A method of carrying a UAV combined with a package on an assist vehicle (AV) in assisted travel mode over one route section, flying the UAV combined with a package over another route section, and carrying the UAV combined with the package on second AV in assisted travel mode over a third route section, the route sections being contiguous. Also, a method of flying a UAV combined with a package over one route section, carrying the UAV combined with the package on an AV in assisted travel mode over a second route section, and flying the UAV combined with the package over a third route section, the route sections being contiguous.

An optimized coverage-path planning method for a single unmanned surface mapping vessel (USMV) is implemented with a system including a computer processor executing a computer program loaded in a storage device and implanting the method. The method includes rasterizing and initializing an environmental map, and an unmanned vessel outputting position data and obstacle data according to the environmental map so that path planning is started to provide a target point to the unmanned vessel. In case of tripping in a local optimum at a current-level map for the target point, the map level is updated in an ascending order until the highest level, in order to identify a map level in which the target point is found.

The present invention discloses an intention-driven reinforcement learning-based path planning method, including the following steps: 1: acquiring, by a data collector, a state of a monitoring network; 2: selecting a steering angle of the data collector according to positions of surrounding obstacles, sensor nodes, and the data collector; 3: selecting a speed of the data collector, a target node, and a next target node as an action of the data collector according to an ? greedy policy; 4: determining, by the data collector, the next time slot according to the selected steering angle and speed; 5: obtaining rewards and penalties according to intentions of the data collector and the sensor nodes, and updating a Q value; 6: repeating step 1 to step 5 until a termination state or a convergence condition is satisfied; and 7: selecting, by the data collector, an action in each time slot having the maximum Q value as a planning result, and generating an optimal path. The method provided in the present invention can complete the data collection path planning with a higher probability of success and performance closer to the intention.

A suspended self-balancing self-cruising online water quality monitoring device, an online water quality monitoring method, and an online water quality assessment method are provided. The device includes a suspension cabin main body, a communication and control system, and a suspended carrying platform configured for self-balancing attitude adjustment. Suspension feet configured to drive the suspension cabin main body to implement self-balancing attitude adjustment, cruising, or fixed-point suspension are arranged outside the suspension cabin main body. The communication and control system is configured to plan a W-shaped water-region cruising path and sampling points thereon according to a topography of a water region, control the water sample testing device to test quality of water along the sampling points online, assess water quality of the water region according to an online testing result, and output the water quality of the water region. The device achieves precise positioning and self-cruise monitoring are achieved.

The present invention discloses an intention-driven reinforcement learning-based path planning method, including the following steps: 1: acquiring, by a data collector, a state of a monitoring network; 2: selecting a steering angle of the data collector according to positions of surrounding obstacles, sensor nodes, and the data collector; 3: selecting a speed of the data collector, a target node, and a next target node as an action of the data collector according to an greedy policy; 4: determining, by the data collector, the next time slot according to the selected steering angle and speed; 5: obtaining rewards and penalties according to intentions of the data collector and the sensor nodes, and updating a Q value; 6: repeating step 1 to step 5 until a termination state or a convergence condition is satisfied; and 7: selecting, by the data collector, an action in each time slot having the maximum Q value as a planning result, and generating an optimal path. The method provided in the present invention can complete the data collection path planning with a higher probability of success and performance closer to the intention.

Navigation system (300) for land, air, marine or submarine vehicle (302), comprising a remote control workstation (301) with Manual control mode (310), Mission Planning mode (330) and tactical control mode (360) initiating command-and-control options; a navigation module (100) retrofittably disposed on the vehicle (302); a plurality of perception sensors (318) disposed on the vehicle (302); the system (300) receives manual, electrical, radio and audio commands of human operator (305) in the manual control (310) and mission planning mode (330) and converts them to dataset for training a navigation model having a navigational algorithm. The perception sensors (318) generate dataset for self-learning in real time in manual control mode (310), mission control mode (330) and tactical control mode (360); the navigational system (300) autonomously navigates with presence of communication network (390) and in absence of communication network (390).

Techniques are disclosed for systems and methods for water non-water segmentation of navigational imagery to assist in the autonomous navigation of mobile structures. An imagery based navigation system includes a logic device configured to communicate with an imaging module coupled to a mobile structure and/or configured to capture images of an environment about the mobile structure. The logic device may be configured to receive at least one image from the imaging module; determine a water/non-water segmented image based, at least in part, on the received at least one image, and generate a range chart corresponding to the environment about the mobile structure based, at least in part, on the determined water/non-water segmented image and/or the received at least one image.

A ship assistance device including a storage medium storing a computer-readable command and a processor connected to the storage medium, the processor executing the computer-readable command to: calculate a pitching amount of a ship body based on a plurality of images photographed by a camera mounted on the ship body; estimate a pitching cycle of the ship body at least based on the calculated pitching amount; predict pitching of the ship body based on the estimated pitching cycle; and control a throttle of the ship body so as to reduce the predicted pitching of the ship body.

An enhanced lifeform transmission system to locate a lifeform (e.g., human, animal, etc.) wanting to be identified as a lifeform for collision avoidance. The lifeform transceiver is worn on a limb or neck of the lifeform. The collision avoidance system is housed inside the vehicle and receives communications from the lifeform transceiver. The lifeform is automatically detected and the lifeform transmitter is automatically turned on and off when a lifeform enters and exits a vehicle. The lifeform transceiver also tracks and protects the lifeform in a manufacturing facility or at an outdoor location.

A method for maintaining a marine vessel at a target position in a body of water, the method being carried out by a processing system and including: estimating at least one roughness condition of the body of water based on measurements related to an attitude of the marine vessel; calculating a desired linear velocity based on a difference between an actual position of the marine vessel and the target position; filtering an actual linear velocity of the marine vessel based on the roughness conditions; and operating a propulsion system of the marine vessel to move the marine vessel to minimize a difference between the desired linear velocity and the filtered actual linear velocity. A method for maintaining a marine vessel at a target heading in a body of water is also disclosed.