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公开(公告)号：US20170341645A1
公开(公告)日：2017-11-30
申请号：US15444866
申请日：2017-02-28
Applicant: KABUSHIKI KAISHA TOSHIBA
Inventor： Kaoru SUGITA , Masahiro SEKINE , Tomoki WATANABE
IPC: B60W30/095 , B60R21/0132 , G05D1/00 , G06K9/52 , G06K9/00 , G06T7/70 , G06K9/46 , B60R21/01 , B60R21/00
CPC classification number: B60W30/0953 , B60R2021/0027 , B60W2520/10 , B60W2520/105 , B60W2550/10 , B60W2550/308 , G05D1/0055 , G05D2201/02 , G06K9/00335 , G06K9/00664 , G06K9/00805 , G06K9/52 , G06K2009/4666
Abstract: According to one embodiment, an information processor includes a memory and processing circuitry. The circuitry receives area information indicating a second area in a first area around a movable body apparatus and third areas in the first area, wherein the movable body apparatus is movable in the second area and an object is present in each of the third areas. The circuitry receives movement information including at least one of a velocity, a movement direction or an acceleration of the apparatus. The circuitry acquires evaluation values each indicative of a damage to be caused when the apparatus collides with each object in the third areas, and determines, based on the evaluation values, a position corresponding to a first object which causes a least damage.
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公开(公告)号：US20170326726A1
公开(公告)日：2017-11-16
申请号：US15470629
申请日：2017-03-27
Applicant: Brain Corporation
Inventor： Oyvind Grotmol , Oleg Sinyavskiy
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: An apparatus and methods for training and/or operating a robotic device to follow a trajectory. A robotic vehicle may utilize a camera and stores the sequence of images of a visual scene seen when following a trajectory during training in an ordered buffer. Motor commands associated with a given image may be stored. During autonomous operation, an acquired image may be compared with one or more images from the training buffer in order to determine the most likely match. An evaluation may be performed in order to determine if the image may correspond to a shifted (e.g., left/right) version of a stored image as previously observed. If the new image is shifted left, right turn command may be issued. If the new image is shifted right then left turn command may be issued.
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公开(公告)号：US09711985B1
公开(公告)日：2017-07-18
申请号：US14673764
申请日：2015-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Joseph Edwin Johnson , Michael Brian Stoops , Benjamin Schwartz , Nathan Eugene Masters , Shiblee Imtiaz Hasan
CPC classification number: H02J7/0054 , B25J9/161 , G05D1/0297 , G05D2201/02 , G05D2201/0211 , H02J7/0003 , H02J7/0004 , H02J7/0042 , H04L67/10 , H04W4/80 , Y10S901/01
Abstract: A method, apparatus, and/or system for providing an action with respect to a mobile device using a robotic device that tracks the user. In accordance with at least one embodiment, a request to perform an action with respect to an electronic device is received. Information may be sent to one or more robotic devices within a proximity of the electronic device. A robotic device of the one or more robotic devices may be selected to perform the action. An indication may be received from the robotic device that indicates that the user has interacted with the robotic device. Instructions may be sent to the robotic device to perform the action with respect to the electronic device. A location of the user may be tracked while charging is performed by the robotic device. The robotic device may be instructed to follow the user at a threshold distance from the user.
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公开(公告)号：US09687984B2
公开(公告)日：2017-06-27
申请号：US14588168
申请日：2014-12-31
Applicant: BRAIN CORPORATION
Inventor： Andrew T. Smith , Vadim Polonichko
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.
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公开(公告)号：US09630317B2
公开(公告)日：2017-04-25
申请号：US14244888
申请日：2014-04-03
Applicant: BRAIN CORPORATION
Inventor： Eugene M. Izhikevich , Patryk Laurent , Micah Richert , Csaba Petre
CPC classification number: B25J9/163 , B25J9/161 , G05D1/0022 , G05D1/0088 , G05D1/0246 , G05D2201/02 , G06N3/008 , G06N3/049 , G06N99/005
Abstract: Robotic devices may be operated by users remotely. A learning controller apparatus may detect remote transmissions comprising user control instructions. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The learning apparatus may monitor one or more wavelength (infrared light, radio channel) and detect transmissions from user remote control device to the robot during its operation by the user. The learning apparatus may be configured to develop associations between the detected user remote control instructions and actions of the robot for given context. When a given sensory context occurs, the learning controller may automatically provide control instructions to the robot that may be associated with the given context. The provision of control instructions to the robot by the learning controller may obviate the need for user remote control of the robot thereby enabling autonomous operation by the robot.
