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    Transitioning a mixed-mode vehicle to autonomous mode
    71.
    发明授权
    Transitioning a mixed-mode vehicle to autonomous mode 有权
    将混合模式车辆转变为自主模式

    公开(公告)号：US09383749B2

    公开(公告)日：2016-07-05

    申请号：US14467201

    申请日：2014-08-25

    Applicant: Google Inc.

    Inventor： Luis Ricardo Prada Gomez ,  Nathaniel Fairfield ,  Andy Szybalski ,  Philip Nemec ,  Christopher Urmson

    IPC: G05D1/00 ,  B62D15/02

    CPC classification number: G05D1/0022 ,  B62D15/0285 ,  G05D1/0061 ,  G05D2201/02

    Abstract: Disclosed are methods and devices for transitioning a mixed-mode autonomous vehicle from a human driven mode to an autonomously driven mode. Transitioning may include stopping a vehicle on a predefined landing strip and detecting a reference indicator. Based on the reference indicator, the vehicle may be able to know its exact position. Additionally, the vehicle may use the reference indictor to obtain an autonomous vehicle instruction via a URL. After the vehicle knows its precise location and has an autonomous vehicle instruction, it can operate in autonomous mode.

    Abstract translation: 公开了用于将混合模式自主车辆从人类驾驶模式转换到自主驾驶模式的方法和装置。 过渡可能包括在预定义的着陆带上停止车辆并检测参考指示符。 基于参考指示器，车辆可能能够知道其确切的位置。 此外，车辆可以使用参考指示器通过URL获得自主车辆指令。 在车辆知道其精确位置并具有自主车辆指令之后，它可以在自主模式下操作。

    Systems and Methods for Capturing Images and Annotating the Captured Images with Information
    72.
    发明申请
    Systems and Methods for Capturing Images and Annotating the Captured Images with Information 有权
    捕获图像和使用信息注释捕获的图像的系统和方法

    公开(公告)号：US20160167226A1

    公开(公告)日：2016-06-16

    申请号：US14572712

    申请日：2014-12-16

    Applicant: iRobot Corporation

    Inventor： Mark S. Schnittman

    IPC: B25J9/16 ,  G06K9/00 ,  G06T7/00 ,  B25J5/00

    CPC classification number: G06K9/00671 ,  B25J5/00 ,  B25J19/023 ,  G05D1/0088 ,  G05D1/0246 ,  G05D1/0274 ,  G05D2201/02 ,  G06K9/00201 ,  G06K9/00664 ,  G06K9/6262 ,  G06K9/66 ,  G06T7/55 ,  Y10S901/01 ,  Y10S901/47

    Abstract: The present teachings provide an autonomous mobile robot that includes a drive configured to maneuver the robot over a ground surface within an operating environment; a camera mounted on the robot having a field of view including the floor adjacent the mobile robot in the drive direction of the mobile robot; a frame buffer that stores image frames obtained by the camera while the mobile robot is driving; and a memory device configured to store a learned data set of a plurality of descriptors corresponding to pixel patches in image frames corresponding to portions of the operating environment and determined by mobile robot sensor events.

    Abstract translation: 本教导提供了一种自主移动机器人，其包括配置成在操作环境内在地面上操纵机器人的驱动器; 安装在机器人上的照相机，其具有在移动机器人的驱动方向上包括邻近移动机器人的地板的视野; 帧缓冲器，其存储在移动机器人驱动时由相机获得的图像帧; 以及存储装置，被配置为存储对应于所述操作环境的部分并由移动机器人传感器事件确定的图像帧中的与像素块对应的多个描述符的学习数据集。

    Apparatus and methods for control of robot actions based on corrective user inputs
    73.
    发明授权
    Apparatus and methods for control of robot actions based on corrective user inputs 有权
    基于校正用户输入来控制机器人动作的装置和方法

    公开(公告)号：US09358685B2

    公开(公告)日：2016-06-07

    申请号：US14171762

    申请日：2014-02-03

    Applicant: BRAIN CORPORATION

    Inventor： Philip Meier ,  Jean-Baptiste Passot ,  Borja Ibarz Gabardos ,  Patryk Laurent ,  Oleg Sinyavskiy ,  Peter O'Connor ,  Eugene Izhikevich

