机器人相关学术速递[1.10]
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cs.RO机器人相关，共计10篇
【1】 Elephant-Human Conflict Mitigation: An Autonomous UAV Approach
标题：缓解大象与人类冲突：一种自主无人机方法
链接：https://arxiv.org/abs/2201.02584
备注：None
摘要：大象-人类冲突（EHC）是大多数非洲和亚洲国家的主要问题之一。由于人类过度利用自然资源进行发展，大象的生存面积持续减少；这导致大象更频繁地入侵人类生活区和掠夺农作物，每年耗资数百万美元。为了缓解EHC，在本文中，我们提出了一个原始解决方案，该解决方案由三部分组成：安装在大象身上的小型定制低功耗GPS标签，安装在人类生活区的接收器，用于检测农场附近大象的存在，以及一个自动无人机（UAV）系统，该系统可以跟踪和放牧农场以外的大象。通过使用比例-积分-微分控制器和机器学习算法，我们以32 FPS的实时处理速度获得了精确的跟踪轨迹。在缓解EHC方面，与人控无人机相比，我们提出的自主系统可以节省超过68%的成本。
摘要：Elephant-human conflict (EHC) is one of the major problems in most African
and Asian countries. As humans overutilize natural resources for their
development, elephants' living area continues to decrease; this leads elephants
to invade the human living area and raid crops more frequently, costing
millions of dollars annually. To mitigate EHC, in this paper, we propose an
original solution that comprises of three parts: a compact custom low-power GPS
tag that is installed on the elephants, a receiver stationed in the human
living area that detects the elephants' presence near a farm, and an autonomous
unmanned aerial vehicle (UAV) system that tracks and herds the elephants away
from the farms. By utilizing proportional-integral-derivative controller and
machine learning algorithms, we obtain accurate tracking trajectories at a
real-time processing speed of 32 FPS. Our proposed autonomous system can save
over 68 % cost compared with human-controlled UAVs in mitigating EHC.
【2】 Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents
标题：视觉注意预测提高自主无人机竞速智能体的性能
链接：https://arxiv.org/abs/2201.02569
备注：12 pages, 6 figures
摘要：人类驾驶无人机的速度比训练用于端到端自主飞行的神经网络要快。这可能与人类飞行员有效选择任务相关视觉信息的能力有关。这项工作研究了神经网络是否能够模仿人眼注视行为和注意力，从而提高神经网络在基于视觉的自主无人机竞赛中的性能。我们假设，在基于模拟器的无人机竞赛任务中，基于注视的注意力预测是一种有效的视觉信息选择和决策机制。我们使用18名人类无人驾驶飞机飞行员的眼睛注视和飞行轨迹数据来检验这一假设，以训练视觉注意预测模型。然后，我们使用该视觉注意力预测模型，通过模仿学习，为基于视觉的自主无人机比赛训练端到端控制器。我们将注意力预测控制器的无人机竞赛性能与使用原始图像输入和基于图像的抽象（即特征轨迹）的无人机竞赛性能进行比较。我们的研究结果表明，基于注意力预测的控制器性能优于基线，能够持续完成具有挑战性的赛道，成功率高达88%。此外，视觉注意预测和基于特征轨迹的模型在保持参考轨迹上的泛化性能优于基于图像的模型。我们的研究结果表明，人类视觉注意力预测提高了基于自主视觉的无人机竞赛代理的性能，为实现最终能够达到甚至超过人类性能的基于视觉、快速和敏捷的自主飞行迈出了重要的一步。
摘要：Humans race drones faster than neural networks trained for end-to-end
autonomous flight. This may be related to the ability of human pilots to select
task-relevant visual information effectively. This work investigates whether
neural networks capable of imitating human eye gaze behavior and attention can
improve neural network performance for the challenging task of vision-based
autonomous drone racing. We hypothesize that gaze-based attention prediction
can be an efficient mechanism for visual information selection and decision
making in a simulator-based drone racing task. We test this hypothesis using
eye gaze and flight trajectory data from 18 human drone pilots to train a
visual attention prediction model. We then use this visual attention prediction
model to train an end-to-end controller for vision-based autonomous drone
racing using imitation learning. We compare the drone racing performance of the
attention-prediction controller to those using raw image inputs and image-based
abstractions (i.e., feature tracks). Our results show that attention-prediction
based controllers outperform the baselines and are able to complete a
challenging race track consistently with up to 88% success rate. Furthermore,
visual attention-prediction and feature-track based models showed better
generalization performance than image-based models when evaluated on hold-out
reference trajectories. Our results demonstrate that human visual attention
prediction improves the performance of autonomous vision-based drone racing
agents and provides an essential step towards vision-based, fast, and agile
autonomous flight that eventually can reach and even exceed human performances.
