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.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

An autonomous mobile machine includes a controller. The controller is configured to execute program instructions to implement following steps: controlling, in response to a mapping task instruction, the autonomous mobile machine to move from a mapping start point to a mapping end point, the mapping end point being located in a loading space of a cargo container transportation vehicle; generating a point cloud map during a process of the movement by using point cloud data scanned by the sensor, the point cloud data including point cloud data obtained by scanning the cargo container transportation vehicle; and obtaining a planning map based on the point cloud map. The planning map is used to plan a path within the cargo container transportation vehicle.

In some embodiments, provided is a robot for water tunnel inspection, comprising: (a) a shell, comprising an upper shell and a lower shell; wherein the upper shell and the lower shell are sized and shaped to match each other, together defining a closed cavity therewithin; (b) a camera system, configured to capture an image or video of a field of view of surrounding; (c) a lighting system, configured to provide illumination at least partially for the field of view; (d) a propulsion system, configured to provide propulsion force to the robot in water; and (e) a controlling system, configured to provide power and control operation of the robot, wherein the robot is configured to float on water and to have a center of gravity positioned lower than geometric center. Other example embodiments are described herein. In certain embodiments, the robots provide safe and efficient tunnel inspections without human operation.

A mobile internet of things (IoT) unit for cleaning grease vents, herein referred to as the unit, is disclosed. The unit is comprised of the following parts: a mobile platform with magnetic tracks; a mobile device software application (app); cleaning attachments such as power washers and lasers, sensors such as conductivity meters (to measure buildup), air temperature, velocity and pressure; recording devices such as digital still and streaming cameras; a microcontroller with wireless communications; onboard lighting and a rechargeable battery. Additional details regarding the unit are examined further in this disclosure.

An inspection robot, including, a multi-sensor sled carriage including a plurality of sleds arranged on the multi-sensor sled carriage, each one of the plurality of sleds including a sensor opening, an acoustic inspection sensor coupled to the corresponding sensor opening; where a width of the multi-sensor sled carriage and a radial position of the plurality of sleds are configured to accommodate an inspection surface including a pipe outer diameter; a plurality of acoustic inspection sensors coupled to the sensor opening of the plurality of sleds.

Provided is a crewless aircraft capable of accurately estimating its own position even inside a manhole, as well as a crewless aircraft control method. A crewless aircraft according to the present invention is a crewless aircraft used to inspect an interior of a manhole, and includes: a camera sensor that captures an image of a manhole opening; a plurality of rangefinders that measure a distance to a ground surface or a predetermined surface in the interior; and a control unit that estimates an own position on the basis of recognition information of the manhole opening obtained by performing recognition on image information obtained from the camera sensor, and distance information of the distance to the ground surface or the predetermined surface obtained from the rangefinders.

A robot that is mobile and includes: a controller that controls the robot; and an action unit that performs an action based on an instruction from the controller. The controller causes the action unit to perform a preliminary action before the robot starts to move in a place where the robot and a person are present together.

A robot sized and shaped for reception in a pipe, the robot including a chassis configured for movement of the robot in the pipe, a plurality of sensors including an inertial measurement unit (IMU), an encoder and a stereo vison camera associated with the robot, and a sensor fusion system operable to combine readings from the IMU, the encoder and the stereo vision camera to determine a position of the robot within the pipe, and wherein the sensor fusion system is operable to use machine learning in creating a digital twin of the pipe.

Disclosed are a method for scheduling a task, an autonomous mobile machine and a controller. The method for scheduling the task includes: sending a mapping task instruction to a first autonomous mobile machine; receiving a planning map sent by the first autonomous mobile machine, where the planning map is obtained after the first autonomous mobile machine scans and maps a cargo container transportation vehicle by using a sensor mounted on the first autonomous mobile machine; and generating a handling task based on the planning map, and sending a handling task instruction to a second autonomous mobile machine, where the handling task instruction includes a handling task path. For a scenario where a docking position and a docking posture of the cargo container transportation vehicle are different each time, the present disclosure may ensure that the autonomous mobile machine executes the handling task accurately.