The invention relates, generally, to systems and methods for optimizing the performance of an Unmanned Aerial Vehicle (UAV) by optimizing the UAV's drivetrain, extending the UAV's battery life; by monitoring and reporting on payload imbalance or overweight conditions; and by improving the aerodynamics and streamlining of certain drag-producing elements.

Dead reckoning correction utilizing patterned light projection is provided herein. An example method can include navigating a drone along a pattern using dead reckoning, the pattern having a plurality of lines, detecting one of the plurality of lines using an optical sensor of the drone, determining when a line of travel of the drone is not aligned with the one of the plurality of lines, and realigning the line of travel of the drone so as to be aligned with the one of the plurality of lines to compensate for drift that occurs during navigation using dead reckoning.

Drones with propulsions systems supported in a housing are provided where the orientation of the housing is independent from the orientation of the propulsion system. Drones are provided where a propulsion system is rotatable about a first axis and a second axis that is perpendicular to the first axis, permitting the propulsion system to assume substantially any position with a sphere. Drones are provided where a bladeless inner tube is rotatable about a first axis and a second axis that is perpendicular to the first axis, permitting the inner tube to assume substantially any position within a sphere. Drone systems are provided with connectable unit drones. An unmanned land vehicle is provided having a wheel assembly that is rotatable about a first axis and a second axis that is perpendicular to the first axis, permitting the wheel assembly to assume substantially any position with a sphere.

The present invention discloses a heading generation method of an unmanned aerial vehicle including the following steps of: making a preliminary flight for selecting a point of view to record flight waypoints, the waypoints including positioning data and flight altitude information of the unmanned aerial vehicle; receiving and recording flight waypoints of the unmanned aerial vehicle; generating a flight trajectory according to waypoints of the preliminary flight; editing the flight trajectory to obtain a new flight trajectory; and transmitting the edited new flight trajectory to the unmanned aerial vehicle to cause the unmanned aerial vehicle to fly according to the new flight trajectory. The present invention further relates to a heading generation system of an unmanned aerial vehicle.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting the transmission power of an unmanned aerial vehicle are disclosed. In one aspect, a method includes the actions of determining, by an unmanned aerial vehicle that includes a radio transceiver, an altitude of the unmanned aerial vehicle and a distance between the unmanned aerial vehicle and a base station. The actions further include, based on the altitude of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the base station, determining, by an unmanned aerial vehicle, a transmission power level for the radio transceiver. The actions further include communicating, by the unmanned aerial vehicle, with the base station using the radio transceiver operating at the transmission power level.

A landing tracking control method comprises the following contents: a tracking model training stage and an unmanned aerial vehicle real-time tracking stage. The landing tracking control method extracts a network Snet by using a lightweight feature and makes modification, so that an extraction speed of the feature is increased to better meet a real-time requirement. Weight allocation on the importance of channel information is carried out to differentiate effective features more purposefully and utilize the features, so that the tracking precision is improved. In order to improve a training effect of the network, a loss function of an RPN network is optimized, a regression precision of a target frame is measured by using CIOU, and meanwhile, calculation of classified loss function is adjusted according to CIOU, and a relation between a regression network and classification network is enhanced.

An autonomous marking apparatus comprising a propulsion system, a location sensor, a payload assembly, one or more marking sensors, a transceiver, a data store, and a processor. The location sensor is arranged to determine the location of the apparatus. The payload assembly is arranged to carry a payload of marking material. The one or more marking sensors are arranged to scan an area in proximity to the apparatus. The transceiver is arranged to exchange data with a remote server via a data network. The data store is arranged to store a portion of the data. The processor is arranged to receive data from the location sensor, the one or more marking sensors, and from the transceiver. The processor is also arranged to send data to the transceiver and control the delivery of the payload at the location of the apparatus.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

The detection and identification of unmanned aerial vehicles (UAVs) from a radio wave measurement result based on artificial intelligence (AI) are provided. A method of operating an apparatus to detect unmanned aerial vehicles (UAVs) includes generating a spectrogram, determining a first region to find a direction of the UAVs in the spectrogram, determining a direction of a first UAV of the UAVs based on signal values in the first region, determining a second region to identify a type of the first UAV in the spectrogram, and identifying the type of the first UAV based on signal values in the second region.

A remotely controlled multirotor aircraft having a frame that includes a first and a second peripheral portions, to which at least one first and one second motor can be respectively coupled, and a central portion including a first end and a second end, to which the first peripheral portion and the second peripheral portion are respectively coupled, so that the first peripheral portion develops in a plane that is different from that in which the second peripheral portion develops; furthermore, the central portion also includes a coupling mechanism allowing the coupling between the central portion and a mobile device having video acquisition ability.