are used by the current state of the art. Finally, we evaluate the performance of our proposed approach on real flight data captured from a UAV, where it achieves a mean detection range of 2527 m and a mean detection range improvement of 299 m (or 13.4%) compared to the current state of the art with no additional false alarms.

关键词： Aerial systems: perception and autonomy computer vision for automation deep learning in robotics and automation

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DRL-VO: learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles

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IEEE TRANSACTIONS ON robotics 2023年第4期39卷 2700-2719页

作者： Xie, Zhanteng Dames, Philip Temple Univ Dept Mech Engn Philadelphia PA 19122 USA

This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a subgoal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move toward the goal. Through a series of 3-D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized.

关键词： Robots Robot sensing systems Navigation Collision avoidance Data models Robot kinematics Laser radar deep learning in robotics and automation field robotics reactive and sensor-based planning

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Constrained Motion Planning Networks X

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IEEE TRANSACTIONS ON robotics 2022年第2期38卷 868-886页

作者： Qureshi, Ahmed Hussain Dong, Jiangeng Baig, Asfiya Yip, Michael C. Univ Calif San Diego La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn La Jolla CA 92093 USA

Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between a given start and goal configuration. These planning problems come up surprisingly frequently, such as in robot manipulation for performing daily life assistive tasks. However, few solutions to constrained motion planning are available, and those that exist struggle with high computational time complexity in finding a path solution on the manifolds. To address this challenge, we present Constrained Motion Planning Networks X (CoMPNetX). It is a neural planning approach, comprising a conditional deep neural generator and discriminator with neural gradients-based fast projection operator. We also introduce neural task and scene representations conditioned on which the CoMPNetX generates implicit manifold configurations to turbo-charge any underlying classical planner such as sampling-based motion planning methods for quickly solving complex constrained planning tasks. We show that our method finds path solutions with high success rates and lower computation times than state-of-the-art traditional path-finding tools on various challenging scenarios.

关键词： Manifolds Planning Task analysis Robots Switched mode power supplies Collision avoidance Jacobian matrices learning from demonstration motion and path planning deep learning in robotics and automation learning and adaptive systems

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Aerial Single-View Depth Completion With Image-Guided Uncertainty Estimation

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1055-1062页

作者： Teixeira, Lucas Oswald, Martin R. Pollefeys, Marc Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8050 Zurich Switzerland Swiss Fed Inst Technol Comp Vis & Geometry Grp CH-8092 Zurich Switzerland Microsoft Res CH-8001 Zurich Switzerland

On the pursuit of autonomous flying robots, the scientific community has been developing onboard real-time algorithms for localisation, mapping and planning. Despite recent progress, the available solutions still lack accuracy and robustness in many aspects. While mapping for autonomous cars had a substantive boost using deep-learning techniques to enhance LIDAR measurements using image-based depth completion, the large viewpoint variations experienced by aerial vehicles are still posing major challenges for learning-based mapping approaches. In this letter, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources. The core of our method is a novel compact network that performs both depth completion and confidence estimation using an image-guided approach. Real-time performance onboard a GPU suitable for small flying robots is achieved by sharing deep features between both tasks. Experiments demonstrate that our network outperforms the state-of-the-art in depth completion and uncertainty estimation for single-view methods on mobile GPUs. We further present a new photorealistic aerial depth completion dataset that exhibits more challenging depth completion scenarios than the established indoor and car driving datasets. The dataset includes an open-source, visual-inertial UAV simulator for photo-realistic data generation. Our results show that our network trained on this dataset can be directly deployed on real-world outdoor aerial public datasets without fine-tuning or style transfer.

关键词： Aerial systems: perception and autonomy deep learning in robotics and automation

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Self-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4386-4393页

作者： Wang, Hengli Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China

The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with hand-labeled ground truth is time-consuming and labor-intensive, making the deep learning-based methods often hard to implement in practice. We contribute to the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. We develop a pipeline that can automatically generate segmentation labels for drivable areas and road anomalies. Then, we train RGB-D data-based semantic segmentation neural networks and get predicted labels. Experimental results show that our proposed automatic labeling pipeline achieves an impressive speed-up compared to manual labeling. In addition, our proposed self-supervised approach exhibits more robust and accurate results than the state-of-the-art traditional algorithms as well as the state-of-the-art self-supervised algorithms.

关键词： Semantic scene understanding deep learning in robotics and automation RCB-D perception

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deepIG: Multi-Robot Information Gathering With deep Reinforcement learning

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IEEE robotics AND automation LETTERS 2019年第3期4卷 3059-3066页

作者： Viseras, Alberto Garcia, Ricardo German Aerosp Ctr DLR Inst Commun & Nav D-82234 Oberplaffenholen Germany Univ Politecn Madrid ETSI Telecomunicac E-28040 Madrid Spain

State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion. This causes MR-IG algorithms to fail when they are applied to new IG tasks, as existing models cannot describe the information of interest. Therefore, we propose in this letter a MR-IG algorithm that can be applied to new IG tasks with little algorithmic changes. To this end, we introduce deepIG: a MR-IG algorithm that uses deep reinforcement learning to allow robots to learn how to gather information. Nevertheless, there are IG tasks for which accurate models have been derived. Therefore, we extend deepIG to exploit existing models for such IG tasks. This algorithm we term it model-based deepIG (MB-deepIG). First, we evaluate deepIG in simulations, and in an indoor experiment with three quadcopters that autonomously map an unknown terrain profile built in our lab. Results demonstrate that deepIG can be applied to different IG tasks without algorithmic changes, and that it is robust to measurement noise. Then, we benchmark MB-deepIG against state-of-the-art information-driven Gaussian-processes-based IG algorithms. Results demonstrate that MB-deepIG outperforms the considered benchmarks.

关键词： Multi-robot systems deep learning in robotics and automation

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Motion Switching With Sensory and Instruction Signals by Designing Dynamical Systems Using deep Neural Network

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3481-3488页

作者： Suzuki, Kanata Mori, Hiroki Ogata, Tetsuya Fujitsu Labs Ltd Artificial Intelligence Labs Kawasaki Kanagawa 2118588 Japan Waseda Univ Sch Fundamental Sci & Engn Dept Intermedia Art & Sci Tokyo 5650871 Japan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 1350064 Japan

To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of tasks performed by them increase. To handle the switching and combination of multiple behaviors, we propose a method to design dynamical systems based on point attractors that accept (i) "instruction signals" for instruction driven switching We incorporate the (ii) "instruction phase" to form a point attractor and divide the target task into multiple sub tasks. By forming an instruction phase that consists of point attractors, the model embeds a subtask in the form of trajectory dynamics that can be manipulated using sensory and instruction signals. Our model comprises two deep neural networks: A convolutional autoencoder and a multiple time-scale recurrent neural network. In this study, we apply the proposed method to manipulate soft materials. To evaluate our model, we design a cloth-folding task that consists of four subtasks and three patterns of instruction signals, which indicate the direction of motion. The results depict that the robot can perform the required task by combining subtasks based on sensory and instruction signals. And, our model determined the relations among these signals using its internal dynamics.

关键词： deep learning in robotics and automation Al-based methods humanoid robots

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High-Speed Autonomous Drifting With deep Reinforcement learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1247-1254页

作者： Cai, Peide Mei, Xiaodong Tai, Lei Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Alibaba Grp AI Lab Hangzhou 311000 Peoples R China

Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the error-based state and reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.

关键词： deep learning in robotics and automation field robots motion control deep reinforcement learning racing car

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