【3】 Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy
标题：在线三轴磁强计硬铁和软铁偏差和角速度传感器偏差估计使用角速度传感器提高动态航向精度
链接：https://arxiv.org/abs/2201.02449
备注：Preprint of an article accepted for publication in Field Robotics, this https URL, Special Issue in Unmanned Marine Systems. Submitted January 16, 2021; Revised May 28, 2021; Accepted August 2, 2021
摘要：本文讨论了在野外机器人技术中，仅利用三轴磁强计和三轴角速率传感器的偏置测量值，动态在线估计和补偿三轴磁强计在动态运动下的硬铁和软铁偏置问题。所提出的磁强计和角速度偏差估计器（MAVBE）利用15态过程模型，对受到角速度偏移影响的磁强计信号的非线性过程动力学进行编码，同时估计9个磁强计偏差参数和3个角速率传感器偏差参数，在扩展卡尔曼滤波框架内。数值计算了偏置参数的局部可观测性。偏置补偿信号与三轴加速度计信号一起用于估计偏置补偿磁大地定向。在美国马里兰州切萨皮克湾的仪器化自主水下航行器的数值模拟、实验室实验和全尺寸现场试验中，与广泛引用的仅使用磁强计的两步方法相比，评估了拟议的MAVBE方法的性能。对于拟议的MAVBE，（i）估计偏差不需要仪器姿态，结果表明（ii）偏差是局部可观测的，（iii）偏差估计快速收敛到真实偏差参数，（iv）偏差估计收敛只需要适度的仪器激励，和（v）磁强计硬铁和软铁偏差补偿显著提高动态航向估计精度。
摘要：This article addresses the problem of dynamic on-line estimation and
compensation of hard-iron and soft-iron biases of 3-axis magnetometers under
dynamic motion in field robotics, utilizing only biased measurements from a
3-axis magnetometer and a 3-axis angular rate sensor. The proposed magnetometer
and angular velocity bias estimator (MAVBE) utilizes a 15-state process model
encoding the nonlinear process dynamics for the magnetometer signal subject to
angular velocity excursions, while simultaneously estimating 9 magnetometer
bias parameters and 3 angular rate sensor bias parameters, within an extended
Kalman filter framework. Bias parameter local observability is numerically
evaluated. The bias-compensated signals, together with 3-axis accelerometer
signals, are utilized to estimate bias compensated magnetic geodetic heading.
Performance of the proposed MAVBE method is evaluated in comparison to the
widely cited magnetometer-only TWOSTEP method in numerical simulations,
laboratory experiments, and full-scale field trials of an instrumented
autonomous underwater vehicle in the Chesapeake Bay, MD, USA. For the proposed
MAVBE, (i) instrument attitude is not required to estimate biases, and the
results show that (ii) the biases are locally observable, (iii) the bias
estimates converge rapidly to true bias parameters, (iv) only modest instrument
excitation is required for bias estimate convergence, and (v) compensation for
magnetometer hard-iron and soft-iron biases dramatically improves dynamic
heading estimation accuracy.
