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  • Human Intention Recognition for Human Aware Planning in Integrated Warehouse Systems
    arXiv.cs.RO Pub Date : 2020-05-22
    Tomislav Petković; Jakub Hvězda; Tomáš Rybecký; Ivan Marković; Miroslav Kulich; Libor Přeučil; Ivan Petrović

    With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders

    更新日期:2020-05-22
  • On the Potential of Smarter Multi-layer Maps
    arXiv.cs.RO Pub Date : 2020-05-22
    Francesco Verdoja; Ville Kyrki

    The most common way for robots to handle environmental information is by using maps. At present, each kind of data is hosted on a separate map, which complicates planning because a robot attempting to perform a task needs to access and process information from many different maps. Also, most often correlation among the information contained in maps obtained from different sources is not evaluated or

    更新日期:2020-05-22
  • Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach
    arXiv.cs.RO Pub Date : 2020-05-22
    Peng Hang; Chen Lv; Yang Xing; Chao Huang; Zhongxu Hu

    Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers

    更新日期:2020-05-22
  • VDO-SLAM: A Visual Dynamic Object-aware SLAM System
    arXiv.cs.RO Pub Date : 2020-05-22
    Jun Zhang; Mina Henein; Robert Mahony; Viorela Ila

    The scene rigidity assumption, also known as the static world assumption, is common in SLAM algorithms. Most existing algorithms operating in complex dynamic environments simplify the problem by removing moving objects from consideration or tracking them separately. Such strong assumptions limit the deployment of autonomous mobile robotic systems in a wide range of important real world applications

    更新日期:2020-05-22
  • Abstractions for computing all robotic sensors that suffice to solve a planning problem
    arXiv.cs.RO Pub Date : 2020-05-22
    Yulin Zhang; Dylan A. Shell

    Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions---i.e., plan and sensor characterization pairs---if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design

    更新日期:2020-05-22
  • Givenness Hierarchy Theoretic Cognitive Status Filtering
    arXiv.cs.RO Pub Date : 2020-05-22
    Poulomi Pal; Lixiao Zhu; Andrea Golden-Lasher; Akshay Swaminathan; Tom Williams

    For language-capable interactive robots to be effectively introduced into human society, they must be able to naturally and efficiently communicate about the objects, locations, and people found in human environments. An important aspect of natural language communication is the use of pronouns. Ac-cording to the linguistic theory of the Givenness Hierarchy(GH), humans use pronouns due to implicit assumptions

    更新日期:2020-05-22
  • State-switching control of the second-order chained form system
    arXiv.cs.RO Pub Date : 2020-05-22
    Masahide Ito

    This paper addresses a motion planning problem of the second-order chained form system. The author presents a novel control approach based on switching a state. The second-order chained form system is composed of three subsystems including two double integrators and a nonlinear system. Switching a single state of the double integrators can modify the nature of the nonlinear system. Such state-switching

    更新日期:2020-05-22
  • An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors
    arXiv.cs.RO Pub Date : 2020-05-22
    Peng Hang; Chen Lv; Chao Huang; Jiacheng Cai; Zhongxu Hu; Yang Xing

    This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is

    更新日期:2020-05-22
  • Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
    arXiv.cs.RO Pub Date : 2020-05-21
    Malte Schilling; Kai Konen; Frank W. Ohl; Timo Korthals

    Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks in recent years, Deep Reinforcement Learning approaches still appear to struggle when applied to real world robots in continuous control tasks and in particular do not appear as robust

    更新日期:2020-05-21
  • Dynamics-Aware Latent Space Reachability for Exploration in Temporally-Extended Tasks
    arXiv.cs.RO Pub Date : 2020-05-21
    Homanga Bharadhwaj; Animesh Garg; Florian Shkurti

    Self-supervised goal proposal and reaching is a key component of efficient policy learning algorithms. Such a self-supervised approach without access to any oracle goal sampling distribution requires deep exploration and commitment so that long horizon plans can be efficiently discovered. In this paper, we propose an exploration framework, which learns a dynamics-aware manifold of reachable states

