METHOD FOR AUTONOMOUS PROCESSING OF SOIL SURFACES

Description

[0001] The invention relates to a method for the autonomous treatment of floor surfaces using a mobile, self-propelled device, in particular a floor cleaning device such as a vacuuming and / or sweeping and / or mopping robot. The invention also relates to a mobile, self-propelled device with which such treatment can be carried out.

[0002] Mobile, self-driving devices, such as robotic vacuum cleaners, are designed to autonomously clean as much of the floor area as possible. However, obstacles like furniture, furnishings, small objects, or door thresholds prevent the robotic vacuum from reaching all areas of the floor. Well-known robotic vacuum cleaners attempt to overcome thresholds and obstacles if they lie in their path. Obstacles that are too large are usually detected by bumper sensors positioned in the direction of travel. Small obstacles can be driven over by the robotic vacuum cleaner. However, above a certain height, the robotic vacuum cleaner lacks the ability to push over the obstacle. After several attempts, the robotic vacuum cleaner registers that it cannot overcome the obstacle and subsequently tries to avoid the area, although it is possible that the obstacle has already been damaged.

[0003] Depending on its capabilities, a robot vacuum can overcome low thresholds by simply walking over them. For example, it can drive over door thresholds to continue cleaning. These are usually manageable with sufficient momentum. Other thresholds, such as ground-level crossbars on furniture like rocking chairs or the legs of clothes drying racks, are not accessible to the robot vacuum. It must navigate around these. Thresholds with a narrow profile pose the risk of the robot vacuum getting stuck after driving over them and being unable to free itself. Especially with furniture that has high-quality surfaces, such as chrome-plated, lacquered, or glass surfaces, the robot vacuum's attempts to free itself can result in damage to the surface.Furthermore, disruptive and unpleasant noises can occur when the robot vacuum cleaner attempts to free itself, which impairs the user's perception of quality.

[0004] Robotic vacuum cleaners that detect and classify obstacles in their environment are known, for example, from publications WO 2019 / 004742 A1, KR 2020 0094816 A and US 2020 / 125113 A1.

[0005] The object of the invention is to provide an effective, optimized and / or non-damaging method for the autonomous treatment of floor surfaces, in which, in particular, the most complete possible cleaning of the floor surface is ensured, while at the same time damage to obstacles by the mobile, self-driving device is avoided.

[0006] This problem is solved by a method for treating ground surfaces with the features of claim 1 and by a mobile, self-propelled device with the features of claim 9. Advantageous embodiments and further developments are the subject of the dependent claims.

[0007] According to the invention, a method for the autonomous processing of floor surfaces using a mobile, self-driving device, in particular a floor cleaning device such as a vacuuming and / or sweeping and / or mopping robot, comprises the following method steps: Conducting an exploratory drive of the mobile, self-propelled device in a designated soil cultivation area to create an environment map, detecting obstacles using a detection device, wherein the mobile, self-propelled device determines a position of detected obstacles in the environment map, classifying the obstacles as passable or impassable, and driving over an obstacle classified as passable and / or driving around an obstacle classified as impassable, wherein obstacles near doors are classified as door thresholds and obstacles farther from doors are classified as furniture.

[0008] The solution according to the invention is characterized by classifying detected obstacles based on their position in the environment map and classifying them as surmountable if they are located within a passageway. Other obstacles are classified as insurmountable and are avoided by the mobile, self-driving device before it attempts to drive over them. This advantageously prevents damage to these obstacles from the outset. The mobile, self-driving device thus assesses whether a detected obstacle is surmountable even before attempting to drive over it. In doing so, the mobile, self-driving device classifies the obstacles based on existing map data from the environment 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 around obstacles classified as impassable.

[0009] The solution according to the invention advantageously reduces the risk of the mobile, self-propelled device becoming stuck on flat obstacles such as floor-level chair supports. It also reduces the risk of damaging high-quality furniture. Furthermore, the reduced contact with the furniture minimizes wear and tear on the mobile, self-propelled device. Advantageously, the cleaning process is quieter, as the device no longer crashes into flat obstacles at maximum speed or drives onto them and gets stuck, with its 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 interrupting the cleaning process is also reduced. Cleaning time can also be shortened, as the device no longer wastes time attempting to overcome obstacles.

[0010] A mobile, self-propelled device is understood to be, in particular, a floor cleaning device, such as a cleaning or lawn mowing machine, which autonomously cleans floors or lawns, especially in the household. This includes, among other things, vacuuming and / or sweeping and / or mopping robots, such as robotic vacuum cleaners or robotic lawn mowers. These devices operate (cleaning or mowing) preferably with little or no user intervention. For example, the device moves autonomously within a predefined area to clean the floor according to a pre-programmed cleaning strategy.

