A method includes establishing a first wireless connection between a movable object and a first remote control device, and establishing a second wireless connection between the movable object and at least one second remote control device. The method also includes selecting, based on a determination that the first wireless connection is normal, a first control signal received from the first remote control device to control the movable object. The method further includes selecting, based on a determination that the first wireless connection is abnormal and that the second wireless connection is normal, a second control signal received from the at least one second remote control device to control the movable object.

To ensure stability of flying by an unmanned aerial vehicle, first acquisition means of an unmanned aerial vehicle control system acquires first information, which is at least one piece of information for operating an unmanned aerial vehicle that is flying or information on a result of detecting an operation of the unmanned aerial vehicle. Second acquisition means acquires second information for operating the unmanned aerial vehicle after switching of control of the unmanned aerial vehicle. Flight control means restricts, in accordance with the first information and the second information, switching to control of the unmanned aerial vehicle based on the second information.

A method, apparatus and system are provided for operating one or more drones in a building. In the context of a method, information is determined that includes at least one of real time information or predictive information. The real time information is indicative of a position of at least one individual in the building, while the predictive information is indicative of a predicted location of the at least one individual in the building at a certain time. The method also includes controlling the one or more drones in the building according to the at least one of the real time information or the predictive information to avoid the at least one individual while the drone is performing a task.

Disclosed are embodiments for employing off board sensors to augment data used by a ground based autonomous vehicle. In some aspects, the off-board sensors may be positioned on another autonomous vehicle, such as an aerial autonomous vehicle (AAV). The disclosed embodiments determine uncertainty scores associated with ground regions. The uncertainty scores indicate a need to reimage the ground regions. An AAV may be tasked to reimage a region having a relatively high uncertainty score, depending on a cost associated with the tasking.

A method of performing deconfliction comprises receiving a request to accept a first operational intent associated with a first unmanned aircraft system, determining whether a conflict exists between the first operational intent and one or more scheduled operational intents, and if a conflict exists between the first operational intent and a second operational intent associated with a second unmanned aircraft system, transmitting data associated with the conflict to a first operator of the first unmanned aircraft system and a second operator of the second unmanned aircraft system, and transmitting information to the first and second operator allowing them to negotiate a resolution of the conflict. If a conflict does not exist, the first operational intent may be accepted. Bids may also be received for a right to utilize a volume of airspace at a particular time and the right may be granted to a highest bidder.

Disclosed herein are embodiments for providing a trusted autonomy framework for unmanned aerial systems. One embodiment of a method includes receiving a request from an entity to participate in secure data sharing within the trusted autonomy framework for unmanned aerial systems, receiving a type of data that will be shared via the entity, and verifying an identity of the entity, a security infrastructure of the entity, and validating the data to be shared. In some embodiments, in response to verifying, accepting the entity into the trusted autonomy framework for unmanned aerial systems.

The law enforcement standoff inspection drone capability (L-SID) integrates Various technology to enable a capability implemented at the squad car level to allow the first-to-scene the ability to remotely pre-screen the scene for threat, before an on-foot approach. This is accomplished with an officer launched and controlled and specially configure small unmanned aircraft system (UAS). The LAS is integrated with a specially configured one-hand drone controller, a wearable see through heads-up-display glasses, microphone that's linked to the UAS's onboard loudspeaker, and a special processing that enables looking through a vehicle of building tinted windows during enforcement event. The system operates on a private ad-hoc network, implements IEEE 802.1 1 g/n WPA 3 standards, and provides continuous live steamed scene data throughout the enforcement event. All data and video collected is transmitted in real-time to headquarters.

A system and method for autonomously controlling a set of unmanned aerial vehicles is provided. The autonomous ground control system may include a communications module and a fleet configuration module in communication with one or more user interface applications. The autonomous ground control system may receive one or more flight commands and generate fleet configuration instructions and safety information. The autonomous ground control system may provide the fleet configuration instructions to each unmanned aerial vehicle in the set in order to carry out the fleet configuration instructions in real time.

Systems and methods for controlling aerial vehicles are provided. An aerial vehicle includes a single circuit board with a number of processor devices and a memory including instructions to perform autonomy operations. The autonomy operations include obtaining GNSS data from GNSS assemblies electrically connected to the processor devices, APNT data from APNT assemblies electrically connected to the processor devices, and radar data from the radar assemblies electrically connected to the processor devices. Each of the assemblies are disposed on the same circuit board that includes the number of processor devices. The processor devices determine a vehicle location based on the GNSS data, the APNT data, and the radar data, identify airborne objects based on the radar data, generate a motion plan based on the vehicle location and the identified objects, and initiate a motion of the aerial vehicle based on the vehicle location.