Some embodiments of the disclosure provide a cooperative positioning system and method for an unmanned underwater vehicle (UUV) cluster based on ranging and information interaction. In some examples, the cooperative positioning system includes one master UUV, multiple slave UUVs, and a cooperative positioning apparatus. In some examples, the cooperative positioning method includes following steps. A master UUV and a slave UUV perform internal clock disciplining. The master UUV periodically packages a location of the master UUV and a covariance matrix of location estimation into a cooperative positioning data packet and broadcasts the cooperative positioning data packet to slave UUVs of a cluster. The slave UUV receives the cooperative positioning data packet. A cooperative positioning apparatus of the salve UUV constructs a state and a measurement equation according to data. The cooperative positioning apparatus calculates and obtains a cooperative positioning result by using an extended Kalman filter (EKF).

Some embodiments of the disclosure provide a cooperative positioning system and method for an unmanned underwater vehicle (UUV) cluster based on ranging and information interaction. In some examples, the cooperative positioning system includes one master UUV, multiple slave UUVs, and a cooperative positioning apparatus. In some examples, the cooperative positioning method includes following steps. A master UUV and a slave UUV perform internal clock disciplining. The master UUV periodically packages a location of the master UUV and a covariance matrix of location estimation into a cooperative positioning data packet and broadcasts the cooperative positioning data packet to slave UUVs of a cluster. The slave UUV receives the cooperative positioning data packet. A cooperative positioning apparatus of the salve UUV constructs a state and a measurement equation according to data. The cooperative positioning apparatus calculates and obtains a cooperative positioning result by using an extended Kalman filter (EKF).

Generally, the present disclosure relates to a forward deployed sensor system or, in a specific embodiment, a forward deployed radar (FDR) system. The forward deployed sensor system includes a radar system and may also include other types of sensors such as optical sensors, acoustic sensors including sonar, and electromagnetic sensors. Further, the forward deployed sensor system may also include a communication system such as a full spectrum receiver/transmitter, a ship to ship relay transponder, a satellite communication system, and global positioning system (GPS) capability. The forward deployed sensor system is able to detect objects in the air, on the sea, and underwater, and communicate such detection to a ship, submarine, aircraft, satellite, or other remote location. Such systems may be used to augment the protection of shipping lanes by military or security forces to allow for peaceful commerce and utility of the sea by all nations.

The present invention is a supercavitation underwater drone equipped with high-speed sensors configured for data acquisition and surveillance. A propulsion system generates gasses that are then used to generate a vapor bubble at the nose of the vehicle. An array of nozzles aids in directional control of the vehicle by altering the pressure profile of the vapor bubble. In some embodiments the vehicle operates autonomously. In other embodiments the vehicle operates semi-autonomously following a preplanned route and communicating with a base. In other embodiments the vehicle is controlled remotely by an operator.

The present invention is a supercavitation underwater drone equipped with high-speed sensors configured for data acquisition and surveillance. A propulsion system generates gasses that are then used to generate a vapor bubble at the nose of the vehicle. An array of nozzles aids in directional control of the vehicle by altering the pressure profile of the vapor bubble. In some embodiments the vehicle operates autonomously. In other embodiments the vehicle operates semi-autonomously following a preplanned route and communicating with a base. In other embodiments the vehicle is controlled remotely by an operator.

An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a computing device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

In some embodiments, provided is a robot for water tunnel inspection, comprising: (a) a shell, comprising an upper shell and a lower shell; wherein the upper shell and the lower shell are sized and shaped to match each other, together defining a closed cavity therewithin; (b) a camera system, configured to capture an image or video of a field of view of surrounding; (c) a lighting system, configured to provide illumination at least partially for the field of view; (d) a propulsion system, configured to provide propulsion force to the robot in water; and (e) a controlling system, configured to provide power and control operation of the robot, wherein the robot is configured to float on water and to have a center of gravity positioned lower than geometric center. Other example embodiments are described herein. In certain embodiments, the robots provide safe and efficient tunnel inspections without human operation.

In some embodiments, provided is a robot for water tunnel inspection, comprising: (a) a shell, comprising an upper shell and a lower shell; wherein the upper shell and the lower shell are sized and shaped to match each other, together defining a closed cavity therewithin; (b) a camera system, configured to capture an image or video of a field of view of surrounding; (c) a lighting system, configured to provide illumination at least partially for the field of view; (d) a propulsion system, configured to provide propulsion force to the robot in water; and (e) a controlling system, configured to provide power and control operation of the robot, wherein the robot is configured to float on water and to have a center of gravity positioned lower than geometric center. Other example embodiments are described herein. In certain embodiments, the robots provide safe and efficient tunnel inspections without human operation.

A method and device for docking control of an underwater vehicle based on sonar imaging, belonging to the field of vehicle automatic control technology. Decomposes docking control of the underwater vehicle into depth tracking control and horizontal plane docking control, designs corresponding reinforcement learning cost functions to train deep network-based depth tracking controller and servo docking controller, designs nonlinear weight to balance position error and field of view in the cost function, and uses reinforcement learning to make the controller learn optimized control strategy, quickly eliminate position error and maintain recovery device features in the imaging sonar field of view. The device may effectively avoid docking failure caused by loss of target features and improve docking success rate.

A method and device for docking control of an underwater vehicle based on sonar imaging, belonging to the field of vehicle automatic control technology. Decomposes docking control of the underwater vehicle into depth tracking control and horizontal plane docking control, designs corresponding reinforcement learning cost functions to train deep network-based depth tracking controller and servo docking controller, designs nonlinear weight to balance position error and field of view in the cost function, and uses reinforcement learning to make the controller learn optimized control strategy, quickly eliminate position error and maintain recovery device features in the imaging sonar field of view. The device may effectively avoid docking failure caused by loss of target features and improve docking success rate.