# US 11,432,694 B2 · Travel method of intelligent robot cleaner

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- Number: US 11,432,694 B2
- Document ID: US11432694B2
- Status: Granted
- Kind: B2
- Application number: 16/493,546
- Filed: 2019-07-05
- Published: 2021-11-25
- Granted: 2022-09-06
- Earliest claimed: 2019-07-05
- Assignees: LG Electronics Inc.
- Applicants: LG Electronics Inc.
- Inventors: Taehyun Kim; Beomoh Kim; Jichan Maeng; Wonho Shin; Jonghoon Chae
- Web page: https://patentgrep.com/patent/US11432694B2

## CPC

- A47L 9/2852: {Elements for displacement of the vacuum cleaner or the accessories therefor, e.g. wheels, casters or nozzles}
- A47L 9/2805: {Parameters or conditions being sensed}
- A47L 9/2857: {User input or output elements for control, e.g. buttons, switches or displays}
- A47L 9/2894: {Details related to signal transmission in suction cleaners}
- B25J 9/1666: {Avoiding collision or forbidden zones}
- B25J 9/1697: {Vision controlled systems}
- B25J 19/02: Sensing devices
- G05B 15/02: electric
- G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)}
- G05D 1/0221: {involving a learning process}
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06T 3/00: Geometric image transformations in the plane of the image
- G06T 3/06: Topological mapping of higher dimensional structures onto lower dimensional surfaces
- G06T 7/11: Region-based segmentation
- G06T 7/50: Depth or shape recovery
- G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)
- G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road}
- H04L 5/0048: {Allocation of pilot signals, i.e. of signals known to the receiver (allocation of control signalling H04L5/0053; use of control signalling H04L5/0091)}
- H04N 7/18: Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04W 56/001: {Synchronization between nodes}
- H04W 72/23: in the downlink direction of a wireless link, i.e. towards a terminal
- A47L 2201/04: Automatic control of the travelling movement; Automatic obstacle detection
- G05B 2219/31003: Supervise route, reserve route and allocate route to vehicle, avoid collision
- G05B 2219/31007: Floor plan, map stored in on-board computer of vehicle
- G05B 2219/45098: Vacuum cleaning robot
- G06N 3/08: Learning methods
- G06T 2200/04: involving 3D image data
- G06T 2207/20084: Artificial neural networks [ANN]
- A47L: DOMESTIC WASHING OR CLEANING (brushes A46B; cleaning quantities of bottles or of other hollow articles of one kind B08B9/00; laundry D06F); SUCTION CLEANERS IN GENERAL (cleaning in general B08)
- B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50})
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Disclosed herein is a travel method of an intelligent robot cleaner. According to the present disclosure, it is possible to minimize a cleaning travel path in a cleaning target region and thus reduce a cleaning time by accumulating a plus unit point when the cleaner travels along a plurality of cleaning travel paths, accumulating a minus unit point when the cleaner travels along an overlapped cleaning travel path among the plurality of cleaning travel paths, and determining a final cleaning travel path by learning the accumulated unit point. The intelligent robot cleaner can be associated with an artificial intelligence module, unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

## Claims

### Claim 1

A travel method of an intelligent robot cleaner, comprising:
extracting, by a processor, a cleaning space using an image photographed while traveling inside a building;
generating, by the processor, a 3D image in the cleaning space;
extracting, by the processor, spatial data from the 3D image, converting the extracted spatial data into plane data, and generating a 2D image using the converted plane data;
extracting, by the processor, a first region that allows cleaning and a second region that does not require cleaning from the 2D image, and simplifying the 2D image by removing the second region;
generating, by the processor, a plurality of ends each representing a distal end of the first region and a plurality of intersections each representing a point of intersection of cleaning subregions of the first region based on the simplified 2D image;
measuring, by the processor, distances of respective straight lines each of which comprises as end points two selected among the plurality of ends, distances of respective straight lines each of which comprises as end points two selected among the plurality of intersections, and distances of respective straight lines each of which comprises as one end point one selected among the plurality of ends and as the other end point one selected among the plurality of intersections, and setting a point value for each of the straight lines to be proportional to a corresponding measured stance;
forming, by the processor, a plurality of cleaning travel paths each of which comprises at least a part of the straight lines;
accumulating a total point value for each of the plurality of cleaning travel paths by totaling the corresponding set point values for each of the straight lines of the cleaning travel path, wherein a negative weight value is applied to a set point value for each instance that a straight line is traveled more than once; and
determining, by the processor, a final cleaning travel path among the plurality of cleaning travel paths based on the calculated total point value.

### Claim 2 (depends on 1)

The travel method of claim 1, wherein a probability of a cleaning travel path being selected as the final cleaning travel path is higher based on a higher total point value.

### Claim 3 (depends on 1)

The travel method of claim 1, wherein the extracting of the cleaning space comprises:
extracting an image characteristic value from the image photographed while the cleaner travels inside the building;
inputting the image characteristic value into an artificial neural network classifier;
analyzing an output value outputted from the artificial neural network classifier; and
extracting the cleaning space inside the building from the output value.

### Claim 4 (depends on 1)

The travel method of claim 1, wherein the extracting of the cleaning space is based on analysis of a cleaning map of an inside of the building.

### Claim 5 (depends on 1)

The travel method of claim 1, wherein the negative weight value is −1.5.

### Claim 6 (depends on 1)

The travel method of claim 1, further comprising:
turning off a suction motor when the cleaner travels along a particular straight line belonging to the final cleaning travel path for a second time or more.

### Claim 7 (depends on 1)

The travel method of claim 1, further comprising:
receiving, by the processor, downlink control information (DCI) used to schedule transmission of the set point values from a network,
wherein the set point values are transmitted to the network based on the DCI.

### Claim 8 (depends on 7)

The travel method of claim 7, further comprising:
performing, by the processor, an initial access procedure with the network based on a synchronization signal block (SSB),
wherein the set point values are transmitted through physical uplink shared channel (PUSCH) to the network, and the SSB and demodulation reference signal (DMRS) of the PUSCH is quasi co-located (QCL) for a QCL type D.

### Claim 9 (depends on 8)

The travel method of claim 8, further comprising:
controlling, by the processor, a communication unit to transmit the set point values to an artificial intelligence (AI) processor included in the network; and
controlling, by the processor, the communication unit to receive AI processed information from the AI processor,
wherein the AI processed information is information that determines the final cleaning travel path among the plurality of cleaning travel paths.