Amazon Technologies, Inc.

Pradeep Krishna Yarlagadda

Barry James O'Brien

James Christopher Curlander

Marshall Friend Tappen

Jean-Guillaume Dominique Durand

Michael Donoser

Cédric Philippe Charles Jean Ghislain Archambeau

Ralf Herbrich

Ishay Kamon

Francesco Callari

A sensor system may include first and second sensors configured to be coupled to a vehicle and generate respective first and second sensor signals indicative of operation of the vehicle. The sensor system may also include a sensor anomaly detector including an anomalous sensor model configured to receive the first and second sensor signals and determine that one or more of the first sensor or the second sensor is an anomalous sensor generating inaccurate sensor data. The sensor system may also be configured to identify one or more of the first sensor or the second sensor as the anomalous sensor generating inaccurate sensor data.

Identifying anomalous sensors

Described are systems and methods for generating training data that is used to train a machine learning system to detect moving objects represented in sensor data. The system and methods utilize position data received from a target vehicle to determine data points within sensor data that represents that target vehicle. For example, a station at a known location may receive Automatic Dependent Surveillance-Broadcast ('ADS-B') data (position data) corresponding to a target vehicle that is within the field of view of a station sensor, such as a camera. The position data may then be correlated with the sensor data and projected into the sensor data to determine data points within the sensor data that represent the target vehicle. Those data points are then labeled to indicate the location, size, and/or shape of the target vehicle as represented in the sensor data, thereby producing training that may be provided to train a machine learning algorithm or system to detect moving objects, such as aircraft.

Producing training data for machine learning

A machine learning engine may correlate characteristics of obstacles identified during remotely piloted UAV flights with manual course deviations performed for obstacle avoidance. An obstacle detection application may access computer vision footage to determine notable characteristics (e.g. a direction of travel and/or velocity) of obstacles identified during the piloted UAV flights. A deviation characteristics application may access flight path information identify course deviations performed by a pilot in response to the obstacles. A machine learning engine may use the obstacle characteristic data and the deviation characteristics data as training data to generate an optimal course deviation model to use by an autopilot module to autonomously avoid obstacles during autonomous UAV flights. In creating the optimal deviation model, the training data may be processed by the machine learning engine to identify correlations between certain types of manual course deviations performed to avoid certain types of obstacles.

Autonomous UAV obstacle avoidance using machine learning from piloted UAV flights

Active sensor fusion systems and methods may include a plurality of sensors, a plurality of detection algorithms, and an active sensor fusion algorithm. Based on detection hypotheses received from the plurality of detection algorithms, the active sensor fusion algorithm may instruct or direct modifications to one or more of the plurality of sensors or the plurality of detection algorithms. In this manner, operations of the plurality of sensors or processing of the plurality of detection algorithms may be refined or adjusted to provide improved object detection with greater accuracy, speed, and reliability.

Active sensor fusion systems and methods for object detection

Described are techniques for storing and retrieving items using a robotic device for moving items. Any combinations of image data depicting a manipulator interacting with an item, sensor data from sensors instrumenting the manipulator or item, item data regarding characteristics of the item, and constraint data relating to characteristics of the robotic device may be used to generate one or more configurations for the robotic device. The configurations may include points of contact and force vectors for contacting the item using the robotic device.

System for configuring a robotic device for moving items

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator or the item may be used to generate one or more configurations for the robotic manipulator. The configurations may include points of contact and force vectors for contacting the item using the robotic manipulator.

System for configuring a robotic manipulator

Described are techniques for storing and retrieving items using a robotic manipulator. Images depicting a human interacting with an item, sensor data from sensors instrumenting the human or item, data regarding physical characteristics of the item, and constraint data relating to the robotic manipulator may be used to generate one or more configurations for the robotic manipulator. Points of contact and force vectors of the configurations may correspond to the points of contact and force vectors determined from the images and sensor data.

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Redmond, WA, United States of America

Pradeep Krishna Yarlagadda