The presently disclosed subject matter includes a system and a method of navigating an unmanned ground vehicle (UGV) vehicle comprising a scanning device and an Inertial Navigation System (INS) being operatively connected to at least one processor. Operating the scanning device for scanning an area surrounding the UGV, and generate scanning output data; Generating, based on the scanning output data, a map representing at least a part of the area, the map being relative to a location of the UGV and comprising cells, each cell is classified to a class selected from at least two classes, comprising traversable and non-traversable, and characterized by dimensions larger than an accumulated drift value of the INS over a predefined distance; receiving INS data indicative of a current location of the UGV and updating a location of the UGV relative to cells in the map based on the INS data.

An arousal support device including a processor programmed to output a dialogue speech in a form of a question, and obtain response speech which is a response of a driver to the dialogue speech; measure a response time from when the dialogue speech is output till the response speech is obtained; store the measured response time in a database; derive an estimated value of wakefulness of the driver based on the measured response time and a plurality of response times previously stored in the database; and output a signal corresponding to the estimated value to provide arousal support.

A system and method of navigating a vehicle, the vehicle comprising a scanning device and a self-contained navigation system (SCNS) operatively connected to a computer, the method comprising: operating the scanning device for repeatedly executing a scanning operation, each operation includes scanning an area surrounding the vehicle, thereby generating respective scanning output data; operating the computer for generating, based on the scanning output data, a relative map representing at least a part of the area, the map having known dimensions and being relative to a position of the vehicle, wherein the map comprises cells, each cell classified to a class from at least two classes, comprising traversable and non-traversable, and characterized by dimensions equal or larger than an accumulated drift value of the SCNS; wherein non-traversable cells correspond to identified obstacles; receiving SCNS data and updating a position of the vehicle relative to the cells based on the SCNS data.

A wheeled base includes a housing, two driven wheeled mechanisms positioned on a bottom of the housing and on opposite sides of the housing, at least one passive wheel positioned on the bottom of the housing, actuated feet positioned on the bottom of the housing and configured to move up and down, sensors, and a battery pack arranged within the housing. The two driven wheeled mechanisms each includes a damping mechanism, and each damping mechanism includes at least two dampers configured to absorb impact caused by an upward movement of the housing, and absorb impact caused by a downward movement of the housing.

Disclosed are a control method of a mobile robot, a mobile robot, and a storage medium. The method includes: when distance information obtained by a laser radar has an effective distance change, predicting an inclination angle of a forward road section relative to a current road surface where the mobile robot is currently located; determining a slope of the forward road section based on the inclination angle and a pitch angle; controlling movement of the mobile robot based on the slope and a result of comparison between the slope and a slope threshold preset by the mobile robot; and marking the forward road section as a slope area on a slope map, where the slope map is used to control, based on the slope area when the mobile robot passes by the slope area, the mobile robot to send climbing warning information.

A computer-implemented method for controlling a vehicle includes receiving, via a processor, from two or more IX control devices disposed at a two or more stationary positions having known latitudes longitudes and orientations, first sensory data identifying the position and dimensions of a feature in a mapped region. The processor generates a plurality of IX nodes based on the first sensory data received from the IX control devices, and receives LiDAR point cloud that includes LiDAR and other vehicle sensory device data such as Inertial Measurement Unit (IMU) data received from a Vehicle (AV) driving in the mapped region. The LiDAR point cloud includes a simultaneous localization and mapping (SLAM) map having second dimension information and second position information associated with the feature in the mapped region. The processor generates, without GPS and/or real-time kinematics information, an optimized High-Definition (HD) map having Absolute accuracy using batch optimization and map smoothing.

Systems and techniques for compensating for the forces exerted on the autonomous lawn mower exerted by operating on a sloped region to be mowed are provided herein. In some examples, such systems and techniques may include receiving a coverage plan of an area to be mowed that includes a sloped region, determining, based on data for the one or more sensors, an orientation of the autonomous lawn mower and determining a slope force to compensate for the slope on which the autonomous lawn mower is operating. The slope force is then converted into signals to generate torques at one or more wheels to compensate for the slope.

A robotic lawn mower includes a first wheel driven by a first electric wheel motor, a second wheel driven by a second electric wheel motor, a cutting implement driven by an electric cutting implement motor, a power system for powering the electric wheel motors and the electric cutting implement motor, and a controller configured to control operation of the electric wheel motors and the electric cutting implement motor to autonomously mow a yard. The power system includes multiple removable rechargeable battery modules and multiple receptacles, each receptacle configured to receive one of the battery modules.

A control device and a control method can quickly estimate a self-location even when the self-location is unknown. In a case of storing information supplied in a time series detected by LIDAR or a wheel encoder and estimating a self-location by using the stored time-series information, when a position change happens unpredictably in advance such as a kidnap state is detected, the stored time-series information is reset, and then the self-location is estimated again. Example host platforms include a multi-legged robot, a flying object, and an in-vehicle system that autonomously moves in accordance with a mounted computing machine.

Robot localization or mapping can be provided without requiring the expense or complexity of an “at-a-distance” sensor, such as a camera, a LIDAR sensor, or the like. Landmark features can be created or matched using motion sensor data, such as odometry or gyro data or the like, and adjacency sensor data. Despite the relative ambiguity of adjacency-sensor derived landmark features, a particle filter approach can be configured to use such information, instead of requiring “at-a-distance” information from a constant stream of visual images from a camera, such as for robot localization or mapping. Landmark sequence constraints or a Wi-Fi signal strength map can be used together with the particle filter approach.