A drone communication system and method. The system includes a first communication module; a second communication module; and a drone processor electrically connected to the first communication module and the second communication module respectively; and configured to receive and send a heartbeat packet and communication data through a first communication module and a first communication network, so as to communicate with a first communication port of a server; receive and send communication data through a second communication module and a second communication network, so as to communicate with a second communication port of a server. A receiving condition of the heartbeat packet is used to determine whether to use the communication data received by the first communication network or the second communication network.

Disclosed is a monitoring method wherein: a target is prepared using terrain model data including terrain location information; at a first time, an image of the target is picked up by means of an image pickup unit of a surveying device, and first image data is generated; at a second time after the first time, an image of the target is picked by means of the image pickup unit of the surveying device, and second image data is generated; and displacement of the target is detected using a first image based on the first image data, and a second image based on the second image data.

Embodiments of the present invention provide a method, an apparatus, a terminal, and a storage medium for elevation surrounding flight control. The method includes: obtaining surrounding parameter information of an unmanned aerial vehicle; determining, according to the surrounding parameter information, an elevation surrounding trajectory to be surrounded, where the elevation surrounding trajectory is a plane with the point of interest as a center and the surrounding radius as a radius, and the plane where the elevation surrounding trajectory is located is perpendicular to a horizontal plane; and obtaining a capture viewing angle mode; and controlling, according to the capture viewing angle mode, the unmanned aerial vehicle to fly along the elevation surrounding trajectory.

An autonomy system for use with a vehicle in an environment. The autonomy system comprising a processor operatively coupled with a memory device, a plurality of sensors operatively coupled with the processor; a vehicle controller, a situational awareness module, a task planning module, and a task execution module. The situational awareness module being configured to determine a state of the environment based at least in part on sensor data from at least one of the plurality of sensors. The task planning module being configured to identify, via the processor, a plurality of tasks to be performed by the vehicle and to generate a task assignment list from the plurality of tasks that is based at least in part on predetermined optimization criteria. The task execution module being configured to instruct the vehicle controller to execute the plurality of tasks in accordance with the task assignment list. The task execution module may be configured to monitor the vehicle or the vehicle controller during execution of the task assignment list to identify any errors.

In some embodiments, apparatuses and methods are provided herein useful to allocate unmanned aircraft system (UAS). Some embodiments, provide UAS allocation systems, comprising: a UAS database that stores for each registered UAS an identifier and corresponding operational capabilities; an allocation control circuit configured to: obtain a first set of multiple task parameters specified by a first customer and corresponding to a requested first predefined task that the customer is requesting a UAS be allocated to perform; identify, from the UAS database, a first UAS having operational capabilities to perform the first set of task parameters while implementing the first task; and cause an allocation notification to be communicated to a first UAS provider, of the multiple UAS providers, associated with the first UAS requesting the first UAS provider to allocate the identified first UAS to implement the first task.

Flight based infrared imaging systems and related techniques, and in particular unmanned aerial system (UAS) based systems, are provided for aiding in operation and/or piloting of a mobile structure. Such systems and techniques may include determining environmental conditions around the mobile structure with the UAS detecting the presence of objects and/or persons around the mobile structure and/or determining the presence of other structures around the mobile structure. Instructions for the operation of such mobile structures may then be accordingly determined responsive to such data.

An information processing apparatus according to the present technology includes a control unit. The control unit calculates a relative position of a second moving body with respect to a first moving body based on a captured image of the second moving body captured by the first moving body and position information of the first moving body, and calculates a movable area of the second moving body based on the relative position.

Provided is a self-position estimation mechanism that can absorb the influence of resetting of a position estimator.

A difference between a position for each epoch and a position of a previous epoch estimated by a first estimation unit using a camera image and IMU information and a velocity and an acceleration for each epoch estimated by a second estimation unit using the camera image and the IMU information are acquired. Whether the difference acquired for each epoch is an outlier is tested on the basis of a threshold and whether the velocity and acceleration acquired for each epoch is an outlier is tested on the basis of a threshold. A self-position is estimated using the difference for each epoch and the velocity and acceleration for each epoch after the outlier is removed.

Systems and methods here may include computing system configured to coordinate more than one remotely operated vehicle using level of automation determination and assignments. In some examples, the method for coordinating a plurality of drones includes using a computer with a processor and a memory in communication with the plurality of drones, and a candidate problem resolver for retrieving a candidate resolution from a data storage, and sending the retrieved candidate resolution to a candidate resolution states predictor.

The learning system SY1 acquires control information output from a control model M by inputting to the control model M environmental information including weather information in at least one of a surrounding environment of an unmanned aerial vehicle P and an environment of a planned flight area of an unmanned aerial vehicle P, and when the unmanned aerial vehicle P takes an action based on the control information, performs reinforcement learning of the control model M using a reward r representing an evaluation of a result of the action.