Abstract: A reliability map is generated by superimposing or associating populations or other intrinsic data is superimposed or associated with a geographic map of a region, which is divided into a grid having cells of uniform size. Densities of towns, cities or other geospatial areas are determined and assigned to cells of the grid, which have sizes corresponding to minimum dimensions of a corridor required for travel by an aerial vehicle. When a mission requiring travel from an origin to a destination within the region is identified, one or more paths of a safe route between the origin and the destination are selected based on the reliability map. The safe route is selected to avoid areas of high population density or locations of critical infrastructure such as schools, hospitals or public safety buildings.

Abstract: An occupancy map of a region is generated for a vehicle by dividing the region into cells and calculating occupancy metrics for each of the cells based on building footprints or other data. Cells having high occupancy metrics are labeled as obstructed, and cells having low occupancy metrics are labeled as free of obstructions. Cells that may not be labeled as obstructed or free based on their occupancy metrics are subdivided, and occupancy metrics are calculated for cells formed from the subdivision. Occupancy metrics may be calculated for cells, and the cells may be subdivided, for as long as the cells formed from the subdivision have areas greater than an area defined based on operational capabilities of the vehicle. The occupancy map is used to determine routes through the region for the vehicle.

Abstract: A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.

Abstract: Disclosed are systems and methods to determine preferred delivery points within a parcel that are available to receive an aerial delivery of an item. For each delivery point a plurality of criteria scores may be determined for various criteria based on a processing of parcel data, image data, and/or sensor data corresponding to the parcel. The criteria may be aerial navigation related criteria or user preference criteria. The criteria scores may then be used to determine a suitability score for each delivery point. In some implementations, a user may specify a preferred delivery point and/or indicate one user criteria as more important than another criteria.

Abstract: Disclosed are systems and methods to determine and rank large areas encompassing many parcels (e.g., neighborhoods, cities, towns) for aerial item delivery availability, without the use of image data of the areas. In some implementations, publicly available two-dimensional parcel maps that indicate parcel boundaries and outlines of structures on those parcels may be obtained and processed. For example, parcels within the area may be processed to determine deliverable area shapes, such as rectangles, within the parcel, excluding the area of the structure. A determination is then made as to whether one or more of the deliverable area shapes exceed a deliverable area threshold. If one or more of the deliverable area shapes of the parcel exceed the threshold, the parcel is considered to be available for aerial item delivery. This processing may be done for all parcels within an area or all customer parcels of customers of a service within the area (or any other selection criteria).

Abstract: Disclosed are systems and methods that generate individual semantic layers for different object types within an environment. For each object type semantic layer and for an aerial vehicle, a semantic configuration space may be formed that indicates all valid aerial vehicle configurations in which the aerial vehicle does not collide with objects of the object type that exists in the environment. Finally, a combined configuration space may be formed by overlaying multiple semantic configuration spaces for object types known to be within the environment. The resulting combined configuration space indicates a common area in which the aerial vehicle can navigate according to the configuration of the common area and not collide with any objects of the object types represented by the combined semantic configuration spaces.

Abstract: Techniques are described for assessing the health of an unmanned vehicle such as an unmanned aerial vehicle (UAV). In some embodiments, sensors corresponding to subsystems of the UAV may be utilized to assess the health of a particular subsystem. Predictive models may be stored within memory of the UAV to enable such assessments to be performed at the UAV (e.g., during performance of a mission). Sensor data collected from sensors on the UAV may be provided as input for a predictive model associated with a particular subsystem. The predictive model may output a failure prediction indicating a likelihood, and in some cases, a time by which failure of the subsystem is predicted to occur given the sensor data. In some embodiments, one or more corrective actions may be identified and triggered based, at least in part, on the failure prediction.