    更新日期:2020-05-21
  • Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
    arXiv.cs.RO Pub Date : 2020-05-21
    Michelle A. Lee; Carlos Florensa; Jonathan Tremblay; Nathan Ratliff; Animesh Garg; Fabio Ramos; Dieter Fox

    Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. On the other hand, reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sample-inefficient and brittle. In this work

    更新日期:2020-05-21
  • RV-FuseNet: Range View based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
    arXiv.cs.RO Pub Date : 2020-05-21
    Ankit Laddha; Shivam Gautam; Gregory P. Meyer; Carlos Vallespi-Gonzalez

    Autonomous vehicles rely on robust real-time detection and future motion prediction of traffic participants to safely navigate urban environments. We present a novel end-to-end approach that uses raw time-series LiDAR data to jointly solve both detection and prediction. We use the range view representation of LiDAR instead of voxelization since it does not discard information and is more efficient

    更新日期:2020-05-21
  • Accurate position tracking with a single UWB anchor
    arXiv.cs.RO Pub Date : 2020-05-21
    Yanjun Cao; Chenhao Yang; Rui Li; Alois Knoll; Giovanni Beltrame

    Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization

    更新日期:2020-05-21
  • Robotics Meets Cosmetic Dermatology: Development of a Novel Vision-Guided System for Skin Photo-Rejuvenation
    arXiv.cs.RO Pub Date : 2020-05-21
    Muhammad Muddassir; Domingo Gomez; Shujian Chen; Luyin Hu; David Navarro-Alarcon

    In this paper, we present a novel robotic system for skin photo-rejuvenation procedures, which can uniformly deliver the laser's energy over the skin of the face. The robotised procedure is performed by a manipulator whose end-effector is instrumented with a depth sensor, a thermal camera, and a cosmetic laser generator. To plan the heat stimulating trajectories for the laser, the system computes the

    更新日期:2020-05-21
  • Computationally efficient stochastic MPC: a probabilistic scaling approach
    arXiv.cs.RO Pub Date : 2020-05-21
    Martina Mammarella; Teodoro Alamo; Fabrizio Dabbene; Matthias Lorenzen

    In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-performing systems. To reduce the computational burden, in this paper we extend the probabilistic scaling approach to obtain low-complexity inner approximation

    更新日期:2020-05-21
  • Complete coordination of robotic fiber positioners for massive spectroscopic surveys
    arXiv.cs.RO Pub Date : 2020-05-21
    Matin Macktoobian; Denis Gillet; Jean-Paul Kneib

    Robotic fiber positioners play a vital role in the generation of massive spectroscopic surveys. The more complete a positioners set is coordinated, the more information its corresponding spectrograph receives during an observation. The complete coordination problem of positioners sets is studied in this paper. We first define the local and the global completeness problems and determine their relationship

    更新日期:2020-05-21
  • MDPs with Unawareness in Robotics
    arXiv.cs.RO Pub Date : 2020-05-20
    Nan Rong; Joseph Y. Halpern; Ashutosh Saxena

    We formalize decision-making problems in robotics and automated control using continuous MDPs and actions that take place over continuous time intervals. We then approximate the continuous MDP using finer and finer discretizations. Doing this results in a family of systems, each of which has an extremely large action space, although only a few actions are "interesting". We can view the decision maker

    更新日期:2020-05-20
  • Learning natural locomotion behaviors for humanoid robots using human knowledge
    arXiv.cs.RO Pub Date : 2020-05-20
    Chuanyu Yang; Kai Yuan; Shuai Heng; Taku Komura; Zhibin Li

    This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure

    更新日期:2020-05-20
  • Differential Mapping Spiking Neural Network for Sensor-Based Robot Control
    arXiv.cs.RO Pub Date : 2020-05-20
    Omar Zahra; Silvia Tolu; David Navarro-Alarcon

    In this work, a spiking neural network is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The network consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with interinhibtory connections at the output

    更新日期:2020-05-20
  • Model Predictive Instantaneous Safety Metric for Evaluation of Automated Driving Systems
    arXiv.cs.RO Pub Date : 2020-05-20
    Bowen Weng; Sughosh J. Rao; Eeshan Deosthale; Scott Schnelle; Frank Barickman

