A method for accessing and navigating an aerial vehicle on a three-dimensional map of transportation skyways comprises requesting, from an aerial-vehicle-transportation database, a driving map based on a three-dimensional representation of one or more transportation skyways, the three-dimensional representation comprising information regarding an x-axis and a y-axis representing movement in a first plane and information regarding a z-axis representing movement in a second plane perpendicular to the first plane; receiving the driving map generated based on the information on the x-axis and the y-axis of movement, the driving map being determined based on the three-dimensional representation; receiving information on altitude-transition zones associated with the z-axis of movement of the aerial vehicle along the second plane; and storing, in a navigation system of the aerial vehicle, navigation information comprising the driving map and the altitude-transition zones.

A robotic control system may be configured to translate a first set of location coordinates based on a first location numbering system to a second set of location coordinates based on a unified location numbering system. A robotic control system may receive layout data of a warehouse, the layout data containing a plurality of location coordinates of racks and storage locations. The location coordinates may be of a first format based on the first location numbering system that is specific to the warehouse. The robotic control system may analyze the format of the location coordinates to select, from a plurality of candidate conversion algorithms, a suitable conversion algorithm to translate the plurality of location coordinates of the first format to a second format based on the unified numbering system. The robotic control system may store the translated location coordinates for use in generating a topometric map of the warehouse.

An automated system of airborne drones may be equipped with components for navigating within a warehouse environment, locating specific bins of product as assigned, retrieving a single item from the bin, and delivering the retrieved item to a central shipping area or disposing in a designated location including conveyor belts or mobile robotic platforms.

An aerial vehicle configured for operation within indoor spaces has a meshed construction with a housing defined by upper and lower sections having meshes provided above and below propellers and motors of the aerial vehicle. The aerial vehicle also includes a suite of sensors such as LIDAR sensors, time-of-flight sensors, cameras, ultrasonic sensors, or others. Meshes of the upper and lower sections include central openings along with spokes and concentric rings provided about the central openings. Meshes of the lower section have substantially larger central openings than meshes of the upper section, but feature more dense spokes or concentric rings beneath tips of the rotating propellers, which may be hinged or foldable in nature. Data captured by sensors of the aerial vehicle may be utilized for any purpose, such as to generate environment maps of an indoor space, or to monitor the indoor space for adverse conditions or events.

An aerial vehicle is configured to calculate ranges to objects around the aerial vehicle when operating within indoor spaces, using a LIDAR sensor or another range sensor. The aerial vehicle calculates ranges within a plurality of sectors around the aerial vehicle and identifies a minimum distance measurement for each of the sectors. Sets of adjacent sectors having distance measurements above a threshold are identified, and bearings and minimum distance measurements of the sets of adjacent sectors are determined. When the aerial vehicle detects an object within a flight path, the aerial vehicle selects one of the sets of adjacent sectors based on the minimum distance measurements and executes a braking maneuver in a direction of the selected one of the sets of adjacent sectors.

Embodiments relate to a drone and a base station. The drone is adapted to navigate a storage site and carry a swappable battery. The base station is adapted to receive the drone to perform a battery swap. As such, the base station may include charging ports to recharge the swappable battery and may be adapted to predict a return timing of the drone, charge a battery to a level in anticipation of the return timing, and subsequent to the drone returning to the base station, swapping the battery of the drone with the charged battery.

A rotorcraft including a fuselage, one or more motor-driven rotors for vertical flight, and a control system. The motors drive the one or more rotors in either of two directions of rotation to provide for flight in either an upright or an inverted orientation. An orientation sensor is used to control the primary direction of thrust, and operational instructions and gathered information are automatically adapted based on the orientation of the fuselage with respect to gravity. The rotors are configured with blades that invert to conform to the direction of rotation.

An unmanned aerial vehicle (UAV) assessment and reporting system may utilize one or more scanning techniques to provide useful assessments and/or reports for structures and other objects. The scanning techniques may be performed in sequence and optionally used to further fine tune each subsequent scan. The system may include shadow elimination, annotation, and/or reduction for the UAV itself and/or other objects. A UAV may be used to determine a pitch of roof of a structure. The pitch of the roof may be used to fine tune subsequent scanning and data capture.

The subject matter described herein includes a modular and extensible approach to integrate noisy measurements from multiple heterogeneous sensors that yield either absolute or relative observations at different and varying time intervals, and to provide smooth and globally consistent estimates of position in real time for autonomous flight. We describe the development of the algorithms and software architecture for a new 1.9 kg MAV platform equipped with an IMU, laser scanner, stereo cameras, pressure altimeter, magnetometer, and a GPS receiver, in which the state estimation and control are performed onboard on an Intel NUC 3.sup.rd generation i3 processor. We illustrate the robustness of our framework in large-scale, indoor-outdoor autonomous aerial navigation experiments involving traversals of over 440 meters at average speeds of 1.5 m/s with winds around 10 mph while entering and exiting buildings.

In one general aspect, a drone control device includes a body, a motor that is configured to move the body, a network module, an input module, and a processor. The network module is configured to communicate with a security system that monitors a property and receive data associated with a location within the property. The input module is configured to receive user input. The processor is configured to perform operations that include: determine, from among the location within the property and other locations within the property, a target location within the property; move the body to the target location within the property by providing a signal to the motor; receive, from the input module, input data that is associated with an operation of the security system; and in response to receiving the input data, perform the operation of the security system.