A mobile object control device including a storage medium and a processor connected to the storage medium is presented. The processor acquires a photographed image, which is obtained by photographing surroundings of a mobile object by a camera mounted on the mobile object, and an input instruction sentence, which is input by a user of the mobile object; detects a stop position of the mobile object corresponding to the input instruction sentence in the photographed image by inputting at least the photographed image and the input instruction sentence into a trained model including a pre-trained visual-language model, the trained model being trained so as to receive input of at least an image and an instruction sentence to output a stop position of the mobile object corresponding to the instruction sentence in the image; and causes the mobile object to travel to the stop position.

A distributed control system with supplemental attitude adjustment including an aircraft control having an engaged state and a disengaged state. The system also including a plurality of flight components and a plurality of aircraft components communicatively connected to the plurality of flight components, wherein each aircraft component is configured to receive an aircraft command and generate a response command directing the flight components as a function of supplemental attitude. The supplemental attitude based at least in part on the engagement datum and generating a supplemental attitude includes choosing a position supplemental attitude if the aircraft control is disengaged and choosing a velocity supplemental attitude if the aircraft control is engaged. In generating the response command, the aircraft attitude is combined with the supplemental attitude to obtain an aggregate attitude, and the aircraft component is configured to generate the response command based on the aggregate attitude.

A moving object control system that controls an operation of a moving object obtains information of a sensor configured to recognize a periphery of the moving object, generates a dynamic prediction map including information indicating a position of a static obstacle recognized based on the information of the sensor and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor, and generates a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map.

According to an embodiment, a control device controls a user-wearable flight device and includes a processing unit configured to acquire state data related to a state of the flight device and manipulation data related to a manipulation of the flight device, input the acquired state data and the acquired manipulation data to a model trained using deep reinforcement learning, and control the flight device on the basis of an output result of the model to which the state data and the manipulation data are input.

A transportation device may include: a receiver configured to receive a coupling request including location information and device identity information; a mobility mechanism configured to drive the transportation device to a location indicated by the location information in the received coupling request; an authentication module configured to authenticate a movable device at the indicated location based on the device identity information in the received coupling request; and a coupler selectively engageable with the movable device based on the authentication, wherein on the selective engagement of the coupler with the movable device, movement of the transportation device correspondingly moves the movable device.

A moving object control system includes a storage device configured to store instructions; and one or more processors, wherein the one or more processors executes the instructions stored in the storage device to determine a stop position of a moving object in a sidewalk region near a specific location in a case where a degree of congestion of a sidewalk near the specific location is less than a threshold value, and determines the stop position in a region that does not belong to the sidewalk region near the specific location in a case where the degree of congestion of the sidewalk near the specific location is equal to or more than the threshold value.

Disclosed are methods and systems for facilitating takeoff and landing of an aircraft. For instance, the method may include obtaining aircraft information and retrieving vertiport information for a desired landing or takeoff location area. The method may further include determining an aircraft path including a vertical path portion and a cruise path portion; determining a dynamic switchover point between the vertical path portion and the cruise path portion along the aircraft path; and transmitting control information including a vertical control portion and a cruise control portion to aircraft propulsion systems. Wherein the aircraft propulsion systems will operate under one of the vertical control portion or the cruise control portion until the aircraft reaches the dynamic switchover point, and wherein the aircraft propulsion systems will operate under the other of the vertical control portion or the cruise control portion after the aircraft reaches the dynamic switchover point.

The present application discloses methods and systems for mobility device control by capturing electroencephalogram (EEG) brain wave data to extract steady-state visually evoked potential (SSVEP) signal data and decoding the SSVEP signal data into at least one command for controlling the mobility device. The SSVEP signals are triggered through visual stimuli on a screen of a device, such as a user device, and are decoded through a machine learning pipeline. At least one of cloud, edge or fog principles may be utilized to enhance response time. The use of the machine learning pipeline and at least one of cloud, edge or fog technology provides an improved mobility device control system response time when compared to the current and prior alternatives for mobility device control systems.

Aspects relate to systems and methods for guided autonomous locomotion of a carriage, including a compartment configured to ensconce a child, a frame configured to support the compartment, a drive motor, a drivetrain operatively coupled to the drive motor; a drive wheel rotatably affixed to the frame, configured to contact a support surface and operatively coupled to the drivetrain, wherein operating the at least a drive motor causes the at least a drive wheel to rotate, an environmental sensor configured to sense an environmental characteristic related to an environment substantially surrounding the carriage; a battery configured to power the at drive motor and a controller configured to control the drive motor in response to the environmental characteristic.

A control method, which is to be performed by a computer to control an electric vehicle that includes an operation component, includes: setting a traveling mode of the electric vehicle to a manual mode in which the electric vehicle travels in a direction and at a speed that are based on an operation performed on the operation component by an occupant of the electric vehicle; determining whether a passage having a width that satisfies a predetermined condition is present within a detection range, the detection range being set in a vicinity of the electric vehicle and being based on the operation; and when the passage is determined to be present, switching the traveling mode of the electric vehicle from the manual mode to a passage mode in which the electric vehicle autonomously travels along the passage.