    Vehicles with Automated Driving Systems (ADS) operate in a high-dimensional continuous system with multi-agent interactions. This continuous system features various types of traffic agents (non-homogeneous) governed by continuous-motion ordinary differential equations (differential-drive). Each agent makes decisions independently that may lead to conflicts with the subject vehicle (SV), as well as

    更新日期:2020-05-20
  • Development of a Shape-memorable Adaptive Pin Array Fixture
    arXiv.cs.RO Pub Date : 2020-05-20
    Peihao Shi; Zhengtao Hu; Kazuyuki Nagata; Weiwei Wan; Yukiyasu Domae; Kensuke Harada

    This paper proposes an adaptive pin-array fixture. The key idea of this research is to use the shape-memorable mechanism of pin array to fix multiple different shaped parts with common pin configuration. The clamping area consists of a matrix of passively slid-able pins that conform themselves to the contour of the target object. Vertical motion of the pins enables the fixture to encase the profile

    更新日期:2020-05-20
  • Collision-free Trajectory Planning for Autonomous Surface Vehicle
    arXiv.cs.RO Pub Date : 2020-05-20
    Licheng Wen; Jiaqing Yan; Xuemeng Yang; Yong Liu; Yong Gu

    In this paper, we propose an efficient and accurate method for autonomous surface vehicles to generate a smooth and collision-free trajectory considering its dynamics constraints. We decouple the trajectory planning problem as a front-end feasible path searching and a back-end kinodynamic trajectory optimization. Firstly, we model the type of two-thrusts under-actuated surface vessel. Then we adopt

    更新日期:2020-05-20
  • Label Efficient Visual Abstractions for Autonomous Driving
    arXiv.cs.RO Pub Date : 2020-05-20
    Aseem Behl; Kashyap Chitta; Aditya Prakash; Eshed Ohn-Bar; Andreas Geiger

    It is well known that semantic segmentation can be used as an effective intermediate representation for learning driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving

    更新日期:2020-05-20
  • Benchmarking of a software stack for autonomous racing against a professional human race driver
    arXiv.cs.RO Pub Date : 2020-05-20
    Leonhard Hermansdorfer; Johannes Betz; Markus Lienkamp

    The way to full autonomy of public road vehicles requires the step-by-step replacement of the human driver, with the ultimate goal of replacing the driver completely. Eventually, the driving software has to be able to handle all situations that occur on its own, even emergency situations. These particular situations require extreme combined braking and steering actions at the limits of handling to

    更新日期:2020-05-20
  • Informative Path Planning for Anomaly Detection in Environment Exploration and Monitoring
    arXiv.cs.RO Pub Date : 2020-05-20
    Antoine Blanchard; Themistoklis Sapsis

    An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The success of the mission is judged by the UAV's ability to faithfully reconstruct any anomalous feature present in the environment (e.g., extreme topographic depressions or abnormal chemical concentrations). We show that the

    更新日期:2020-05-20
  • Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
    arXiv.cs.RO Pub Date : 2020-05-20
    Alex Bewley; Pei Sun; Thomas Mensink; Dragomir Anguelov; Cristian Sminchisescu

    This paper presents a novel 3D object detection framework that processes LiDAR data directly on a representation of the sensor's native range images. When operating in the range image view, one faces learning challenges, including occlusion and considerable scale variation, limiting the obtainable accuracy. To address these challenges, a range-conditioned dilated block (RCD) is proposed to dynamically

    更新日期:2020-05-20
  • Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
    arXiv.cs.RO Pub Date : 2020-05-19
    Cong FeiHuawei Noah's Ark LabTsinghua University; Bin WangHuawei Noah's Ark Lab; Yuzheng ZhuangHuawei Noah's Ark Lab; Zongzhang ZhangNanjing University; Jianye HaoHuawei Noah's Ark Lab; Hongbo ZhangHuawei Noah's Ark Lab; Xuewu JiTsinghua University; Wulong LiuHuawei Noah's Ark Lab

    Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper

    更新日期:2020-05-19
  • An Information-Theoretic Approach for Path Planning in Agents with Computational Constraints
    arXiv.cs.RO Pub Date : 2020-05-19
    Daniel T. Larsson; Dipankar Maity; Panagiotis Tsiotras

    In this paper, we develop a framework for path-planning on abstractions that are not provided to the system a-priori but instead emerge as a function of the agent's available computational resources. We show how a path-planning problem in an environment can be systematically approximated by solving a sequence of easier to solve problems on abstractions of the original space. The properties of the problem

    更新日期:2020-05-19
  • Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning
    arXiv.cs.RO Pub Date : 2020-05-19
    Jixuan Zhi; Jyh-Ming Lien

    Robotic shepherding problem considers the control and navigation of a group of coherent agents (e.g., a flock of bird or a fleet of drones) through the motion of an external robot, called shepherd. Machine learning based methods have successfully solved this problem in an empty environment with no obstacles. Rule-based methods, on the other hand, can handle more complex scenarios in which environments

    更新日期:2020-05-19
  • Pass-Fail Criteria for Scenario-Based Testing of Automated Driving Systems
    arXiv.cs.RO Pub Date : 2020-05-19
    Robert Myers; Zeyn Saigol

    The MUSICC project has created a proof-of-concept scenario database to be used as part of a type approval process for the verification of automated driving systems (ADS). This process must include a highly automated means of evaluating test results, as manual review at the scale required is impractical. This paper sets out a framework for assessing an ADS's behavioural safety in normal operation (i

    更新日期:2020-05-19
  • Real World Morphological Evolution is Feasible
    arXiv.cs.RO Pub Date : 2020-05-19
    Tonnes F. Nygaard; David Howard; Kyrre Glette

    Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture the natural noise and richness of the real world. Very few of these virtual robots are built as physical robots, and the few that are will rarely be further improved

    更新日期:2020-05-19
  • Robust Robot-assisted Tele-grasping Through Intent-Uncertainty-Aware Planning
    arXiv.cs.RO Pub Date : 2020-05-19
    Michael Bowman; Songpo Li; Xiaoli Zhang

    In teleoperation, research has mainly focused on target approaching, where we deal with the more challenging object manipulation task by advancing the shared control technique. Appropriately manipulating an object is challenging due to the fine motion constraint requirements for a specific manipulation task. Although these motion constraints are critical for task success, they often are subtle when

    更新日期:2020-05-19
  • Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding
    arXiv.cs.RO Pub Date : 2020-05-19
    Zhiyu Huang; Chen Lv; Yang Xing; Jingda Wu

    This study aims to improve the control performance and generalization capability of end-to-end autonomous driving with scene understanding leveraging deep learning and multimodal sensor fusion technology. The designed end-to-end deep neural network takes the visual image and associated depth information as inputs in an early fusion level and outputs the pixel-wise semantic segmentation as scene understanding

    更新日期:2020-05-19
  • Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization
    arXiv.cs.RO Pub Date : 2020-05-19
    Peter Karkus; Anelia Angelova; Vincent Vanhoucke; Rico Jonschkowski

    Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization

    更新日期:2020-05-19
  • Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction
    arXiv.cs.RO Pub Date : 2020-05-19
    Andreas Eitel; Nico Hauff; Wolfram Burgard

    Instance segmentation of unknown objects from images is regarded as relevant for several robot skills including grasping, tracking and object sorting. Recent results in computer vision have shown that large hand-labeled datasets enable high segmentation performance. To overcome the time-consuming process of manually labeling data for new environments, we present a transfer learning approach for robots

    更新日期:2020-05-19
  • Satellite Navigation for the Age of Autonomy
    arXiv.cs.RO Pub Date : 2020-05-19
    Tyler G. R. Reid; Bryan Chan; Ashish Goel; Kazuma Gunning; Brian Manning; Jerami Martin; Andrew Neish; Adrien Perkins; Paul Tarantino

    Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires

    更新日期:2020-05-19
  • Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints
    arXiv.cs.RO Pub Date : 2020-05-18
    Rahul Shome; Kostas E. Bekris

