METHOD FOR AUTONOMOUS PROCESSING OF SOIL SURFACES

Abstract

The invention relates to a method for autonomous processing of floor surfaces by means of a mobile, self-driving device, in particular a floor cleaning device such as a suction robot (4) and / or sweeping and / or mopping robot, comprising the following method steps: performing an exploration travel of the mobile, self-driving device in a proposed floor processing region for establishing a surroundings map (10), detecting obstacles (2, 3) by means of a detection device, classifying the obstacles (2, 3) as passable or not passable, and traveling over an obstacle (2) classified as passable and / or traveling around an obstacle (3) classified as not passable. The invention also relates to a mobile, self-driving device which is suitable for carrying out such a method.

Description

[0001] Method for autonomous processing of soil surfaces

[0002] The invention relates to a method for the autonomous processing of floor surfaces using a mobile, self-propelled device, in particular a floor cleaning device such as a vacuum and / or sweeping and / or mopping robot. Furthermore, the invention relates to a mobile, self-propelled device with which such processing can be carried out.

[0003] Mobile, self-driving devices such as robot vacuum cleaners are designed to autonomously clean as entire a floor as possible. However, obstacles such as furniture, furnishings, small objects, or door thresholds prevent the robot vacuum cleaner from reaching all areas of the floor. Common robot vacuum cleaners attempt to overcome thresholds and obstacles if they are in their path of travel. Obstacles that are too large are usually detected by the bumper sensors mounted in the direction of travel. Small obstacles can be driven over by the robot vacuum cleaner. However, once the obstacle reaches a certain height, the robot vacuum cleaner is no longer able to push itself over it. After a few attempts, the robot vacuum cleaner registers that it cannot overcome the obstacle and then attempts to avoid the area, although this may already have caused damage to the obstacle.

[0004] Depending on the robot vacuum's capabilities, it can overcome low thresholds by passing over them. For example, door thresholds can be driven over to continue the cleaning job. These can usually be overcome by the robot vacuum with sufficient momentum. Other thresholds, such as crossbars on furniture at ground level, such as rocking chairs or the feet of clothes racks, cannot be driven over by the robot vacuum. The robot vacuum must drive around these. Thresholds with a narrow profile pose the risk that the robot vacuum will get stuck after driving over them and will no longer be able to extricate itself. Especially with furniture with high-quality surfaces, such as chrome-plated, painted surfaces or surfaces made of glass, attempts by the robot vacuum to free itself can lead to adverse surface damage.Furthermore, the robot vacuum's attempts to free itself can produce annoying and unpleasant noises that impair the user's perception of quality. The object of the invention is to provide an effective, optimized, and / or non-damaging method for autonomously cleaning floor surfaces, which, in particular, ensures the most complete cleaning of the floor surface possible while simultaneously avoiding damage to obstacles caused by the mobile, self-propelled device.

[0005] This object is achieved by a method for cultivating soil surfaces having the features of claim 1 and by a mobile, self-propelled device having the features of claim 9. Advantageous embodiments and further developments are the subject of the subclaims.

[0006] According to the invention, a method for autonomously treating floor surfaces using a mobile, self-propelled device, in particular a floor cleaning device such as a vacuum and / or sweeping and / or wiping robot, comprises the following method steps:

[0007] Carrying out an exploration drive of the mobile, self-propelled device in a designated soil cultivation area to create an environmental map,

[0008] Detecting obstacles using a detection device,

[0009] Classifying the obstacles as passable or impassable, and driving over an obstacle classified as passable and / or bypassing an obstacle classified as impassable.

[0010] The inventive solution is characterized by classifying detected obstacles based on their position in the surrounding map and classifying them as surmountable if they are located within a passageway. Other obstacles are classified as insurmountable and avoided by the mobile, self-propelled device before attempting to cross them. This advantageously prevents damage to these obstacles from the outset. The mobile, self-propelled device therefore assesses whether the detected obstacle is surmountable even before attempting to cross it. The mobile, self-propelled device classifies the obstacles based on existing map data from the surrounding map.In particular, the mobile, self-driving device identifies an obstacle before driving over it, compares it with information from known map data, classifies it as passable or impassable and reacts accordingly by driving over obstacles classified as passable and / or bypassing obstacles classified as impassable.

