Qualcomm Incorporated

Matthew Hyatt Turpin

Stephen Marc Chaves

Daniel Warren Mellinger, Iii

Michael Joshua Shomin

Aleksandr Kushleyev

Rizwan Ehtesham Ahmed

Travis Van Schoyck

Paul Daniel Martin

John Anthony Dougherty

Michael Franco Taveira

Ross Eric Kessler

Jonathan Paul Davis

Moussa Ben Coulibaly

Daniel Warren Mellinger Iii

Methods, systems, and devices for automatically customizing operation of a robotic vehicle are described. The method may include identifying an operator, retrieving an operator profile and associated metadata for the operator from a database, where the metadata includes operator habit information, and configuring the robotic vehicle based on existing preference-based and performance-based settings, where the existing preference-based and performance-based settings are based on the metadata. The methods may include identifying operator habit information during operation of the robotic vehicle, deriving updated preference-based and performance-based settings for the operator based on the identified operator habit information, and providing, to the database, modifications to the metadata associated with the operator profile of the operator.

Systems and methods for automatically customizing operation of a robotic vehicle

Various embodiments include processing devices and methods for managing cleaning behavior by a cleaning robot. In some embodiments, a processor of the cleaning robot may obtain user planning information and user location information from one or more information sources external to the cleaning robot. The processor may analyze the user planning information and the user location information. The processor may determine one or more cleaning parameters for the cleaning robot based on the analysis of the user planning information and the user location information. The processor may generate an instruction for the cleaning robot to schedule an operation of the cleaning robot based on the one or more cleaning parameters. The processor may execute the generated instruction to perform the operation of the cleaning robot.

Managing cleaning robot behavior

Embodiments include devices and methods for vehicle collision avoidance. A processor of the vehicle may receive lidar sensor data comprising one or more points including a distance and a direction from the vehicle to the one or more points. The processor may determine a velocity constraint for each of the one or more points based on the distance and direction from the vehicle to the one or more points. Based on a navigation instruction, the processor may determine one or more velocity solutions that satisfy the one or more velocity constraints. The processor may select a velocity solution from the determined one or more velocity solutions based on the navigation instruction, and may adjust a vehicle velocity based on the selected velocity solution.

Vehicle collision avoidance

Methods, systems, and devices for robotic navigation are described. A robotic device such as a robotic vacuum or a robotic assistant may navigate a first surface. In some cases, navigating the first surface may include removing debris from the first surface. The robotic device may identify a location of a track that connects the first surface to a second surface that is vertically displaced from the first surface. The robotic device may engage the track based at least in part on the identified location. The robotic device may ascend to the second surface by activating an actuator and navigate the second surface (e.g., may remove debris from the second surface, may map the second surface, etc.).

Robotic stair lifts

Some embodiments include methods for customizing operation of the robotic vehicle for an operator. Such embodiments may include identifying a current operator of the robotic vehicle, configuring the robotic vehicle based on metadata associated with an operator profile for the operator, determining whether the operator has changed, and if so, identifying the new operator, deriving updated preference-based settings and performance-based settings for the new operator, and updating configurations of the robotic vehicle accordingly. Some embodiments may include identifying a current operator, retrieving an operator profile and associated metadata for the operator from a central database, configuring the robotic vehicle based on preference-based and performance-based settings, analyzing input data during operation, storing identified real-time operator habit information during operation of the robotic vehicle, deriving updated preference-based and performance-based settings for the operator; and uploading to the central database modifications to the metadata associated with the operator profile of the operator.

Various embodiments may include methods of using image data to estimate motion of a vehicle observed within camera images, such as images captured by a vehicle navigation system of a host vehicle. Various embodiments may include a camera capturing a sequence of images including the observed vehicle, and a processor performing image processing to identify a wheel of the observed vehicle, and determining a rate of rotation of the wheel based on changes in orientation of the wheel between at least two images within the sequence of images. The processor may further determine a speed of the observed vehicle based on the wheel's rate of rotation and diameter. The processor may further determine a direction of travel and/or turning rate of the observed vehicle by determining relative angles of wheels of the observed vehicle in at least one image.

State and position prediction of observed vehicles using optical tracking of wheel rotation

Various embodiments include methods for performing temperature calibration of a first temperature sensitive unit with an electronic device having a first processing unit that is thermally coupled to the first temperature sensitive unit. Various embodiments may include determining a current temperature of the first temperature sensitive unit, determining a processing load for the first processing unit based on the current temperature and a target temperature, applying the determined processing load to the first processing unit to vary a temperature of the first temperature sensitive unit, and determining a temperature bias for the first temperature sensitive unit at the temperature of the first temperature sensitive unit based on an output of the first temperature sensitive unit.

Utilizing processing units to control temperature

Embodiments include devices and methods for vehicle collision avoidance. A processor of the vehicle may receive sensor data comprising one or more points. The processor may determine a distance and a direction from the vehicle to the one or more points. The processor may determine a velocity constraint for each of the one or more points based on the determined distance and direction from the vehicle to the one or more points. Based on a navigation instruction, the processor may determine one or more velocity solutions that satisfy the one or more velocity constraints. The processor may select a velocity solution from the determined one or more velocity solutions based on the navigation instruction, and may adjust a vehicle velocity based on the selected velocity solution.

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Philadelphia, PA, United States of America

Matthew Hyatt Turpin