A moving object manufactured in a factory comprises: a driving controller that implements driving control over the moving object by unmanned driving during a course of manufacture of the moving object in the factory; a process completion detector that detects completion of a process by at least one step included in the course of manufacture; and a control content change unit that changes a content in control over the moving object when the completion of the process is detected.

A remotely controlled vehicle (RCV) includes a vehicle propulsion system constructed and arranged to move the RCV. The RCV further includes a vehicle control computer coupled with the vehicle propulsion system. The vehicle control computer is constructed and arranged to operate the vehicle propulsion system. The RCV further includes electronic safety equipment coupled with the vehicle propulsion system. The electronic safety equipment is constructed and arranged to perform a method which includes receiving a set of speed signals indicating a current speed of the RCV. The method further includes performing a comparison operation which compares the current speed of the RCV, as indicated by the set of speed signals, to a predefined maximum speed. The method further includes triggering an emergency vehicle stop in response to a result of the comparison operation indicating that the current speed of the RCV exceeds the predefined maximum speed by a predefined amount.

A UAV includes a body and rotor coupled to the body. The UAV may include a boom coupled to the body, and a nozzle coupled to a distal end of the boom, wherein an operational configuration of the nozzle is responsive to a second control signal. The rotor, boom, and nozzle are arranged such that the nozzle is disposed further away from the body than the rotor. The UAV may further include a sensor disposed on either the body or the boom, wherein the sensor is configured to generate a detection signal associated with a distance between the sensor and a surface disposed proximate to the sensor.

A method performed by a control unit for operating an autonomous vehicle is provided. The control unit is arranged to communicate via at least one antenna. The control unit obtains, based on a signal from the at least one antenna, information relating to at least one geographical zone associated with a transponder as the autonomous vehicle moves in proximity of the transponder. Also, the control unit determines an autonomous operating mode of the autonomous vehicle based on the obtained information relating to the at least one geographical zone associated with the transponder. The control unit further operates the autonomous vehicle in accordance with the determined autonomous operating mode.

An intelligent obstacle detection system for an unmanned mine vehicle is provided to solve the problem of existing intelligent obstacle detection systems for unmanned mine vehicles cannot compare and analyze the actual driving data with the preset data and includes an intelligent detection platform, a route planning device, an obstacle detection device, a planning management device, an operation monitoring device, and a storage device. The route planning device is configured to perform route planning analysis for the unmanned mine vehicle to obtain a planned route of the unmanned mine vehicle. The planned route is sent to the obstacle detection device. The intelligent obstacle detection system can plan and analyze the travel route. By locking onto starting and target positions of the unmanned mine vehicles, and then obtaining point cloud data through the detection terminals and calculating with algorithms, the optimal planned route can be determined.

A drone delivery system and network includes host sites that are geographically arranged in a region having overlapping drone delivery ranges. A central flight server of the system determines delivery flight paths for a drone transporting a payload from an originating host site to another host site or a target site in the network. The flight range of the drone is extended based on a delivery type for the payload and a distance of the target site from the originating host site.

Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a subset of the plurality of agents having an anomaly sensor subsystem; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents; obtaining pre-surveyed map data; and determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data.

A moving object operable by remote control includes a detection unit configured to detect a signal transmitted from a control device for remotely controlling the moving object; and a control signal acceptance unit configured to switch to either an activation state of accepting a control signal transmitted from the control device for remotely controlling the moving object or a deactivation state of not accepting the control signal, wherein the control signal acceptance unit switches to the activation state when the signal is detected by the detection unit.

A remote support apparatus remotely supports a travelling of a vehicle. The remote support apparatus communicates wirelessly with the vehicle. The remote support apparatus wirelessly acquires an image of a view in a travelling direction of the vehicle and display the acquired image by a displaying device of the remote support apparatus. The image is taken by an imaging device of the vehicle. The remote support apparatus displays an inner rear wheel line in the image displayed by the displaying device while the vehicle is turning. The inner rear wheel line is a line along which an inner rear wheel of the vehicle during vehicle turning is predicted to move.

Embodiments of the present disclosure may include a method for optimizing flight of an unmanned aerial vehicle (UAV) including a payload, the method including receiving one or more human-initiated flight instructions. Embodiments may also include determining a UAV context based at least in part on Inertial Measurement Unit (IMU) data from the UAV. Embodiments may also include receiving payload identification data. Embodiments may also include accessing a laden flight profile based at least in part on the payload identification data. Embodiments may also include determining one or more laden flight parameters. In some embodiments, the one or more laden flight parameters may be based at least in part on the one or more human-initiated flight instructions, the UAV context, and the laden flight profile.