【4】 Continuous-time Radar-inertial Odometry for Automotive Radars
标题：汽车雷达的连续时间雷达惯性里程计
链接：https://arxiv.org/abs/2201.02437
备注：In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
摘要：我们提出了一种雷达惯性里程计方法，该方法使用连续时间框架来融合来自多个汽车雷达和惯性测量单元（IMU）的测量。与照相机和激光雷达传感器不同，恶劣天气条件不会对雷达传感器的工作性能产生重大影响。雷达在这种情况下的鲁棒性以及乘用车上雷达的日益普及促使我们考虑使用雷达进行自我运动估计。连续时间轨迹表示不仅作为一个框架用于实现异构和异步多传感器融合，而且通过能够在轨迹上的任何给定时间以闭合形式计算姿势及其导数来促进高效优化。我们将我们的连续时间估计与离散时间雷达惯性里程计方法的估计进行了比较，结果表明，我们的连续时间方法优于离散时间方法。据我们所知，这是首次将连续时间框架应用于雷达惯性里程计。
摘要：We present an approach for radar-inertial odometry which uses a
continuous-time framework to fuse measurements from multiple automotive radars
and an inertial measurement unit (IMU). Adverse weather conditions do not have
a significant impact on the operating performance of radar sensors unlike that
of camera and LiDAR sensors. Radar's robustness in such conditions and the
increasing prevalence of radars on passenger vehicles motivate us to look at
the use of radar for ego-motion estimation. A continuous-time trajectory
representation is applied not only as a framework to enable heterogeneous and
asynchronous multi-sensor fusion, but also, to facilitate efficient
optimization by being able to compute poses and their derivatives in
closed-form and at any given time along the trajectory. We compare our
continuous-time estimates to those from a discrete-time radar-inertial odometry
approach and show that our continuous-time method outperforms the discrete-time
method. To the best of our knowledge, this is the first time a continuous-time
framework has been applied to radar-inertial odometry.
【5】 Unwinding Rotations Improves User Comfort with Immersive Telepresence Robots
标题：使用身临其境的网真机器人，展开旋转可提高用户舒适度
链接：https://arxiv.org/abs/2201.02392
备注：Accepted for publication in HRI (Int. Conf. on Human-Robot Interaction) 2022
摘要：我们建议解开沉浸式临场感机器人用户所经历的旋转，以提高舒适度并减少用户的虚拟现实疾病。沉浸式临场感指的是移动机器人顶部的360度摄像头将视频和音频流传输到可能很远的远程用户佩戴的头戴式显示器上。因此，它使用户能够出现在机器人的位置，通过转动头部环顾四周，并与机器人附近的人进行通信。通过展开相机帧的旋转，当机器人旋转时，用户的视点不会改变。用户只能通过在其本地设置中物理旋转来改变其视点；由于没有相应前庭刺激的视觉旋转是VR疾病的主要来源，用户的身体旋转有望减少VR疾病。我们为一个穿越虚拟环境的模拟机器人实现了展开旋转，并进行了一项用户研究（N=34），将展开旋转与机器人转动时用户的视点转动进行比较。我们的研究结果表明，用户发现放松旋转更可取、更舒适，并且降低了他们的VR疾病水平。我们还提供了关于用户路径集成能力、观察方向以及对机器人速度和距离的主观观察的进一步结果。
摘要：We propose unwinding the rotations experienced by the user of an immersive
telepresence robot to improve comfort and reduce VR sickness of the user. By
immersive telepresence we refer to a situation where a 360\textdegree~camera on
top of a mobile robot is streaming video and audio into a head-mounted display
worn by a remote user possibly far away. Thus, it enables the user to be
present at the robot's location, look around by turning the head and
communicate with people near the robot. By unwinding the rotations of the
camera frame, the user's viewpoint is not changed when the robot rotates. The
user can change her viewpoint only by physically rotating in her local setting;
as visual rotation without the corresponding vestibular stimulation is a major
source of VR sickness, physical rotation by the user is expected to reduce VR
sickness. We implemented unwinding the rotations for a simulated robot
traversing a virtual environment and ran a user study (N=34) comparing
unwinding rotations to user's viewpoint turning when the robot turns. Our
results show that the users found unwound rotations more preferable and
comfortable and that it reduced their level of VR sickness. We also present
further results about the users' path integration capabilities, viewing
directions, and subjective observations of the robot's speed and distances to
simulated people and objects.