[0011] The solution according to the invention advantageously reduces the risk of the mobile, self-propelled device getting stuck on flat obstacles such as ground-level chair supports. The risk of damaging high-quality furniture is also reduced. In addition, the signs of wear on the mobile, self-propelled device are reduced due to the reduced contact with the furniture. Advantageously, there is less noise during the cleaning process because the device no longer crashes into flat obstacles at maximum speed, for example, or drives into them and gets stuck, with the device's wheels rattling over the obstacle. Because the risk of the device getting stuck is reduced, the cleaning job can usually be completed without user intervention. The risk of cleaning being aborted is advantageously reduced. Cleaning time can also be reduced because the device does not waste time attempting to overcome obstacles.

[0012] A mobile, self-propelled device is understood in particular to be a floor cleaning device, such as a cleaning or lawnmower, which autonomously cleans floors or lawns, particularly in the home. These include, among others, vacuuming and / or sweeping and / or mopping robots such as robot vacuum cleaners or robot lawnmowers. These devices preferably operate without, or with as little as possible, user intervention during operation (cleaning or lawnmowing). For example, the device moves independently within a specified space to clean the floor according to a predefined and programmed process strategy.

[0013] An exploration trip is understood in particular to be a reconnaissance trip suitable for exploring a soil area to be worked, looking for obstacles, room layout, and the like. The goal of an exploration trip is, in particular, to be able to assess and / or depict the conditions of the soil processing area to be worked. A soil processing area is understood to be any spatial area intended for processing, in particular cleaning. This could be, for example, a single (living) room or an entire apartment. It can also be understood to mean only areas of a (living) room or an apartment that are intended for cleaning.

[0014] Obstacles are understood to mean any objects and / or items that are located in the soil processing area, for example lying there, and that influence the processing by the mobile, self-propelled device, in particular hinder and / or interfere with it, such as thresholds, door thresholds, furniture, walls, curtains, carpets and the like.

[0015] Passable obstacles are defined as obstacles that the mobile, self-propelled device can negotiate due to their low height without getting stuck or crashing into them. For example, door thresholds or carpets can be classified as passable obstacles.

[0016] Impassable obstacles are understood to be obstacles that the mobile, self-propelled device cannot drive over due to their height, i.e. it would drive into them or get stuck on them, thus interrupting the cleaning process.

[0017] After the exploratory drive, the mobile, self-driving device is familiar with its surroundings and can communicate this information to the user in the form of an environment map, for example, in an app on a mobile device. The detected passable and impassable obstacles are preferably displayed on the environment map. Particularly preferred are the detected obstacles displayed according to their classification. For example, obstacles classified as passable are displayed in a different color, shape, or similar to those classified as impassable.

[0018] An environmental map is understood to mean, in particular, any map suitable for depicting the surroundings of the tillage area with all its obstacles. For example, the environmental map shows a sketch of the tillage area with the obstacles and walls it contains.

[0019] The surrounding map with the obstacles is preferably displayed in the app on a portable device. This primarily serves to visualize a possible interaction for the user.

[0020] In the present case, an additional device is understood to mean in particular any device that is portable for a user, that is arranged outside the mobile, self-propelled device, in particular that is differentiated from the mobile, self-propelled device, and that is suitable for displaying, providing, transmitting and / or transmitting data, such as a mobile phone, a smartphone, a tablet and / or a computer or laptop.

[0021] The portable attachment is equipped with an app, particularly a cleaning app, which serves to communicate between the mobile, self-propelled device and the attachment and, in particular, enables visualization of the floor-treatment area, i.e., the living space or living area to be cleaned. The app preferably shows the user the area to be cleaned as a map of the surrounding area, along with any obstacles.

[0022] A detection device is any device capable of reliably detecting both passable and impassable obstacles. This is preferably laser-based, sensor-based, and / or camera-based.

[0023] Classification refers, in particular, to categorizing obstacles and / or objects as passable or impassable. Additional classifications can also be made, such as flat and uneven obstacles, or similar.

[0024] In an advantageous embodiment, the obstacles are classified based on existing map data obtained during the exploration drive. In particular, the detected obstacles are compared with information from the surrounding map in order to classify or assess them accordingly. Preferably, the classification is performed by comparing information from the exploration drive with information obtained upon detection of the obstacle. Particularly preferably, the mobile, self-driving device automatically identifies spaces as such for classification purposes based on information from its exploration drive.

