An autonomous robot drive assembly includes a plurality of drive units. The plurality of drive units may allow for movement and control of the autonomous robot drive. Each of the plurality of drive units are configured to be oriented independent of the other drive units. Each drive unit may include a plurality of independently operable driven wheels. Each drive unit may further include a drive unit coupling, allowing for the drive unit to rotate independently of other portions of the autonomous robot. The drive unit coupling may not be driven and may be configured to freely rotate.

Disclosed herein are system, method, and computer program product embodiments for locating, identifying, and tracking a known criminal, fugitive, missing person, and/or any other person of interest. An embodiment operates by deploying an unmanned aerial vehicle, determining the mode of operation of the UAV, operating the UAV in accordance with the mode of operation of the UAV, determining whether a subject has been detected, capturing a first voice sample associated with the subject, authenticating the identity of the subject, and transmitting the GPS location of the unmanned aerial vehicle to a computing device.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

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 method for determining a motion path on a surface in an environment, along which motion path a mobile appliance, in particular a robot, preferably a domestic robot or a robot vacuum cleaner, is intended to move. The method includes obtaining environment information and determining a region of the surface intended to be covered by the motion of the mobile appliance; determining, while taking into account the environment information, whether within the region there is at least one uneven area in the surface that can be negotiated by the mobile appliance; and determining the motion path while taking into account the at least one uneven area, if there is one. A mobile appliance is also described.

A work vehicle that performs auto-steer driving in forward travel and backward travel includes a position sensor to output chronological position data of the work vehicle, a controller configured or programmed to, in an automatic steering mode, perform steering control for the work vehicle based on the chronological position data and a target path that is previously set, and a toggling switch to switch between forward travel and backward travel of the work vehicle. In the automatic steering mode, when a moving speed of the work vehicle is lower than a first speed, the controller is configured or programmed to determine a traveling direction of the work vehicle based on the chronological position data and a state of the toggling switch.

A method for deactivating a remote automatic parking mode of a vehicle in a parking garage. The vehicle being equipped with a tire monitoring system, including wheel units mounted in each wheel of the vehicle and each being provided with an accelerometer measuring a radial component of an acceleration of the wheel and being dependent on a terrestrial gravitational field. The vehicle being provided with an automatic parking mode remotely controlled by portable equipment carried by a user. The method includes, if the conditions for activating the measurements of the wheel units are verified: each wheel unit measuring acceleration values over time; comparing said values with a threshold value, for each wheel unit; if the acceleration values of at least two wheel units exceed said threshold value, then: detecting any movement of said vehicle in a parking garage; and deactivating the remote automatic parking mode.

A method and system are provided for characterizing a vehicle motion of an autonomous mobile robot in response to a triggering event. The method and system involve an autonomous mobile robot and a vehicle processor operable to navigate the autonomous mobile robot. The system further includes a motion characterization system coupled to the autonomous mobile robot, the motion characterization system comprising an odometry system operable to collect vehicle motion data associated with the vehicle motion; a triggering component; a storage component for storing an event start time, an event end time and the vehicle motion data between the event start time and the event end time; and a motion characterization processor operable to: receive an initialization input to initiate the triggering event; generate a trigger signal to cause the triggering component to cause the triggering event; and identify the event start time and an event end time.

Provided are an obstacle avoidance method for a self-propelled device, a medium, and a self-propelled device. The method includes acquiring a suspension height of an obstacle on a current travel route during traveling; determining whether the suspension height of the obstacle is within a preset limited height range, wherein the preset limited height range enables a part of the self-propelled device to pass the suspension height and an other part of the self-propelled device is limited by the suspension height; and acquiring, in response to determining that the suspension height of the obstacle is within the preset limited height range, current traveling state information of the self-propelled device, and determining whether to adjust the current travel route based on the current traveling state information to avoid the obstacle.