    IPC: G05B19/04 ,  B25J9/16 ,  G06N99/00 ,  G06N3/00

    CPC classification number: B25J9/163 ,  B25J9/1602 ,  B25J9/161 ,  B25J9/1656 ,  G05B2219/40116 ,  G05D1/0033 ,  G05D1/0088 ,  G05D2201/02 ,  G06N3/008 ,  G06N99/005 ,  Y10S901/01 ,  Y10S901/46

    Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

    Abstract translation: 机器人有能力执行广泛的有用任务，如工厂自动化，清洁，交付，辅助护理，环境监测和娱乐。 使机器人在新环境中执行新任务通常需要大量新的软件来编写，通常由专家小组编写。 如果未来的技术可以赋予人们对软件编码有限或不了解的机会来训练机器人来执行定制任务，这将是有价值的。 本发明的一些实施方案提供了响应于用户的校正命令以生成和改进用于基于传感器数据输入确定适当动作的策略的方法和系统。 完成学习后，系统可以通过从感官数据中导出控制命令来生成控制命令。 使用学习的控制策略，机器人可以自主行为。

    APPARATUS AND METHODS FOR TRAINING OF ROBOTS
    74.
    发明申请
    APPARATUS AND METHODS FOR TRAINING OF ROBOTS 有权
    装备和训练机器人的方法

    公开(公告)号：US20160096272A1

    公开(公告)日：2016-04-07

    申请号：US14588168

    申请日：2014-12-31

    Applicant: BRAIN CORPORATION

    Inventor： Andrew T. Smith ,  Vadim Polonichko

    IPC: B25J9/16

    CPC classification number: B25J9/163 ,  B25J9/1607 ,  B25J9/1666 ,  B25J9/1697 ,  G05D1/0088 ,  G05D1/0246 ,  G05D2201/02 ,  G06N3/00 ,  G06N3/008 ,  G06N3/049 ,  Y10S901/01 ,  Y10S901/03 ,  Y10S901/09 ,  Y10S901/47

    Abstract: A random k-nearest neighbors (RKNN) approach may be used for regression/classification model wherein the input includes the k closest training examples in the feature space. The RKNN process may utilize video images as input in order to predict motor command for controlling navigation of a robot. In some implementations of robotic vision based navigation, the input space may be highly dimensional and highly redundant. When visual inputs are augmented with data of another modality that is characterized by fewer dimensions (e.g., audio), the visual data may overwhelm lower-dimension data. The RKNN process may partition available data into subsets comprising a given number of samples from the lower-dimension data. Outputs associated with individual subsets may be combined (e.g., averaged). Selection of number of neighbors, subset size and/or number of subsets may be used to trade-off between speed and accuracy of the prediction.

    Abstract translation: 随机k最近邻（RKNN）方法可用于回归/分类模型，其中输入包括特征空间中最接近的k个训练样本。 RKNN过程可以利用视频图像作为输入，以便预测用于控制机器人的导航的马达命令。 在基于机器人视觉的导航的一些实现中，输入空间可以是高度尺寸和高度冗余的。 当视觉输入用另一种具有较少尺寸（例如，音频）特征的模态的数据进行增强时，视觉数据可能会压倒较低维度的数据。 RKNN过程可以将可用数据划分为包含来自较低维数据的给定数量样本的子集。 与各个子集相关联的输出可以组合（例如，平均）。 选择邻居数量，子集大小和/或子集数可用于在速度和预测精度之间进行权衡。

    Transitioning a mixed-mode vehicle to autonomous mode
    78.
    发明授权
    Transitioning a mixed-mode vehicle to autonomous mode 有权
    将混合模式车辆转变为自主模式

    公开(公告)号：US08849493B2

    公开(公告)日：2014-09-30

    申请号：US13660071

    申请日：2012-10-25

    Applicant: Google Inc.

