A method for operating a cleaning system that comprises at least one self-traveling cleaning device that travels in an environment based on an environment map and carries out cleaning activities. The cleaning device accesses a database, in which multiple cleaning activities are stored. A user accesses the database and defines in advance at least one randomly occurring event, depending on the occurrence of which at least one certain cleaning activity is carried out. The user defines an event-dependent activity scenario and the activity scenario is carried out upon the subsequent occurrence of the defined event. At least one cleaning activity is also scheduled time-dependently, and predefined rules determine whether only the event-dependent activity scenario or only the time-dependently scheduled cleaning activity is carried out if the time of an occurrence of a defined event falls short of a predefined minimum time interval.

A machine control system includes an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity/event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.

Autonomous carriers or totes that include vacuum units are provided. As the totes move or are moved through a warehouse carrying products, they collect debris. The debris can be analyzed at the tote, and actions can be performed based upon the analysis.

An automatic program-correction device includes: a clearance detecting unit that detects an amount of clearance between a robot and a peripheral device in an operation program; a near-miss detecting unit that detects a near-miss section; a closest-point detecting unit that detects a pair of closest points, in the near-miss section; and a program updating unit that generates a new operation program having an intermediate teaching point to which the closest points have been moved, along a straight line passing through the detected pair of closest points, until the amount of clearance becomes greater than a minimum amount of clearance and equal to or less than the threshold. While gradually reducing, from the threshold, the amount of clearance at the intermediate teaching point, the program updating unit obtains an intermediate teaching point that provides a maximum amount of clearance at which a new near-miss section is not detected.

An automatic program-correction device includes: a clearance detecting unit that detects an amount of clearance between a robot and a peripheral device in an operation program; a near-miss detecting unit that detects a near-miss section; a closest-point detecting unit that detects a pair of closest points, in the near-miss section; and a program updating unit that generates a new operation program having an intermediate teaching point to which the closest points have been moved, along a straight line passing through the detected pair of closest points, until the amount of clearance becomes greater than a minimum amount of clearance and equal to or less than the threshold. While gradually reducing, from the threshold, the amount of clearance at the intermediate teaching point, the program updating unit obtains an intermediate teaching point that provides a maximum amount of clearance at which a new near-miss section is not detected.

Disclosed is a mobile robot adapted to traverse vertical obstacles. The robot comprises a frame and at least one wheel positioned in a front section of the robot, at least one middle wheel positioned in a middle section of the robot, at least one back wheel positioned in a back section of the robot, and at least one further wheel in the front, middle or back of the robot. The robot also comprises at least one motor-driven device for exerting a downward and/or upward force on the middle wheel and at least two motors for driving the wheels and the motor-driven device. Also disclosed is a method of climbing using a mobile robot as disclosed.

Methods of operating a robotic breaker-racking apparatus are provided. A method of operating a robotic breaker-racking apparatus includes controlling a motor to drive the robotic breaker-racking apparatus to a first circuit breaker. The method includes accessing the first circuit breaker via remote or autonomous control of the robotic breaker-racking apparatus. Moreover, the method includes visually inspecting, via a camera of the robotic breaker-racking apparatus, a first relay of the first circuit breaker and/or a second relay of a second circuit breaker. Related robotic breaker-racking apparatuses are also provided.

A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

A combinable and detachable vehicle includes: a front connecting portion and a rear connecting portion provided at a front portion and a rear portion of a vehicle, respectively, and connected to another vehicle when the vehicle is combined with the other vehicle; a front monitoring portion and a rear monitoring portion monitoring a forward area and a rearward area of the vehicle, respectively; and a controller determining whether the other vehicle is connected to the front connecting portion or the rear connecting portion of the vehicle based on information received from the front connecting portion or the rear connecting portion, or the front monitoring portion or the rear monitoring portion, and controlling whether to operate each of a front component and a rear component of the vehicle depending on a direction of the vehicle is connected to the other vehicle.