A method for assessing, by a robot, a feature of an environment based on data from one of the plurality of sensors, wherein the robot is positioned in the environment includes comparing, by the robot, the feature of the environment to an expected feature of the environment; creating, by the robot, a feedback loop based on the comparing step; and adjusting an operational condition of the robot based on the feedback loop.

This disclosure provides an autonomous vehicle driving system and method. A vehicle may be located by using a vehicle-determined location determined by a vehicle-side locator based on a differential operation and an environmental image captured by an image sensor, that is, a current vehicle location of the vehicle is determined, so that accuracy of vehicle locating is ensured. Further, vehicle driving may be controlled based on the current vehicle location and environmental sensing data, and thus autonomous driving can be implemented without a LiDAR during vehicle operation, thereby reducing costs without affecting the appearance and use of the vehicle.

An interface including a first detection unit, a second detection unit and a processor is disclosed. The first detection unit may be configured to detect a user intent to cause a vehicle movement via the interface. The second detection unit may be configured to receive movement inputs to cause the vehicle movement. The processor may determine that a user intends to cause the vehicle movement based on inputs obtained from the first detection unit, and determine that the movement inputs are received by the second detection unit within a predefined time duration of determining that the user intends to cause the vehicle movement, based on inputs obtained from the second detection unit. The processor may further transmit a command signal to the vehicle to cause the vehicle movement based on the movement inputs, responsive to determining that the movement inputs are received within the predefined time duration.

Automatic private public transport system, comprising a plurality of vehicles, operating by optoguidance along a colored strip, each vehicle incorporating an inertial unit, capable of managing all the parameters associated with the movements of a vehicle, namely: a starting point, the direction, accelerations, durations, as well as all the successive variations of these different parameters, the processing of the aforementioned data enables the on-board computer to calculate and define the vehicle's route and to store it, the said route data reproducing a succession of points on the said route, i.e. a virtual computer image of the colored strip of the route travelled. In this way, the on-board computer uses the virtual computer strip to continue its journey if the colored strip is no longer visible.

A method for detecting anomalies in a swarm system comprises: collecting first movement data from multiple vehicles moving as a swarm in a first scenario; generating first training data based on positioning data and second training data based on multi-channel inertial sensor data from the first movement data; training a first learning model using the first training data and multiple second learning models using the second training data for each vehicle; receiving real-time second movement data from vehicles moving as a swarm in a second scenario; generating first input data based on positioning data from the second movement data; inputting the first input data into the first learning model to detect abnormal vehicles in real-time; generating second input data for abnormal vehicles based on inertial sensor data from the second movement data; and inputting the second input data into the corresponding second learning model to identify abnormal channels in the inertial measurement unit of abnormal vehicles.

A robot device is disclosed. The robot device includes memory, a first LiDAR sensor and a second LiDAR sensor, and at least one processor configured to obtain bottom information about a bottom of a space where the robot device is located based on first sensing data received from the first LiDAR sensor and second sensing data received from the second LiDAR sensor, identify a plurality of sub spaces included in the space based on the bottom information, obtain driving level information including driving levels associated with each of the plurality of sub spaces, and control a movement of the robot device based on a driving level of a sub space corresponding to a location of the robot device, the sub space being among the plurality of sub spaces, and wherein the driving level is obtained from the driving level information stored in memory.

An automatically guided vehicle designed as a floor-bound conveying means which is automatically controlled and guided in a contact-free manner for use in a hall includes a travel drive having a travel converter and a dynamo-electric machine, with the travel converter including a printed circuit board. The dynamo-electric machine is connected to wheels of the vehicle. A sensor is arranged or formed in the travel converter on the printed circuit board of the travel converter and designed to detect an acceleration of the vehicle in x, y and/or z direction and/or detect a spatial location of the vehicle.

A system may include a row cleaner configured to apply a pressure to a field and a work vehicle coupled to the row cleaner and configured to traverse through the field, where the work vehicle comprising an engine configured to adjust a speed of the work vehicle. The system may also include a sensor configured to provide sensor data indicative of the field and a controller comprising a memory and a processor, where the controller is communicatively coupled to the row cleaner, the engine, and the sensor. The controller may provide an instruction to adjust the pressure applied by the row cleaner, the speed of the engine, or both based on the sensor data.

A monitoring device monitors a vehicle that travels through unattended operation using an electric motor as a power source, and includes: a selection unit that selects which one of a first method and a second method is used to acquire a speed of the vehicle according to a status about the speed of the vehicle, the first method being a method of acquiring a speed of the vehicle using a detection value from a vehicle speed sensor that makes an output in synchronization with rotation of a wheel of the vehicle, and the second method being a method of acquiring a speed of the vehicle using information about a rotational speed of the electric motor provided in the vehicle; and a speed acquisition unit that acquires a speed of the vehicle using the method selected by the selection unit.

A filtering method for a two-dimensional laser point cloud includes: acquiring a pitch angle and a roll angle of a mobile robot when the mobile robot is collecting a two-dimensional laser point cloud; determining a target tilt direction corresponding to the mobile robot and a target tilt angle corresponding to the two-dimensional laser point cloud according to the pitch angle and the roll angle; determining an angular filtering interval according to the target tilt direction and the target tilt angle, wherein the angular filtering interval is an angle interval corresponding to the laser point cloud to be filtered out; and filtering, by the mobile robot, the two-dimensional laser point cloud based on the angular filtering interval.