    Inventor： Luis Ricardo Prada Gomez ,  Nathaniel Fairfield ,  Andy Szybalski ,  Philip Nemec ,  Christopher Urmson

    IPC: G01C22/00

    CPC classification number: G05D1/0022 ,  B62D15/0285 ,  G05D1/0061 ,  G05D2201/02

    Abstract: Disclosed are methods and devices for transitioning a mixed-mode autonomous vehicle from a human driven mode to an autonomously driven mode. Transitioning may include stopping a vehicle on a predefined landing strip and detecting a reference indicator. Based on the reference indicator, the vehicle may be able to know its exact position. Additionally, the vehicle may use the reference indictor to obtain an autonomous vehicle instruction via a URL. After the vehicle knows its precise location and has an autonomous vehicle instruction, it can operate in autonomous mode.

    Abstract translation: 公开了用于将混合模式自主车辆从人类驾驶模式转换到自主驾驶模式的方法和装置。 过渡可能包括在预定义的着陆带上停止车辆并检测参考指示符。 基于参考指示器，车辆可能能够知道其确切的位置。 此外，车辆可以使用参考指示器通过URL获得自主车辆指令。 在车辆知道其精确位置并具有自主车辆指令之后，它可以在自主模式下操作。

    LOW LATENCY DATA LINK SYSTEM AND METHOD
    79.
    发明申请
    LOW LATENCY DATA LINK SYSTEM AND METHOD 审中-公开
    低数据链接系统和方法

    公开(公告)号：US20140249695A1

    公开(公告)日：2014-09-04

    申请号：US14188575

    申请日：2014-02-24

    Applicant: RoboteX Inc.

    Inventor： Adam M. GETTINGS ,  Randy Wai TING ,  Kito BERG-TAYLOR ,  Joel D. BRINTON ,  Taylor J. PENN

    IPC: G05D1/00

    CPC classification number: G05D1/0022 ,  G05D1/0038 ,  G05D2201/02 ,  Y10S901/01 ,  Y10S901/47

    Abstract: Devices and methods for a low latency data telecommunication system and method for video, audio control data and other data for use with one or more robots and remote controls are disclosed. The data transmission can be digital. The data telecommunication system can enable the use of multiple robots and multiple remote controls in the same location with encrypted data transmission.

    Abstract translation: 公开了用于低延迟数据电信系统的装置和方法以及用于与一个或多个机器人和遥控器一起使用的视频，音频控制数据和其它数据的方法。 数据传输可以是数字传输。 数据电信系统可以在加密数据传输的同一位置使多个机器人和多个遥控器使用。

    Transitioning a mixed-mode vehicle to autonomous mode
    80.
    发明授权
    Transitioning a mixed-mode vehicle to autonomous mode 有权
    将混合模式车辆转变为自主模式

    公开(公告)号：US08321067B1

    公开(公告)日：2012-11-27

    申请号：US13293905

    申请日：2011-11-10

    Applicant: Luis Ricardo Prada Gomez ,  Nathaniel Fairfield ,  Andy Szybalski ,  Philip Nemec ,  Christopher Urmson

    Inventor： Luis Ricardo Prada Gomez ,  Nathaniel Fairfield ,  Andy Szybalski ,  Philip Nemec ,  Christopher Urmson

    IPC: G05D1/00

    CPC classification number: G05D1/0022 ,  B62D15/0285 ,  G05D1/0061 ,  G05D2201/02

    Abstract: Disclosed are methods and devices for transitioning a mixed-mode autonomous vehicle from a human driven mode to an autonomously driven mode. Transitioning may include stopping a vehicle on a predefined landing strip and detecting a reference indicator. Based on the reference indicator, the vehicle may be able to know its exact position. Additionally, the vehicle may use the reference indictor to obtain an autonomous vehicle instruction via a URL. After the vehicle knows its precise location and has an autonomous vehicle instruction, it can operate in autonomous mode.

    Abstract translation: 公开了用于将混合模式自主车辆从人类驾驶模式转换到自主驾驶模式的方法和装置。 过渡可能包括在预定义的着陆带上停止车辆并检测参考指示符。 基于参考指示器，车辆可能能够知道其确切的位置。 此外，车辆可以使用参考指示器通过URL获得自主车辆指令。 在车辆知道其精确位置并具有自主车辆指令之后，它可以在自主模式下操作。

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