Provided is a detection and classification system and method for small unmanned aircraft systems (sUAS). The system and method detect and classify multiple simultaneous heterogeneous RC transmitters/sUAS downlinks from the RF signature using Object Detection Deep Convolutional Neural Networks (DCNNs). The method further utilizes not only passive RF, but may also utilize Electro Optic/Infrared (EO/IR), radar and acoustic sensors as well, with a fusion of the individual sensor classifications. Detection and classification with Identification Friend or Foe (IFF) of individual sUAS in a swarm, multi-modal approach for high confidence classification, decision, and implementation on a low C-SWaP (cost, size, weight and power) NVIDIA Jetson TX2 embedded AI platform is achieved.

A controller for an unmanned aerial vehicle (UAV) comprising an image capture means, the controller comprising: inputs arranged to receive: positional data relating to the UAV, a vehicle and a user device; image data captured by the image capture means; a processor arranged to process the received positional data to determine the relative locations of the UAV, vehicle and user device; an output arranged to output a control signal for controlling the UAV and to output an image signal comprising captured image data; wherein the processor is arranged to: generate the control signal for the UAV such that the image data captured by the image capture means comprises at least an image of an obscured portion of the vehicle that is obscured from a field of view of a user of the user device.

A method for controlling a movable object is provided. A user input that includes a first parameter corresponding to a first coordinate system is received and an operation mode is determined. In response to determining the operation mode being a first operation mode, a second parameter corresponding to a second coordinate system is generated and the movable object is controlled to move based on the second parameter. In response to determining the operation mode being a second operation mode, the first parameter is translated to a third parameter corresponding to the second coordinate system and the movable object is controlled to move based on the third parameter.

An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

Many different types of systems are utilized and tasks are performed in a marine environment. The present invention provides various configurations of castable devices that can be operated and/or controlled for such systems or tasks. One or more castable devices can be integrated with a transducer assembly, such as a phased array, that emits sonar beams and receives sonar returns from the underwater environment. Processing circuitry may receive the sonar returns, process the sonar returns, generate an image, and transmit the image to a display.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

A teleoperations system may be used to selectively override conditions detected by an autonomous vehicle to enable the autonomous vehicle to effectively ignore detected conditions that are identified as false positives by the teleoperations system. Furthermore, a teleoperations system may be used to generate commands that an autonomous vehicle validates prior to executing to confirm that the commands do not violate any vehicle constraints for the autonomous vehicle. Still further, an autonomous vehicle may be capable of dynamically varying the video quality of one or more camera feeds that are streamed to a teleoperations system over a bandwidth-constrained wireless network based upon a current context of the autonomous vehicle.

A multi-tank/vessel buoyancy system for use in deploying and connecting tendons or other elongate members between subsea anchors and floating/semi-submersible platforms. The vessels are interconnected at axially spaced locations toward their upper ends and lower ends, there being an equalizing system proximate the top ends of the vessels to permit ingress and egress of air into the vessels and a lower water equalizing system to permit free-flowing ingress and egress of water into the vessels. There is at least one clamping system operatively connected to the multi-vessel system, the clamping system, like the valving systems, being remotely, acoustically operable from a PLC on a work barge or the like.

Aspects of the disclosure provide for a method for identifying speed limit signs and controlling an autonomous vehicle in response to detected speed limit signs. The autonomous vehicle's computing devices identifies a speed limit sign in a vehicle's environment and a location and orientation corresponding to the speed limit sign. Then, the and orientation location of the speed limit sign is determined to not correspond to a pre-stored location and a pre-stored orientation of a speed limit sign that is pre-stored in map information. An effect zone of the speed limit sign is determined based on the location and orientation of the speed limit sign and characteristics of surrounding areas or other detected object before or after the speed limit sign. The autonomous vehicle's computing devices determines a response of the vehicle based on the determined effect zone, and controls the autonomous vehicle based on the determined response.

A method is provided. An unmanned aerial vehicle (UAV) is operated. A position of the UAV is determined while in flight, and a nonce is generated. A Merkel root is generated based at least in part on a timestamp and the position of the UAV. A current block is calculated based at least in part on a previous block, the Merkel root, and the nonce, and the current block, the timestamp, the nonce, the prior block, and the position of the UAV are transmitted.