[0025] For example, the mobile, self-driving device has a map of its surroundings from the exploration drive. Based on the geometry, the device can estimate which areas correspond to rooms in reality. Narrowed areas located between adjacent rooms in the environment map are identified as doors or thresholds and are therefore classified as passable.

[0026] Alternatively, the user can use the app on the portable device to specify which areas or regions on the surrounding area map correspond to which rooms in their home. Here, too, the device can automatically identify narrow spaces between adjacent rooms as doors or thresholds and thus classify them as passable.

[0027] In an advantageous embodiment, the obstacles are classified before an attempt to drive over them. In this case, an attempt to overcome the obstacle is therefore not made if the obstacle is classified as impassable, in order to prevent possible damage to the obstacles or the mobile, self-propelled device from becoming stuck.

[0028] In an advantageous embodiment, the mobile, self-propelled device determines the position of detected obstacles in the surrounding map. In particular, the location of the detected obstacle in the surrounding map is determined as precisely as possible. If the obstacle is located near a door or a room transition, the obstacle is classified as a threshold, in particular a door or room threshold, that can be overcome. If the detected obstacle is located within a room, it can be assumed that it is a piece of furniture. The mobile, self-propelled device will avoid this during cleaning.

[0029] In particular, obstacles near the door or near the wall are classified as door thresholds and obstacles far from the door or far from the wall are classified as furniture, with obstacles near the door being driven over and obstacles far from the door being driven around before an attempt is made to drive over the obstacles far from the door.

[0030] According to the invention, a mobile, self-propelled device, in particular a floor cleaning device for autonomously cleaning floor surfaces, such as a vacuum and / or sweeping and / or mopping robot, comprises a detection unit for detecting obstacles and an evaluation unit for classifying the obstacles as passable or impassable. In particular, the detection unit comprises sensors that determine distance measurements and / or temporal changes in sensor values.

[0031] Any features, configurations, embodiments and advantages relating to the method also apply in connection with the mobile, self-propelled device according to the invention, and vice versa.

[0032] An evaluation unit is understood to mean, in particular, any device capable of classifying obstacles and / or objects as passable or impassable, particularly based on the obstacle's position in its surroundings. A more detailed classification of obstacles is not necessarily required, but can be implemented.

[0033] To detect an obstacle before the mobile, self-driving device collides with it, sensors similar to the well-known cliff sensors can be used. If their distance measurement increases, the mobile, self-driving device is at a precipice or has been lifted by the user. However, if the measurement decreases by a certain value, this is a threshold.

[0034] In addition to the cliff sensors built into the device, other installation options are available that enable reliable threshold detection. Elevated and / or angled sensor positions ensure threshold detection before the device touches it. Forward-facing positions, particularly spring-loaded retractable positions, enable detection at close range.

[0035] Furthermore, the temporal change in sensor values ​​can be recorded to distinguish between flat obstacles, such as the ground-level struts of rocking chairs, flat table legs, or similar, and carpets. For smooth obstacles, a continuous profile shape can be detected over time, whereas a rough structure on carpets produces irregular sensor values.

[0036] Alternatively, the obstacle can be detected by a second bumper at the same height as the obstacle on the ramp. The spring force is selected so that a soft obstacle such as a carpet can be recognized as such and distinguished from an obstacle such as a chair rail.

[0037] Using the existing map data of the rooms in the surrounding area map, a threshold is reliably recognized as such, allowing the device to check and classify whether it is a door threshold or an obstacle within the room. The device passes through door thresholds and avoids other obstacles.

[0038] The invention is explained in more detail with reference to the following embodiments, which are merely examples. They show:

[0039] Figure 1: a schematic view of an embodiment of a

[0040] Environmental map created when carrying out the method according to the invention for the automatic processing of ground surfaces with the aid of a mobile, self-propelled device,

[0041] Figure 2: a schematic section of the surrounding area map of the

[0042] Embodiment of Figure 1,

[0043] Figures 3A-3C: a schematic cross-section of an embodiment of a mobile, self-propelled device during the implementation of the method according to the invention for the automatic processing of floor surfaces, and

[0044] Figure 4: a schematic flow diagram for the sequence of the method according to the invention for the automatic processing of floor surfaces. Figure 1 shows an environmental map 10 which was created by a mobile, self-propelled device, in particular a vacuum robot, during an exploratory drive. During the exploratory drive, all obstacles such as furniture, door thresholds, carpets and hanging curtains in a predetermined floor processing area are detected by a detection device of the vacuum robot. Based on the geometry in the environmental map, the vacuum robot can estimate for itself which areas correspond to rooms in reality. In particular, individual boundaries in the environmental map correspond to individual room sections and / or individual rooms 1a - 1i.