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Patent Agency Ranking
公开(公告)号：US20160363934A1
公开(公告)日：2016-12-15
申请号：US15248215
申请日：2016-08-26
Applicant: Sikorsky Aircraft Corporation
Inventor： Thomas Zygmant , Jesse J. Lesperance , Ira E. Zoock , James A. Kelly
CPC classification number: G05D1/0061 , B64C39/024 , B64C2201/141 , B64C2201/146 , G05D1/0011 , G05D1/0055 , G05D1/0088 , G05D2201/02 , G08G5/003
Abstract: An aircraft is provided and includes a frame, drive elements configured to drive movements of the frame and a computer configured to receive mission planning and manual commands and to control operations of the drive elements to operate in a safe mode in which mission commands are accepted but manual commands are refused, a manual mode in which mission commands are refused but manual commands are accepted and an enroute mode. The computer is further configured to only allow mode transitions between the safe and manual modes and between the safe and enroute modes.
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公开(公告)号：US09513633B2
公开(公告)日：2016-12-06
申请号：US14646843
申请日：2014-07-30
Applicant: Komatsu Ltd.
Inventor： Katsuyuki Sakai
CPC classification number: G05D1/021 , G05D2201/02 , G08C17/02
Abstract: The present invention provides a work vehicle including a control system that can switch between a first driving mode for allowing the work vehicle to travel in a manned state and a second driving mode for allowing the work vehicle to travel in an unmanned state, wherein the control system controls such that, during an execution of the second driving mode, a number of types of information exchanged by communication in the control system becomes less than that during the first driving mode, or a communication interval of information exchanged by the communication in the control system becomes longer than that during the first driving mode.
Abstract translation: 本发明提供了一种作业车辆，其包括控制系统，该控制系统能够在用于允许作业车辆在载人状态下行驶的第一驾驶模式和允许作业车辆在无人驾驶状态下行驶的第二驾驶模式切换，其中，所述控制 系统控制，使得在第二驱动模式的执行期间，通过控制系统中的通信交换的多种类型的信息变得小于第一驱动模式期间的信息类型，或通过控制中的通信交换的信息的通信间隔 系统变得比在第一驱动模式期间更长。
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公开(公告)号：US09492922B1
公开(公告)日：2016-11-15
申请号：US14673744
申请日：2015-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Joseph Edwin Johnson , Michael Brian Stoops , Benjamin Schwartz , Nathan Eugene Masters , Shiblee Imtiaz Hasan
CPC classification number: H02J7/0054 , B25J9/161 , G05D1/0297 , G05D2201/02 , G05D2201/0211 , H02J7/0003 , H02J7/0004 , H02J7/0042 , H04L67/10 , H04W4/80 , Y10S901/01
Abstract: A method, apparatus, and/or system for providing an action with respect to a mobile device using a robotic device that tracks the user and that interacts with a charging management engine. In accordance with at least one embodiment, a request to perform an action with respect to an electronic device is received. Information may be sent to one or more robotic devices within a proximity of the electronic device. A robotic device of the one or more robotic devices may be selected to perform the action. An indication may be received from the robotic device that indicates that the user has interacted with the robotic device. Instructions may be sent to the robotic device to perform the action with respect to the electronic device.
Abstract translation: 一种用于使用跟踪用户并与计费管理引擎交互的机器人设备来提供关于移动设备的动作的方法，装置和/或系统。 根据至少一个实施例，接收对电子设备执行动作的请求。 信息可以发送到电子设备附近的一个或多个机器人设备。 可以选择一个或多个机器人装置的机器人装置来执行动作。 可以从机器人设备接收到指示用户已经与机器人设备交互的指示。 可以向机器人装置发送指令以执行关于电子设备的动作。
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公开(公告)号：US20160279790A1
公开(公告)日：2016-09-29
申请号：US15174858
申请日：2016-06-06
Applicant: Brain Corporation
Inventor： Philip Meier , Jean-Baptiste Passot , Borja Ibarz Gabardos , Patryk Laurent , Oleg Sinyavskiy , Peter O'Connor , Eugene Izhikevich
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.
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110.发明申请公开(公告)号：US20160096270A1
公开(公告)日：2016-04-07
申请号：US14542391
申请日：2014-11-14
Applicant: Brain Corporation
Inventor： Borja Ibarz Gabardos , Andrew Smith , Peter O'Connor
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 robotic device may be operated by a learning controller comprising a feature learning configured to determine control signal based on sensory input. An input may be analyzed in order to determine occurrence of one or more features. Features in the input may be associated with the control signal during online supervised training. During training, learning process may be adapted based on training input and the predicted output. A combination of the predicted and the target output may be provided to a robotic device to execute a task. Feature determination may comprise online adaptation of input, sparse encoding transformations. Computations related to learning process adaptation and feature detection may be performed on board by the robotic device in real time thereby enabling autonomous navigation by trained robots.
Abstract translation: 机器人设备可以由包括被配置为基于感觉输入来确定控制信号的特征学习的学习控制器操作。 可以分析输入以确定一个或多个特征的发生。 输入中的特征可能与在线监督训练期间的控制信号相关联。 在训练中，学习过程可以根据训练输入和预测输出进行调整。 可以将预测和目标输出的组合提供给机器人装置以执行任务。 特征确定可以包括输入，稀疏编码变换的在线适配。 与学习过程适应和特征检测相关的计算可以由机器人设备实时执行，从而使训练有素的机器人能够进行自主导航。
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