【6】 Degrees of Freedom Analysis of Mechanisms using the New Zebra Crossing Method
标题：用新斑马线交叉法进行机构自由度分析
链接：https://arxiv.org/abs/2201.02352
备注：31 pages and 17 figures
摘要：机动性是机构的一个基本特性，必须对其进行分析才能找到自由度。本文提出了一种快速计算机构自由度的方法。该机制的表现方式类似于斑马线。提出了一种从斑马线图中确定移动性的算法。该算法考虑了黑色面片之间的面片数、连接到固定连杆的关节数以及机构中的回路数。已经讨论了一些使用广泛使用的经典Kutzbach-Grubler公式无法给出预期结果的情况。
摘要：Mobility, which is a basic property for a mechanism has to be analyzed to
find the degrees of freedom. A quick method for calculation of degrees of
freedom in a mechanism is proposed in this work. The mechanism is represented
in a way that resembles a zebra crossing. An algorithm is proposed which is
used to determine the mobility from the zebra crossing diagram. This algorithm
takes into account the number of patches between the black patches, the number
of joints attached to the fixed link and the number of loops in the mechanism.
A number of cases have been discussed which fail to give the desired results
using the widely used classical Kutzbach-Grubler formula.
【7】 Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation
标题：用于车辆导航的高质量运动规划控制的数据高效学习
链接：https://arxiv.org/abs/2201.02254
备注：None
摘要：本文旨在提高用于车辆系统的动力学规划器的路径质量和计算效率。它提出了一个学习框架，用于在基于采样的动态系统运动规划器的扩展过程中识别有希望的控制。离线时，学习过程被训练为在当前状态和本地目标状态之间的输入差向量没有障碍物的情况下，返回达到本地目标状态（即航路点）的最高质量控制。数据生成方案提供了目标离散度的界限，并使用状态空间修剪来确保高质量的控制。通过关注系统的动态，该过程是数据高效的，对于一个动态系统来说只发生一次，因此它可以用于具有模块化扩展功能的不同环境。这项工作将所提出的学习过程与a）一个探索性扩展函数相结合，该函数生成在可达空间上具有偏置覆盖的航路点，以及b）一个移动机器人的开发性扩展函数，该函数使用中轴信息生成航路点。本文评估了一阶和二阶差动传动系统的学习过程和相应的规划。结果表明，与具有随机控制的基诺动力学规划相比，本文提出的学习与规划相结合的方法可以在更少的迭代次数和计算时间内生成更高质量的路径。
摘要：This paper aims to improve the path quality and computational efficiency of
kinodynamic planners used for vehicular systems. It proposes a learning
framework for identifying promising controls during the expansion process of
sampling-based motion planners for systems with dynamics. Offline, the learning
process is trained to return the highest-quality control that reaches a local
goal state (i.e., a waypoint) in the absence of obstacles from an input
difference vector between its current state and a local goal state. The data
generation scheme provides bounds on the target dispersion and uses state space
pruning to ensure high-quality controls. By focusing on the system's dynamics,
this process is data efficient and takes place once for a dynamical system, so
that it can be used for different environments with modular expansion
functions. This work integrates the proposed learning process with a) an
exploratory expansion function that generates waypoints with biased coverage
over the reachable space, and b) proposes an exploitative expansion function
for mobile robots, which generates waypoints using medial axis information.
This paper evaluates the learning process and the corresponding planners for a
first and second-order differential drive systems. The results show that the
proposed integration of learning and planning can produce better quality paths
than kinodynamic planning with random controls in fewer iterations and
computation time.