[0045] As an alternative to the robot vacuum cleaner automatically classifying the rooms, the user can use an app on their mobile device, for example, to specify which boundaries on the surrounding area map correspond to which rooms 1a - 1i in their apartment.

[0046] In addition to the rooms, any obstacles located within the rooms are detected by the robot vacuum. The robot vacuum classifies narrowed areas that lie between adjacent rooms in the environment map as doors and / or door areas and / or door thresholds and separates them from obstacles 3 that are located within a room. If the robot vacuum detects an obstacle 2, 3 in front of it while driving, it can compare the location of the obstacle 2, 3 based on its known position in the environment map 10. If this is in an area near a door or between two adjacent rooms, the obstacle 2 is classified as a door threshold that can be overcome or driven over. If the detected obstacle 3 is located within a room, it is classified as a piece of furniture. The robot vacuum drives around this piece of furniture when cleaning.An attempt to drive over or overcome obstacle 3 is not made in order to prevent damage and / or jamming of the vacuum robot.

[0047] In particular, the vacuum robot classifies obstacles 2, 3 as passable or impassable, depending on the position of the obstacle 2, 3 in the created environmental map. Obstacles 2 near the door are classified as door thresholds, and obstacles 3 far from the door are classified as furniture. As a result of the classification, obstacles 2 near the door are driven over and obstacles 3 far from the door are driven around before an attempt is made to drive over obstacles 3 far from the door. Following the classification of obstacles 2, 3, obstacles 2 classified as passable are driven over and obstacles 3 classified as impassable are driven around.

[0048] Figure 2 shows a section of the environment map 10 of the embodiment of Figure 1. The vacuum robot has recognized individual rooms 1c to 1g in the environment map and saved them accordingly. Between individual rooms 1d and 1e, 1c and 1d, the vacuum robot has detected obstacles 2 and classified them as door thresholds based on their position in the space between them. These obstacles 2 near the door or wall can be driven over during the vacuum robot's cleaning program without causing damage to the obstacle or the vacuum robot becoming stuck. Obstacles 3, which are located within a room due to their position, are classified as pieces of furniture. These obstacles 3 far from the door or wall are driven around during the vacuum robot's cleaning program without attempting to drive over them, so that damage to the obstacle 3 or the vacuum robot becoming stuck is prevented.

[0049] Figure 3 shows a side view of a vacuum robot 4 that detects an obstacle 2 as a door threshold and classifies it as such. In order to detect a door threshold before the vacuum robot 4 touches or drives onto it, the vacuum robot comprises at least one distance-measuring sensor 5, which is similar to known cliff sensors. If the distance measurement value increases, the vacuum robot 4 is located at a precipice or has been lifted by the user. If, on the other hand, the distance measurement value decreases by a certain amount within a defined range, a door threshold is recognized as such. This allows door thresholds to be detected before the vacuum robot physically touches them or before it reaches them.

[0050] In Figure 3A, the vacuum robot 4 is traveling in direction F and detects obstacles along its laser beam 6 with its distance-measuring sensor 5. Figure 3B shows an alternative arrangement of the distance-measuring sensor 5 in the vacuum robot 4. In Figure 3C, a forward and spring-loaded retractable arrangement of the sensor 5 on the vacuum robot 4 is used, which enables the detection of obstacles even at short distances. For this purpose, the sensor 5 is attached to a spring 7. The spring 7 can be retracted in direction K, so that the sensor 5 can be retracted into the vacuum robot, which protects it from impacts and damage when retracted.

[0051] In addition, a temporal change in the distance measurement values ​​of sensor 5 is preferably recorded to enable differentiation between smooth obstacles, such as the ground-level struts of rocking chairs or the flat feet of tables, and rough obstacles, such as carpets. Smooth obstacles result in a continuous profile shape over time, whereas a rough structure produces irregular sensor values.