    Solving task planning problems involving multiple objects and multiple robotic arms poses scalability challenges. Such problems involve not only coordinating multiple high-DoF arms, but also searching through possible sequences of actions including object placements, and handoffs. The current work identifies a useful connection between multi-arm rearrangement and recent results in multi-body path planning

    更新日期:2020-05-18
  • Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation
    arXiv.cs.RO Pub Date : 2020-05-18
    Branden Romero; Filipe Veiga; Edward Adelson

    High resolution tactile sensors are often bulky and have shape profiles that make them awkward for use in manipulation. This becomes important when using such sensors as fingertips for dexterous multi-fingered hands, where boxy or planar fingertips limit the available set of smooth manipulation strategies. High resolution optical based sensors such as GelSight have until now been constrained to relatively

    更新日期:2020-05-18
  • FootTile: a Rugged Foot Sensor for Force and Center of Pressure Sensing in Soft Terrain
    arXiv.cs.RO Pub Date : 2020-05-18
    Felix Ruppert; Alexander Badri-Spröwitz

    In this paper we present FootTile, a foot sensor for reaction force and center of pressure sensing in challenging terrain. We compare our sensor design to standard biomechanical devices, force plates and pressure plates. We show that FootTile can accurately estimate force and pressure distribution during legged locomotion. FootTile weighs 0.9g, has a sampling rate of 330Hz, a footprint of 10 by 10mm

    更新日期:2020-05-18
  • Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
    arXiv.cs.RO Pub Date : 2020-05-18
    Tim Stahl; Alexander Wischnewski; Johannes Betz; Markus Lienkamp

    Trajectory planning at high velocities and at the handling limits is a challenging task. In order to cope with the requirements of a race scenario, we propose a far-sighted two step, multi-layered graph-based trajectory planner, capable to run with speeds up to 212~km/h. The planner is designed to generate an action set of multiple drivable trajectories, allowing an adjacent behavior planner to pick

    更新日期:2020-05-18
  • Development of GenNav: A Generic Indoor Navigation System for any Mobile Robot
    arXiv.cs.RO Pub Date : 2020-05-18
    Sudarshan S Harithas; Biswajit Pardia

    The navigation system is at the heart of any mobile robot. It consists of both the SLAM and path planning units, which the robot utilizes to generate a map of the environment, localize itself within it and generate a path to the destination . This paper describes the conceptualization, development, simulation and hardware implementation of GenNav a generic indoor navigation system for any mobile aerial

    更新日期:2020-05-18
  • Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
    arXiv.cs.RO Pub Date : 2020-05-18
    Lai Wei; Xiaobo Tan; Vaibhav Srivastava

    We consider a scenario in which an autonomous vehicle equipped with a downward facing camera operates in a 3D environment and is tasked with searching for an unknown number of stationary targets on the 2D floor of the environment. The key challenge is to minimize the search time while ensuring a high detection accuracy. We model the sensing field using a multi-fidelity Gaussian process that systematically

    更新日期:2020-05-18
  • A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization
    arXiv.cs.RO Pub Date : 2020-05-18
    Peng Gang; Wang Zhihao; Yang Jin; Li Xinde

    The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the

    更新日期:2020-05-18
  • Optimal target assignment for massive spectroscopic surveys
    arXiv.cs.RO Pub Date : 2020-05-18
    Matin Macktoobian; Denis Gillet; Jean-Paul Kneib

    Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a target associated with a particular observation. The assignment process directly impacts on the coordination of robotic fiber positioners to reach

    更新日期:2020-05-18
  • Visual Memorability for Robotic Interestingness Prediction via Unsupervised Online Learning
    arXiv.cs.RO Pub Date : 2020-05-18
    Chen Wang; Wenshan Wang; Yuheng Qiu; Yafei Hu; Sebastian Scherer

    In this paper, we aim to solve the problem of interesting scene prediction for mobile robots. This area is currently under explored but is crucial for many practical applications such as autonomous exploration and decision making. First, we expect a robot to detect novel and interesting scenes in unknown environments and lose interests over time after repeatedly observing similar objects. Second, we

