A method models a navigation environment of a vehicle equipped with environment perception structure, with a decision module, and with an autonomous controller for autonomously controlling the vehicle. The method includes defining a global environment model of the vehicle as being a structured set of information constructed from data supplied by the environment perception structure, and receiving, from the decision module, a request for information relating to a decision to be taken to control the vehicle by way of the autonomous controller.

A method models a navigation environment of a vehicle equipped with environment perception structure, with a decision module, and with an autonomous controller for autonomously controlling the vehicle. The method includes defining a global environment model of the vehicle as being a structured set of information constructed from data supplied by the environment perception structure, and receiving, from the decision module, a request for information relating to a decision to be taken to control the vehicle by way of the autonomous controller.

The learning system SY1 acquires control information output from a control model M by inputting to the control model M environmental information including weather information in at least one of a surrounding environment of an unmanned aerial vehicle P and an environment of a planned flight area of an unmanned aerial vehicle P, and when the unmanned aerial vehicle P takes an action based on the control information, performs reinforcement learning of the control model M using a reward r representing an evaluation of a result of the action.

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; capturing one or more images of the structure from the pose.

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; capturing one or more images of the structure from the pose.

There is provided a mobile body that includes an imaging unit to capture an image of an environment around the mobile body, an estimation unit to estimate a position of the mobile body on the basis of the image captured by the imaging unit, a calculation unit to calculate the position of the mobile body on the basis of a control command for controlling movement of the mobile body, and a wind information calculation unit to calculate information regarding wind acting on the mobile body on the basis of a first position that is the position of the mobile body, which is estimated by the estimation unit, and a second position that is the position of the mobile body, which is calculated by the calculation unit.

A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels based on the at least one original set of sensor data. The method also includes generating a spherical depth map based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.

A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels based on the at least one original set of sensor data. The method also includes generating a spherical depth map based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.

A disturbance estimation apparatus that includes a position data receiver, a thrust data receiver, and processing circuitry is provided. The position data receiver receives position data indicating a position of a ship. The thrust data receiver receives thrust data indicating a thrust force driving the ship during navigation. The processing circuitry determines a magnitude of the thrust force based on the thrust data, and determines, based on the position data, disturbance data including a drift direction in which the ship drifts due to an external force and a drift speed of the ship while the thrust force is less than a threshold value. The processing circuitry outputs the disturbance data that indicates disturbance acting on the ship and assists to control movement of the ship for automatically maintaining a selected position or heading direction of the ship.

An unmanned aerial vehicle according to certain embodiments generally includes a chassis, a control system, and at least one rotor. The chassis includes a first battery compartment configured to receive sliding insertion of a first battery, and a second battery compartment configured to receive sliding insertion of a second battery. The control system is operable to receive power from the first battery when the first battery is received in the first battery compartment, and is operable to receive power from the second battery when the second battery is received in the second battery compartment. The at least one rotor is operable to generate lift under control of the control system. The control system is configured to remain at least partially active under power supplied by the first battery when the second battery is removed from the second battery compartment.