Abstract: Systems and methods for determining strategy modes for autonomous vehicles are described. An autonomous vehicle may detect aspects of other vehicles and aspects of the environment using one or more sensors. The autonomous vehicle may then determine strategy modes of the other vehicles, and select a strategy mode for its own operation based on the determined strategy modes and an operational goal for the autonomous vehicle. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle may further determine elements in the environment and topological constraints associated with the environment, and select the strategy mode for its own operation based thereon.

Abstract: Systems and methods for broadcasting strategy modes of autonomous vehicles are described. An autonomous vehicle may include one or more notification devices that broadcast a selected strategy mode for the autonomous vehicle. The selected strategy mode may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The notification devices may include visual, audio, or signal notification devices. In addition, the autonomous vehicle may broadcast other information associated with the vehicle, such as a position, a velocity, a priority, or an identity of the vehicle.

Abstract: Systems and methods for coordinating operations of a plurality of autonomous vehicles are described. An autonomous vehicle management system may receive information from the plurality of autonomous vehicles related to strategy modes, actions, and the environments. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle management system may then process the received information based on an operational goal for the system as a whole. The autonomous vehicle management system may then instruct modifications to operations of one or more of the plurality of autonomous vehicles to achieve the operational goal for the system.

Abstract: Systems and methods for operating autonomous vehicles are described. An autonomous vehicle may detect strategy modes and/or actions of other vehicles in a local environment. The autonomous vehicle may then select a strategy mode for its operations based on the detected strategy modes and/or actions of other vehicles, and based on an operational goal for the autonomous vehicle. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle may further select an action for its operations based on the selected strategy mode.

Abstract: Systems and methods for receiving objects into inventory are described. The objects are individually scanned to generate scan data. The scan data is processed to determine what the object is (and to add the object into inventory) and also to determine values for one or more additional attributes. The attribute values are stored in a data structure which may be analyzed to detect a change over time.

Abstract: 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.

Abstract: Disclosed are various embodiments for obtaining environmental data and operational data from autonomous vehicles in a roadway. A vehicle state of the autonomous vehicles can be updated using data that is obtained from nearby vehicles or other vehicles that are on the roadway. The roadway management system can also generate updates to the vehicle state based upon data obtained from sources external to autonomous vehicles.

Abstract: A surveillance system including a number of image capturing devices is described. A processing device may receive an image from one of the image capturing devices and determine a device identifier of the image capturing device. The processing device may further detect a machine-readable target in the image and generate data based on the detection. The processing device may store the data in association with the device identifier in a data structure.

Abstract: The high-intensity specular reflection of light from a surface of an object may create distortions in images of the object, in locations determined based on a perspective of an optical sensor. Multiple images of the object taken from different perspectives may include multiple specular artifacts in different locations, and a composite image of the object that omits such artifacts may be generated based on the multiple images. In particular, pixels of each of the images corresponding to the locations of the specular artifacts and having optimal intensities may be identified. The composite image may be generated based on the pixels having optimal intensities, and by excluding the pixels corresponding to the specular artifacts.

Abstract: Methods and systems for collecting camera calibration data using at least one fixed calibration target are described. Calibration parameters of a camera that is attached to a vehicle may be accessed. A set of calibration instructions for the camera may be determined based at least in part on the calibration parameters. The set of calibration instructions may include navigational instructions for the vehicle to follow to present the camera to the at least one fixed calibration target. Calibration data collected by the camera viewing the at least one fixed calibration target may be received. The camera may be calibrated based at least in part on the calibration data.

Abstract: Imaging devices may be mounted to one or more hands of a worker, e.g., on a glove or other substrate that maybe worn on the hands of the worker, and used to capture images or imaging data from a perspective associated with the hands. The images or imaging data may be stored, analyzed or displayed on a computer display, e.g., a display provided on the glove or substrate. Additionally, the images or imaging data may be evaluated to determine whether a task has been properly performed by the worker, such as by identifying one or more markings (e.g., bar codes) that may be provided within the images or the imaging data.

