Abstract:
Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.
Abstract:
Methods and systems for determining a status of a component of a device are provided. An example method includes triggering an action of a component of a device, and responsively receiving information associated with the action of the component from a sensor. The method further includes a computing system having a processor and a memory comparing the information with calibration data and determining a status of the component based on the comparison. In some examples, the calibration data may include information derived from data received from a pool of one or more devices utilizing same or similar components as the component. The determined status may include information associated with a performance of the component with respect to performances of same or similar components of the pool of devices. In one example, the device may self-calibrate the component based on the status.
Abstract:
Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.
Abstract:
Examples disclose a method and system to transform a colored point cloud to a 3D textured mesh. The method may be executable to identify a location on a 2D image of an object, identify a location on a 3D image of the object, and determine a color associated with the location on the 2D image. Determining a color may include receiving data associated with a simulation of a plurality of rays cast on the 3D image, identifying a color of the location on the 3D image associated with the received data, identifying a confidence level associated with the identified color of the location on the 3D image, and associating the identified color of the location on the 3D image with the location on the 2D image.
Abstract:
Methods and systems for determining a status of a component of a robotic device are provided. An example method includes triggering an action of a component of a robotic device, and responsively receiving information associated with the action of the component from a sensor. The method further includes a computing system having a processor and a memory comparing the information with calibration data and determining a status of the component based on the comparison. In some examples, the calibration data may include information derived from data received from a pool of one or more robotic devices utilizing same or similar components as the component. The determined status may include information associated with a performance of the component with respect to performances of same or similar components of the pool of robotic devices. In one example, the robotic device may self-calibrate the component based on the status.
Abstract:
Methods and systems for allocating tasks to robotic devices are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices and in a computing system configured to access a processor and memory, determining information associated with a health level for the plurality of robotic devices based on the information associated with the task logs. A health level for a given robotic device may be proportional to a current level of ability to perform a function, which may change over a lifespan of the given robotic device. Information associated with a plurality of tasks to be performed by one or more or the robotic devices may also be determined. The computing system may optimize an allocation of the plurality of tasks such that a high precision task may be allocated to a robotic device having a greater current health level than another robotic device.
Abstract:
Methods and systems for allocating tasks to robotic devices are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices and in a computing system configured to access a processor and memory, determining information associated with a health level for the plurality of robotic devices based on the information associated with the task logs. A health level for a given robotic device may be proportional to a current level of ability to perform a function, which may change over a lifespan of the given robotic device. Information associated with a plurality of tasks to be performed by one or more or the robotic devices may also be determined. According to the method, the computing system may optimize an allocation of the plurality of tasks such that a high precision task may be allocated to a robotic device having a greater current health level than another robotic device.
Abstract:
Methods and systems for interacting with multiple three-dimensional (3D) object data models are provided. An example method may involve providing to a display device for display a first 3D object data model and a second 3D object data model. Information associated with a modification to the first 3D object data model may be received. Based on the received information, a same change may be applied to the first 3D object data model and applied to the second 3D object data model to obtain a first modified 3D object data model and a second modified 3D object data model. According to the method, the first modified 3D object data model and the second modified 3D object data model may be provided to the display device for substantially simultaneous display.
Abstract:
Methods and systems for encoding and compressing 3D object data models are provided. An example method may involve receiving 3D mesh data for an object that includes geometry coordinates for a surface of the object. Additionally, material properties may be associated with the geometry coordinates. The method may also include identifying multiple portions of the mesh data based on the material properties associated with the geometry coordinates. For example, a given group of adjacent geometry coordinates having common material properties may be identified as a given portion. For at least some of the identified portions of the mesh data, the method may further include encoding information related to an identified portion of the mesh data and compressing the encoded information into a file of compressed geometric data.
Abstract:
Methods and systems for writing, interpreting, and translating three-dimensional (3D) scenes are provided. An example method may involve accessing data associated with a three-dimensional (3D) scene that includes one or more objects of the 3D scene and one or more rendering effects for the one or more objects. Requests for assets and instructions associated with rendering the one or more objects based on the data associated with the 3D scene may be determined and sent to a server. Additionally, the method may include receiving from the server assets and instructions that facilitate rendering the one or more objects based on the one or more rendering effects. According to the method, the one or more objects of the 3D scene may be rendered based on the received instructions and the received assets.