A self-moving device, including: a moving module, a task execution module, a control module. The control module is electrically connected to the moving module and the task execution module, controls the moving module to actuate the self-moving device to move, controls the task execution module to execute a working task. The self-moving device further includes a satellite navigation apparatus, electrically connected to the control module and configured to receive a satellite signal and output current location information of the self-moving device. The control module determines whether quality of location information output by the satellite navigation apparatus at a current location satisfies a preset condition, controls, if the quality does not satisfy the preset condition, the moving module to actuate the self-moving device to change a moving manner, to enable quality of location information output by the satellite navigation apparatus at a location after the movement to satisfy the preset condition.

A control device includes a direction identifying data generator that generates direction identifying data including at least a portion of acquired point group data indicating a position of a region including the ridge in front of an agricultural vehicle in a traveling direction, a direction identification part that identifies a direction of the ridge on the basis of the direction identifying data, and a travel control part that controls the agricultural vehicle such that the agricultural vehicle travels in the direction of the ridge identified by the direction identification part.

An autonomous tilting three-wheeled vehicle comprises a pair of front wheels coupled to a tiltable chassis by a mechanical linkage, such that the pair of wheels and the chassis are configured to tilt in unison with respect to a roll axis of the chassis. An electronic controller of the autonomous vehicle controls a tilt actuator to selectively tilt the chassis. Optionally, a steering actuator is coupled to the front wheels and controlled by the electronic controller to selectively steer the wheels. A sensor configured to measure orientation-dependent information may be coupled to the chassis by a gimbal configured to compensate for vehicle tilt. In some examples, the autonomous vehicle comprises an autonomous delivery robot.

Embodiments of present disclosure relate to a receiving station, an automatic guided vehicle (AGV), a pallet for use therewith, a conveying system, and a method for conveyance control. The receiving station comprises a carriage adapted to hook a pallet. The receiving station further comprises linear drive equipment operable to move the carriage in a first direction away from an AGV to load the pallet from the AGV or move the carriage in a second direction opposite to the first direction towards the AGV to unload the pallet onto the AGV, wherein the AGV stops near the receiving station, and wherein the carriage hooks the pallet during the movement of the carriage such that the pallet is moved along with the carriage.

Embodiments of the disclosure provide a transfusion guiding robot and a guiding method for guiding a movement of a transfused person. The transfusion guiding robot comprises a controller, a moving portion and a fixing portion. The controller is configured to receive and process instruction information, and control the transfusion guiding robot according to the instruction information; the moving portion is configured to move according to a command from the controller; and the fixing portion is configured to fix a container, a height of the fixing portion being adjustable.

A method for improving performance of a local device based on guide data from a remote device, according to one embodiment of the present disclosure, includes transmitting, to the remote device, first image data generated by the local device at a first time point, receiving guide data related to the first image data from the remote device, and registering, by a processor, the guide data to second image data generated by the local device at a second time point, based on first spatial information on the first image data, wherein the second time point is a time point that is after the first time point. A trained model for object recognition according to the present disclosure may include a deep neural network generated through machine learning, and the transmitting of the guide data may be performed in an Internet of Things (IoT) environment using a 5G network.

Provided herein is a system and method of a vehicle that detects a signal and reacts to the signal. The system comprises one or more sensors; one or more processors; a memory storing instructions that, when executed by the one or more processors, causes the system to perform detecting a signal from a source; determining an intended action of the vehicle based on the detected signal; sending, to the source, a response signal indicative of the intended action; determining whether the source has sent a response to the response signal; and in response to determining that the source has sent a response to the response signal, taking the intended action based on the response to the response signal.

Systems and methods for object detection in a dusty environment can enhance the ability of autonomous machines to distinguish dust clouds from solid obstacles and proceed appropriately. A library of dust classifiers can be provided, where each dust classifier is separately trained to distinguish airborne dust from objects in the environment. Different dust classifiers can correspond to different categories of dusty environments. Based on current conditions, control logic in an autonomous machine can categorize its environment and select a corresponding dust classifier. The dust classifier output can be used to alter a behavior of the autonomous machine, including a behavior of the control logic. For instance, the control logic can apply a consistency check to the output of the dust classifier and an output of an AI-based object classifier to detect instances where the object classifier misidentifies dust as an object.

To make it possible to correct a current position detected by an autonomous traveling work machine to the correct position with a simple configuration. A robot lawn mower includes a first position detecting unit for detecting a current position by using odometry and a second position detecting unit for detecting a current position by using an image capture. When position detection accuracy of both of the first and second position detecting units decreases to less than or equal to a predetermined value, the robot lawn mower is controlled to travel to either of zones Z1 and Z2 in which the position detection accuracy of the second position detecting unit is relatively high, and when the robot lawn mower moves to either of the zones Z1 and Z2, a current position used for autonomous traveling is corrected to the current position detected by the second position detecting unit.

A method for bypassing impassable objects by a robot through the use of artificial intelligence. A reliable and low-cost bypassing of obstacles taking account of data privacy aspects is achieved in that in the event of a collision of the robot with an obstacle, an optical original recording of the obstacle is produced, artificial duplicates being generated from the original recording, the duplicates being used to train the artificial intelligence. A system has a robot and an IT infrastructure configured to execute the method.