Abstract: A suitable imaging device for capturing images of an object or an area of interest may be automatically selected based on information regarding the locations and/or other operational properties of a plurality of imaging devices, and a location of the object or the area of interest. One or more geometric models of the fields of view of the imaging devices may be generated, and whether the object or the area of interest falls within one or more of the fields of view may be determined using such models. Where multiple imaging devices may include the object or the area of interest, within a field of view, the imaging device having the most suitable image, e.g., the largest or highest resolution image of the object or the area of interest, may be selected.

Abstract: Imaging data regarding an interior portion of a storage vessel may be obtained from an imaging device (e.g., a digital camera or depth sensor) and interpreted in order to identify an available volume within the storage vessel. Where the imaging data includes an image of an interior of the storage vessel, an area of one or more contiguous spaces of a rear face of the storage vessel may be identified from the image and multiplied by a depth of the storage vessel in order to estimate the available volume. Where the imaging data includes a depth profile of the interior, the depth profile may be processed in order to estimate the available volume. Once an available volume has been estimated, the capacity of the available volume to accommodate one or more additional items may be determined, and information regarding the capacity may be provided to a user.

Abstract: This disclosure describes a device and system for verifying the content of items in a bin within a materials handling facility. In some implementations, a bin content verification apparatus may pass by one or more bins and capture images of those bins. The images may be processed to determine whether the content included in the bins has changed since the last time images of the bins were captured. A determination may also be made as to whether a change to the bin content was expected and, if so, if the determined change corresponds with the expected change.

Abstract: A multi-level (ML) fulfillment center is designed to accommodate landing and takeoff of unmanned aerial vehicles (UAVs), possibly in an urban setting, such as in a densely populated area. Unlike traditional fulfillment centers, the ML fulfillment centers may include many levels (i.e., stories, floors, etc.) as permitted under zoning regulations for respective areas. The fulfillment center may have one or more landing locations and one or more deployment locations to accommodate UAVs, which may delivery at least some of the items from the fulfillment center to locations associated with customers.

Abstract: The high-intensity specular reflection of light from a surface of an object may create distortions in images of the object, in locations determined based on a perspective of an optical sensor. Multiple images of the object taken from different perspectives may include multiple specular artifacts in different locations, and a composite image of the object that omits such artifacts may be generated based on the multiple images. In particular, pixels of each of the images corresponding to the locations of the specular artifacts and having optimal intensities may be identified. The composite image may be generated based on the pixels having optimal intensities, and by excluding the pixels corresponding to the specular artifacts.

Abstract: 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.

Abstract: Information regarding actions or activities to be performed at a workstation may be projected upon a portion of the workstation using one or more projectors. The information may include one or more arrows or other indicators referencing specific tools, materials or objects that may be used to perform one or more of the actions or activities. Such arrows or indicators may be rendered in a manner that simulates a three-dimensional or floating appearance thereof from a perspective of a user that may be adjusted or modified based on changes in the perspective of the user, and with respect to one or more physical or virtual sources of light.

Abstract: Imaging data regarding an interior portion of a storage vessel may be obtained from an imaging device (e.g., a digital camera or depth sensor) and interpreted in order to identify an available volume within the storage vessel. Where the imaging data includes an image of an interior of the storage vessel, an area of one or more contiguous spaces of a rear face of the storage vessel may be identified from the image and multiplied by a depth of the storage vessel in order to estimate the available volume. Where the imaging data includes a depth profile of the interior, the depth profile may be processed in order to estimate the available volume. Once an available volume has been estimated, the capacity of the available volume to accommodate one or more additional items may be determined, and information regarding the capacity may be provided to a user.

Abstract: A multi-level (ML) fulfillment center is designed to accommodate landing and takeoff of unmanned aerial vehicles (UAVs), possibly in an urban setting, such as in a densely populated area. Unlike traditional fulfillment centers, the ML fulfillment centers may include many levels (i.e., stories, floors, etc.) as permitted under zoning regulations for respective areas. The fulfillment center may have one or more landing locations and one or more deployment locations to accommodate UAVs, which may delivery at least some of the items from the fulfillment center to locations associated with customers.