    更新日期:2020-05-18
  • Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
    arXiv.cs.RO Pub Date : 2020-05-18
    Cunjun Yu; Xiao Ma; Jiawei Ren; Haiyu Zhao; Shuai Yi

    Understanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. This is challenging because it requires effectively modeling the socially aware crowd spatial interaction and complex temporal dependencies. We believe attention is the most important factor for trajectory prediction. In this paper, we present STAR, a Spatio-Temporal grAph

    更新日期:2020-05-18
  • Certifiably Optimal Monocular Hand-Eye Calibration
    arXiv.cs.RO Pub Date : 2020-05-17
    Emmett Wise; Matthew Giamou; Soroush Khoubyarian; Abhinav Grover; Jonathan Kelly

    Correct fusion of data from two sensors is not possible without an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When two or more sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the 'hand-eye' formulation of extrinsic calibration can be employed. In this

    更新日期:2020-05-17
  • Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization
    arXiv.cs.RO Pub Date : 2020-05-17
    Gang Peng; Zezao Lu; Zejie Tan; Dingxin He; Xinde Li

    Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal

    更新日期:2020-05-17
  • SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper
    arXiv.cs.RO Pub Date : 2020-05-17
    WANG Dexin

    In this paper, we present Segmentation-Based Grasp Detection Network (SGDN) to predict a feasible robotic grasping for a unsymmetrical three-finger robotic gripper using RGB images. The feasible grasping of a target should be a collection of grasp regions with the same grasp angle and width. In other words, a simplified planar grasp representation should be pixel-level rather than region-level such

    更新日期:2020-05-17
  • Coordinated Coverage and Fault Tolerance using Fixed-Wing Unmanned Aerial Vehicles
    arXiv.cs.RO Pub Date : 2020-05-17
    Sachin Shriwastav; Zhuoyuan Song

    This paper presents an approach for deploying and maintaining a fleet of homogeneous fixed-wing unmanned aerial vehicles (UAVs) for all-time coverage of an area. Two approaches for loiter circle packing have been presented: square and hexagon packing, and the benefits of hexagon packing for minimizing the number of deployed UAVs have been shown. Based on the number of UAVs available and the desired

    更新日期:2020-05-17
  • On Efficient Connectivity-Preserving Transformations in a Grid
    arXiv.cs.RO Pub Date : 2020-05-17
    Abdullah Almethen; Othon Michail; Igor Potapov

    We consider a discrete system of $n$ devices lying on a 2-dimensional square grid and forming an initial connected shape $S_I$. Each device is equipped with a linear-strength mechanism which enables it to move a whole line of consecutive devices in a single time-step. We study the problem of transforming $S_I$ into a given connected target shape $S_F$ of the same number of devices, via a finite sequence

    更新日期:2020-05-17
  • Reinforcement Learning Based Transmission Range Control (RL-TRC) in SD-WSN with Moving Sensors
    arXiv.cs.RO Pub Date : 2020-05-17
    Anuradha BanerjeeKalyani Government Engineering College, West Bengal, India; Abu SufianUniversity of Gour Banga, West Bengal, India

    Routing in Software-Defined Wireless sensor networks (SD-WSNs) can be either single or multi-hop. The network is either static or dynamic. In static SD-WSN, the selection of the optimum route from source to destination is accomplished by the SDN controller(s). On the other hand, if moving sensors are there then SDN controllers of zones are not able to handle route discovery sessions by themselves;

    更新日期:2020-05-17
  • Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator
    arXiv.cs.RO Pub Date : 2020-05-17
    Rui Fan; Hengli Wang; Bohuan Xue; Huaiyang Huang; Yuan Wang; Ming Liu; Ioannis Pitas

    Over the past decade, significant efforts have been made to improve the trade-off between speed and accuracy of surface normal estimators (SNEs). This paper introduces an accurate and ultrafast SNE for structured range data. The proposed approach computes surface normals by simply performing three filtering operations, namely, two image gradient filters (in horizontal and vertical directions, respectively)