【8】 Multi-modal data fusion of Voice and EMG data for Robotic Control
标题：机器人控制中语音和肌电数据的多模态数据融合
链接：https://arxiv.org/abs/2201.02237
摘要：可穿戴电子设备在不断发展，并正在增加人类与技术的融合。这些灵活和可弯曲的装置有多种形式，可感知并测量人体的生理和肌肉变化，并可使用这些信号进行机器控制。肌肉手势带就是这样一种设备，它使用肌电信号捕获肌电图数据（EMG），并通过一些预定义的手势将其转换为输入信号。在多模式环境中使用该设备不仅可以增加借助该设备可以完成的可能工作类型，还可以帮助提高所执行任务的准确性。本文讨论了通过麦克风和肌电带分别捕获的语音和肌电信号等输入模式的融合，以控制机械臂。文中还给出了实验结果及其性能分析的精度。
摘要：Wearable electronic equipment is constantly evolving and is increasing the
integration of humans with technology. Available in various forms, these
flexible and bendable devices sense and can measure the physiological and
muscular changes in the human body and may use those signals to machine
control. The MYO gesture band, one such device, captures Electromyography data
(EMG) using myoelectric signals and translates them to be used as input signals
through some predefined gestures. Use of this device in a multi-modal
environment will not only increase the possible types of work that can be
accomplished with the help of such device, but it will also help in improving
the accuracy of the tasks performed. This paper addresses the fusion of input
modalities such as speech and myoelectric signals captured through a microphone
and MYO band, respectively, to control a robotic arm. Experimental results
obtained as well as their accuracies for performance analysis are also
presented.
【9】 PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals
标题：PIEEG：把树莓PI变成脑机接口来测量生物信号
链接：https://arxiv.org/abs/2201.02228
摘要：本文介绍了一种廉价、高精度，但同时易于维护的PIEEG板，用于将树莓类型转换为脑-机接口。该屏蔽允许测量和处理八个实时EEG（脑电图）信号。我们使用最流行的编程语言C、C++和Python来读取设备记录的信号。尽可能完整、清晰地演示了读取EEG信号的过程。该设备可以方便地用于机器学习爱好者创建项目，利用思维能力控制机器人和机械肢体。我们将在GitHub上发布用例(https://github.com/Ildaron/EEGwithRaspberryPI)用于控制机器人、无人驾驶飞行器，更简单的是利用思维的力量。
摘要：This paper presents an inexpensive, high-precision, but at the same time,
easy-to-maintain PIEEG board to convert a RaspberryPI to a Brain-computer
interface. This shield allows measuring and processing eight real-time EEG
(Electroencephalography) signals. We used the most popular programming
languages - C, C++ and Python to read the signals, recorded by the device . The
process of reading EEG signals was demonstrated as completely and clearly as
possible. This device can be easily used for machine learning enthusiasts to
create projects for controlling robots and mechanical limbs using the power of
thought. We will post use cases on GitHub
(https://github.com/Ildaron/EEGwithRaspberryPI) for controlling a robotic
machine, unmanned aerial vehicle, and more just using the power of thought.
【10】 Investigation of the Relationship Between Localization Accuracy and Sensor Array
标题：定位精度与传感器阵列关系的研究
链接：https://arxiv.org/abs/2201.02372
摘要：磁定位方法得到了广泛的研究，它主要是基于磁源产生的磁场的精确映射。实验中影响定位精度的因素很多。因此，本文试图通过不同的实验研究定位精度与传感器阵列之间的关系。该系统采用小磁铁作为磁源，基于磁偶极子模型建立了磁定位系统的数学模型来估计磁场。采用Levenberg-Marquardt算法构造了磁定位目标函数，并进行了对比实验。实验结果表明：当传感器均匀分布在磁体周围时，其定位精度高于传感器阵列的其他布局，平均定位误差为0.47mm，平均定位误差为0.92°。
摘要：The magnetic localization method has been widely studied, which is mainly
based on the accurate mapping of the magnetic field generated by magnetic
sources. Many factors affect localization accuracy in the experiment.
Therefore, this paper tends to study the relationship between localization
accuracy and sensor array with different experiments. This system uses a small
magnet as the magnetic source, and the mathematical model of the magnetic
positioning system is established based on the magnetic dipole model to
estimate the magnetic field. The Levenberg-Marquardt algorithm was used to
construct a magnetic positioning objective function for comparison experiments.
Experimental results show:When the sensor is evenly distributed around the
magnet, the positioning accuracy is higher than other layout of the sensor
array, the average localization error is 0.47mm and the average orientation
error is 0.92 degree.
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