[0052] Alternatively, the obstacle to be detected can be detected by a bumper at the height of the obstacle in the ramp (not shown). The spring force of the bumper is selected so that a soft obstacle such as a carpet can be recognized as such and distinguished from an obstacle such as a chair crossbar.

[0053] If a map of the surrounding area with recognized rooms is available and the robot vacuum detects a door threshold as such, the robot vacuum can check and classify whether it is a door threshold or an obstacle within the room. The robot vacuum will pass over door thresholds, while other obstacles will be avoided.

[0054] Figure 4 shows a flow chart for this. Initially, in method step 11, the robot vacuum cleaner carries out an exploratory drive in a designated floor processing area and creates a map of the surroundings. In method step 12, the robot vacuum cleaner now moves in its known surroundings, for example, to complete a cleaning task. The robot vacuum cleaner can access the map of the surroundings with recognized or specified rooms. In method step 13, the robot vacuum cleaner uses its sensors to detect an obstacle, for example a door threshold, in front of it. The robot vacuum cleaner uses its map of the surroundings to check whether the obstacle is in a doorway (step 14). If the obstacle is in an area between two rooms or in an area approved by the user (path 15a), the obstacle is classified as passable, for example as a doorstep. The robot vacuum cleaner attempts to overcome the obstacle in method step 16a.If, however, the obstacle is located within a room (path 15b), it is classified as impassable, for example, as a piece of furniture. The robot vacuum cleaner evaluates the obstacle as such and searches for a way around it without attempting to drive over it (step 16b). After driving over or around the detected and classified obstacle, the robot vacuum cleaner continues its movement and cleaning task in process step 17. Based on the position or location of the detected obstacle in the environment map, the obstacle is classified, and the robot vacuum cleaner's further course of action is decided accordingly. This significantly reduces the risk of the robot vacuum cleaner getting stuck on flat obstacles, such as ground-level chair struts. It also advantageously reduces the risk of damaging high-quality furniture by driving over or attempting to drive over it.Wear and tear on the robot vacuum cleaner itself is also reduced due to less direct contact with obstacles. This results in lower noise levels and a lower risk of getting stuck during cleaning. Overall, cleaning time can be reduced, as attempts by the robot vacuum cleaner to overcome obstacles are prevented from the outset.

Claims

PATENT CLAIMS 1. Method for autonomously processing floor surfaces using a mobile, self-propelled device, in particular a floor cleaning device such as a vacuum robot (4) and / or sweeping and / or mopping robot, comprising the following process steps: Conducting an exploratory drive of the mobile, self-propelled device in a designated soil cultivation area to create an environment map (10), Detecting obstacles (2, 3) using a detection device, classifying the obstacles (2, 3) as passable or impassable, and driving over an obstacle (2) classified as passable and / or driving around an obstacle (3) classified as impassable.

2. Method according to claim 1, wherein the classification of the obstacles (2, 3) is carried out on the basis of existing map data obtained through the exploration drive.

3. Method according to claim 2, wherein the classification is carried out by comparing information from the exploration drive and information from the detection of the obstacle (2, 3).

4. Method according to one of the preceding claims, wherein the classification of the obstacles (2, 3) takes place before an attempt to drive over the obstacle (2, 3).

5. Method according to one of the preceding claims, wherein the mobile, self-driving device for classification automatically identifies spaces (1a — 1i) as such based on information from its exploration drive.

6. Method according to one of the preceding claims, wherein the mobile, self-driving device determines a position of detected obstacles (2, 3) in the environment map (10).

7. Method according to claim 6, wherein obstacles near the door are classified as door thresholds and obstacles far from the door are classified as furniture.

8. Method according to claim 7, wherein obstacles near the door are driven over and obstacles farther from the door are driven around before an attempt is made to drive over the obstacles farther from the door. Obstacles are encountered.

9. Mobile, self-driving device, in particular floor cleaning device for autonomous processing of floor surfaces such as a vacuum robot (4) and / or sweeping and / or mopping robot, which is suitable for carrying out a method according to one of the preceding claims, comprising a detection unit for detecting obstacles (2, 3), an evaluation unit for classifying the obstacles (2, 3) as passable or impassable.

10. Mobile, self-driving device according to claim 9, wherein the detection unit comprises sensors (5) that determine distance measurements and / or time changes of sensor values.