    更新日期:2020-05-17
  • A Methodology to Assess the Human Factors Associated with Lunar Teleoperated Assembly Tasks
    arXiv.cs.RO Pub Date : 2020-05-16
    Arun Kumar; Mason Bell; Benjamin Mellinkoff; Alex Sandoval; Wendy Bailey Martin; Jack Burns

    Low-latency telerobotics can enable more intricate surface tasks on extraterrestrial planetary bodies than has ever been attempted. For humanity to create a sustainable lunar presence, well-developed collaboration between humans and robots is necessary to perform complex tasks. This paper presents a methodology to assess the human factors, situational awareness (SA) and cognitive load (CL), associated

    更新日期:2020-05-16
  • Reachability as a Unifying Framework for Computing Helicopter Safe Operating Conditions and Autonomous Emergency Landing
    arXiv.cs.RO Pub Date : 2020-05-16
    Matthew R. Kirchner; Eddie Ball; Jacques Hoffler; Don Gaublomme

    We present a numeric method to compute the safe operating flight conditions for a helicopter such that we can ensure a safe landing in the event of a partial or total engine failure. The unsafe operating region is the complement of the backwards reachable tube, which can be found as the sub-zero level set of the viscosity solution of a Hamilton-Jacobi (HJ) equation. Traditionally, numerical methods

    更新日期:2020-05-16
  • Gathering on a Circle with Limited Visibility by Anonymous Oblivious Robots
    arXiv.cs.RO Pub Date : 2020-05-16
    Giuseppe A. Di Luna; Ryuhei Uehara; Giovanni Viglietta; Yukiko Yamauchi

    A swarm of anonymous oblivious mobile robots, operating in deterministic Look-Compute-Move cycles, is confined within a circular track. All robots agree on the clockwise direction (chirality), they are activated by an adversarial semi-synchronous scheduler (SSYNCH), and an active robot always reaches the destination point it computes (rigidity). Robots have limited visibility: each robot can see only

    更新日期:2020-05-16
  • Gentlemen on the Road: Effect of Yielding Behavior of Autonomous Vehicle on Pedestrian Head Orientation
    arXiv.cs.RO Pub Date : 2020-05-16
    Yoon Kyung Lee; Yong-Eun Rhee; Jeh-Kwang Ryu; Sowon Hahn

    Autonomous vehicles can improve pedestrian safety by learning human-like social behaviors (e.g., yielding). We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using Dynamic time warping and K-means

    更新日期:2020-05-16
  • Model-based Randomness Monitor for Stealthy Sensor Attacks
    arXiv.cs.RO Pub Date : 2020-05-16
    Paul J. BonczekUniversity of Virginia; Shijie GaoUniversity of Virginia; Nicola BezzoUniversity of Virginia

    Malicious attacks on modern autonomous cyber-physical systems (CPSs) can leverage information about the system dynamics and noise characteristics to hide while hijacking the system toward undesired states. Given attacks attempting to hide within the system noise profile to remain undetected, an attacker with the intent to hijack a system will alter sensor measurements, contradicting with what is expected

    更新日期:2020-05-16
  • Optimal Path Planning for Automated Dimensional Inspection of Free-Form Surfaces
    arXiv.cs.RO Pub Date : 2020-05-15
    Yinhua Liu; Wenzheng Zhao; Rui Sun; Xiaowei Yue

    Structural dimensional inspection is vital for the process monitoring, quality control, and fault diagnosis in the mass production of auto bodies. Comparing with the non-contact measurement, the high-precision five-axis measuring machine with the touch-trigger probe is a preferred choice for data collection. It can assist manufacturers in making accurate inspection quickly. As the increase of free-form

    更新日期:2020-05-15
  • A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios
    arXiv.cs.RO Pub Date : 2020-05-15
    Dongfang Yang; Keith Redmill; Umit Ozguner

    Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for automated driving systems. The design of driving strategies for such systems usually starts with verifying VPI in simulation. This work proposed an improved framework for the study of VPI in uncontrolled pedestrian crossing scenarios. The framework admits the mutual effect between the pedestrian and the vehicle. A multi-state

    更新日期:2020-05-15
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