Embodiments described herein include a visual guidance system for a boat trailer that can include a housing defining a cavity, the housing having a plurality of openings such that fluid can pass into the cavity, a first track and a second track, where the first track and the second track have a substantially vertical orientation, and a laser head mounted on the first track and the second track that can include a housing, a laser module positioned within the housing that is configured to provide a first illumination beam, and a float.

Flight based infrared imaging systems and related techniques, and in particular unmanned aerial system (UAS) based systems, are provided for aiding in operation and/or piloting of a mobile structure. Such systems and techniques may include determining environmental conditions around the mobile structure with the UAS detecting the presence of objects and/or persons around the mobile structure and/or determining the presence of other structures around the mobile structure. Instructions for the operation of such mobile structures may then be accordingly determined responsive to such data.

Flight based infrared imaging systems and related techniques, and in particular unmanned aerial system (UAS) based systems, are provided for aiding in operation and/or piloting of a mobile structure. Such systems and techniques may include determining environmental conditions around the mobile structure with the UAS detecting the presence of objects and/or persons around the mobile structure and/or determining the presence of other structures around the mobile structure. Instructions for the operation of such mobile structures may then be accordingly determined responsive to such data.

The present application discloses is a scheduling method and system for fully autonomous waterborne inter terminal transportation, and belongs to the field of transportation. The method includes: establishing a dynamic scheduling model for waterborne Autonomous guided vessels (wAGVs); quickly inserting the dynamically arriving transportation tasks into all the existing wAGV paths, calculating the insertion cost and selecting the path and position with the lowest insertion cost to obtain updated initial paths; improving the initial path using a heuristic algorithm based on tabu search to obtain quasi-optimal wAGV paths; executing the scheduling wAGV paths. The scheduling model of the present application is based on the combinatorial optimization, and considers performance indexes such as transportation distance and customer satisfaction, and constraints such as time window and loading capacity. An initial path by using an insertion method within the framework of rolling horizon is constructed. The initial path is then optimized and improved by designing a tabu search heuristic so that obtaining quasi-optimal multi-wAGV paths are obtained in real time. This system is helpful to improve the autonomy level of large ports.

The present application discloses is a scheduling method and system for fully autonomous waterborne inter terminal transportation, and belongs to the field of transportation. The method includes: establishing a dynamic scheduling model for waterborne Autonomous guided vessels (wAGVs); quickly inserting the dynamically arriving transportation tasks into all the existing wAGV paths, calculating the insertion cost and selecting the path and position with the lowest insertion cost to obtain updated initial paths; improving the initial path using a heuristic algorithm based on tabu search to obtain quasi-optimal wAGV paths; executing the scheduling wAGV paths. The scheduling model of the present application is based on the combinatorial optimization, and considers performance indexes such as transportation distance and customer satisfaction, and constraints such as time window and loading capacity. An initial path by using an insertion method within the framework of rolling horizon is constructed. The initial path is then optimized and improved by designing a tabu search heuristic so that obtaining quasi-optimal multi-wAGV paths are obtained in real time. This system is helpful to improve the autonomy level of large ports.

A vessel location validation method and apparatus are provided. The vessel location validation method includes receiving a wireless signal from a vessel, acquiring location information of the vessel from the received wireless signal, and determining whether the acquired location information is valid based on the acquired location information and a signal strength of the received wireless signal.

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.

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

Systems and methods for sea state prediction and autonomous navigation in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes a method of predicting a future sea state including generating a sequence of at least two 3D images of a sea surface using at least two image sensors, detecting peaks and troughs in the 3D images using a processor, identifying at least one wavefront in each 3D image based upon the detected peaks and troughs using the processor, characterizing at least one propagating wave based upon the propagation of wavefronts detected in the sequence of 3D images using the processor, and predicting a future sea state using at least one propagating wave characterizing the propagation of wavefronts in the sequence of 3D images using the processor. Another embodiment includes a method of autonomous vessel navigation based upon a predicted sea state and target location.

A hierarchical data acquisition system and method applied to a marine information network are provided. Multiple sensor nodes are arranged in clusters, each of the clusters includes a cluster head node and multiple ordinary nodes. The multiple ordinary nodes acquire data information of a seafloor and transmit the acquired data information to the cluster head node, and the cluster head node aggregate the data and transmits the aggregated data to an autonomous underwater vehicle, reducing energy consumption of each of the sensor nodes, prolonging service lives of sensors of a data acquisition layer, and improving data acquisition efficiency of a data acquisition layer. In addition, after each of data acquisition periods, a sensor node in each of the clusters is selected as a cluster head node in a next data acquisition cycle. Cluster head nodes are continuously updated in cycles.