Systems and methods for conveniently providing anchoring assistance onboard a watercraft are provided herein. An example system includes a display and a processor in communication with a marine system. The processor is configured to receive marine data from the marine system and/or one or more user inputs and cause the display to show one or more anchoring locations with visual indications of the anchorage quality index based on at least the marine data and/or user inputs. The one or more anchoring locations may be shown as a heat map overlaid on a map. The system may use real-time marine data, environmental data, weather data, tide data, etc. to dynamically adjust the anchoring locations and anchorage quality index. The system may enable convenient and helpful suggestions and notifications to the user when anchoring a watercraft. Some examples provide automatic deployment of an anchoring system and monitoring of a current anchoring.

The Earth's oceans naturally distribute items aboard barges or other carrying vessels in an efficient manner. Carrying vessels are inserted into gyres, currents, eddies or other sources of flow by support vessels, which may be manned or autonomous. A carrying vessel may be transported from a port or other origin to a point within a naturally occurring flow of seawater by a support vessel, and permitted to travel at speeds and in directions defined by natural factors, from one point to another point, for extended durations. A carrying vessel may be removed from a naturally occurring flow of seawater by a support vessel and transported to a port or other destination. Flow rates, transit times and points within naturally occurring flows at which a carrying vessel may engage with or disengage from a support vessel may be determined according to a machine learning model or in any other manner.

Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Techniques are disclosed for systems and methods to provide perimeter ranging for navigation of mobile structures. A navigation control system includes a logic device, a perimeter ranging system, one or more actuators/controllers, and modules to interface with users, sensors, actuators, and/or other elements of a mobile structure. The logic device is configured to receive perimeter sensor data from ultrasonic perimeter ranging sensor assemblies of the perimeter ranging system and generate an obstruction map based on the received perimeter sensor data. The logic device determines a range to and/or a relative velocity of a navigation hazard based on the received perimeter sensor data. The logic device determines navigation control signals based on the range and/or relative velocity of the navigation hazard. Control signals may be displayed to a user and/or used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.

A marine vessel advisory system may be configured to calculate and provide operational information that show fuel consumption savings based on adjustment of vessel speed and/or heading. In an embodiment, the advisory system may operate real-time to collect operational and/or environmental conditions information to be used to calculate alternative operational performance of the marine vessel that will save fuel and reduce emissions. The calculations may include a simulation, machine learning, and/or artificial intelligence to determine a speed and/or heading of the marine vessel that will reduce fuel consumption. The advisory system may display the computed information for the operator, and the operator may elect to switch to the alternative operating parameters (e.g., slower speed). In an embodiment, the advisory system may interact directly with a marine vessel system and automatically cause the marine vessel system to adjust operating parameters based on computed operating parameters that saves fuel and reduces emissions.

An imaging device that reduces differences between a bird's eye image and an actually measured distance includes imaging cameras mounted on a ship to capture peripheral images of the ship and combines the peripheral images captured by the imaging cameras to create the bird's eye image as a composite image. The imaging device includes an auxiliary camera adjacent to at least one of the imaging cameras, and a distance calculator that calculates a distance in a lateral direction using the auxiliary camera and the at least one of the imaging cameras adjacent to the auxiliary camera.

According to the present invention, a route controller generates a route that makes a ship dock at a berthing facility. The route includes a first location and a second location (P2). The first location is a via point that is offset a prescribed distance in a direction that is orthogonal to the direction in which the berthing facility is oriented from a docking point for the ship at the berthing facility. The second location is the docking point for the ship at the berthing facility. The first location is immediately before the second location in the sequence to be realized by the ship. The route is generated such that the ship remains oriented in the direction in which the berthing facility is oriented as the ship moves from the first location to the second location.

A LiDAR included in this automatic docking device measures the distance to a surrounding object at each predetermined angle by irradiating the object with light and receiving the light reflected by the object. When a ship offshore is instructed to perform automatic docking, the ship navigates to some extent by automatic navigation based on satellite positioning, and is then switched to automatic navigation based on the LiDAR. Before switching to the automatic navigation based on the LiDAR, the LiDAR performs preparatory measurement for measuring the distance to an object around a docking position. In this preparatory measurement, a control unit controls to change, for example, the orientation of the ship such that light emitted from the LiDAR can be reflected by the object around the docking position and can be received by the LiDAR.

A first sensor detects first environment information indicating a shape of a shore arrival location and a positional relationship between the shore arrival location and a boat body. A second sensor, different from the first sensor, detects second environment information indicating the shape of the shore arrival location and the positional relationship between the shore arrival location and the boat body. A controller is communicatively connected to the first sensor and the second sensor. When the distance from the boat body to the shore arrival location is greater than a predetermined distance threshold, the controller generates, based on the first environment information, an instruction signal to control a propulsion device to as to cause the boat body to arrive at the shore arrival location. When the distance from the boat body to the shore arrival location is equal to or less than the distance threshold, the controller generates an instruction signal based on the second environment information.

Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.