Communicative Self-Guiding Automation
    2.
    发明申请

    公开(公告)号:US20190224850A1

    公开(公告)日:2019-07-25

    申请号:US15877192

    申请日:2018-01-22

    Abstract: A self-guiding automaton includes sensors, a hardware processor, and a memory storing a self-guidance software code. The hardware processor executes the self-guidance software code to detect a location and/or travel path of one or more human being(s) in a pedestrian environment using the sensors, and identify a self -guided path forward in the pedestrian environment based on the location and/or travel path of each human being(s). The self-guidance software code also determines a social cue for communicating the self-guided path forward to the human being(s), detects any change in the location and/or travel path of each human being(s) using the sensors, determines a pedestrian safety score of the self-guided path forward based on the location and/or travel path and any change in the location and/or travel path of each human being(s), and moves the automaton along the self-guided path forward if the pedestrian safety score satisfies a safety threshold.

    Communicative self-guiding automation

    公开(公告)号:US10814487B2

    公开(公告)日:2020-10-27

    申请号:US15877192

    申请日:2018-01-22

    Abstract: A self-guiding automaton includes sensors, a hardware processor, and a memory storing a self-guidance software code. The hardware processor executes the self-guidance software code to detect a location and/or travel path of one or more human being(s) in a pedestrian environment using the sensors, and identify a self-guided path forward in the pedestrian environment based on the location and/or travel path of each human being(s). The self-guidance software code also determines a social cue for communicating the self-guided path forward to the human being(s), detects any change in the location and/or travel path of each human being(s) using the sensors, determines a pedestrian safety score of the self-guided path forward based on the location and/or travel path and any change in the location and/or travel path of each human being(s), and moves the automaton along the self-guided path forward if the pedestrian safety score satisfies a safety threshold.

    Perceptual data association
    6.
    发明授权

    公开(公告)号:US10796195B1

    公开(公告)日:2020-10-06

    申请号:US16745145

    申请日:2020-01-16

    Abstract: Embodiments provide for perceptual data association from at least a first and a second sensor disposed at different positions in an environment, in respective series of local scene graphs that identify characteristics of objects in the environment that are updated asynchronously and merging the series of local scene graphs to form a coherent image of the environment from multiple perspectives.

    Perceptual data association
    7.
    发明授权

    公开(公告)号:US10607105B1

    公开(公告)日:2020-03-31

    申请号:US16366191

    申请日:2019-03-27

    Abstract: Embodiments provide for perceptual data association received from at least a first and a second sensor disposed at different positions in an environment, in respective time series of local scene graphs that identify several characteristics of at least one object in the environment that are updated different rates; merging, at an output rate, characteristics for each given object from the several time series of local scene graphs that are updated at the output rate; merging, at the output rate, characteristics for each given object from the time series of local scene graphs that are updated at rates other than the output rate; and outputting, at the output rate, a time series of global scene graphs including the merged characteristics.

    Map localizing with partially obstructed ranging devices for autonomous robots and vehicles

    公开(公告)号:US09943959B2

    公开(公告)日:2018-04-17

    申请号:US15137735

    申请日:2016-04-25

    Inventor: Kyle G. Freeman

    Abstract: A method for localizing robots and other objects when ranging devices are partially obstructed. The method includes retrieving a digital map of the space and then identifying potential locations for the object in the space. The method involves generating a prediction of expected ranges between the object and surfaces in the space at each of the potential locations. The method includes operating a ranging device to measure ranges to the surfaces in the space and comparing, for each of the predictions, the measured ranges with the expected ranges to identify the most accurate prediction. The comparing includes weighting ranges that are too small neutrally as these are strikes on obstructions, weighting ranges that provide matches positively, and weighting ranges that are too large negatively, and the comparing then involves summing the weighted ranges to identify a most accurate one of the predictions and associated current location for the object.

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