[0011] An exploratory drive is understood to be, in particular, a reconnaissance trip suitable for exploring a soil area to be cultivated, looking for obstacles, spatial layout, and similar features. The aim of an exploratory drive is, in particular, to be able to assess and / or document the conditions of the soil cultivation area to be worked.

[0012] A floor treatment area is any spatial area intended for treatment, particularly cleaning. This can be, for example, a single (living) room or an entire apartment. It can also include only areas within a (living) room or apartment that are designated for cleaning.

[0013] Obstacles are understood to be any objects and / or items located in the soil processing area, for example lying there, that affect the processing by the mobile, self-propelled device, in particular hindering and / or disturbing it, such as thresholds, door thresholds, furniture, walls, curtains, carpets and the like.

[0014] Passable obstacles are understood to be, in particular, obstacles that the mobile, self-propelled device can drive over due to their low height without getting stuck or colliding with them. For example, door thresholds or carpets can be classified as passable obstacles.

[0015] Impassable obstacles are therefore understood to be obstacles that the mobile, self-driving device cannot cross due to their height, i.e., would crash into or get stuck on, thus interrupting the cleaning process.

[0016] After the exploration drive, the mobile, self-driving device knows its surroundings and can transmit this information to the user in the form of an environmental map, for example, in an app on a mobile device. The detected obstacles, both passable and impassable, are preferably displayed on the environmental map. Ideally, the detected obstacles are displayed according to their classification. For example, obstacles classified as passable are shown in a different color, shape, or similar way than those classified as impassable.

[0017] A site map is understood to be any map suitable for depicting the area surrounding the tillage zone, including all its obstacles. For example, the site map shows the tillage zone with its obstacles and walls in a sketchy manner.

[0018] The map of the environment, including obstacles, is preferably displayed in the app on a portable accessory. This serves primarily to visualize potential interactions for the user.

[0019] In the present context, an additional device is understood to mean in particular any device that is portable for a user, that is located outside the mobile, self-driving device, in particular separate from the mobile, self-driving device, and that is suitable for displaying, providing, transmitting and / or transferring data, such as a mobile phone, a smartphone, a tablet and / or a computer or laptop.

[0020] The portable accessory has an app installed, specifically a cleaning app, which facilitates communication between the mobile, self-driving device and the accessory and, in particular, enables a visualization of the cleaning area, i.e., the living space or area to be cleaned. The app preferably displays the area to be cleaned as a map and shows the user any obstacles.

[0021] A detection device is any device suitable for reliably detecting both passable and impassable obstacles. It is preferably laser-based, sensor-based, and / or camera-based.

[0022] Classification refers specifically to categorizing obstacles and / or objects as passable or impassable. Additional classifications can be made, such as between level and uneven obstacles, or similar categories.

[0023] In an advantageous embodiment, obstacles are classified using existing map data obtained during the exploration drive. Specifically, the detected obstacles are compared with information from the surrounding area map to classify or assess them accordingly. Preferably, the classification is performed by comparing information from the exploration drive with information obtained when the obstacle is detected. Particularly preferably, the mobile, self-propelled device automatically identifies spaces as such based on information from its exploration drive.

[0024] For example, the mobile, self-driving device has a map of its surroundings available for the exploration drive. Based on the geometry, the device can independently assess which areas correspond to rooms in reality. Narrowed areas located between adjacent rooms in the map are identified as doors or door thresholds and therefore classified as passable.

[0025] Alternatively, the user can use the app on the portable device to specify which areas or sections of the map correspond to which rooms in their home. Here, too, the device can automatically identify narrowed areas between adjacent rooms as doors or thresholds and classify them as passable.

[0026] In an advantageous embodiment, the obstacles are classified before any attempt is made to drive over them. Therefore, in the present case, no attempt is made to overcome obstacles classified as impassable, in order to prevent potential damage to the obstacles or the mobile, self-propelled device becoming stuck.

[0027] According to the invention, the mobile, self-driving device determines the position of detected obstacles on the environment map. In particular, the device determines as precisely as possible where the detected obstacle is located on the environment map. If the obstacle is located in an area near a door or a room transition, it is classified as a threshold, especially a door or room threshold, which can be overcome. If the detected obstacle is located within a room, it is assumed to be a piece of furniture. The mobile, self-driving device navigates around this piece of furniture during cleaning.

[0028] In particular, according to the invention, 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, wherein obstacles near the door are driven over and obstacles far from the door are driven around before an attempt is made to drive over the obstacles far from the door.

[0029] According to the invention, a mobile, self-driving device, in particular a floor cleaning device for the autonomous cleaning of floor surfaces such as a vacuuming 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 changes in sensor values ​​over time.

[0030] Any features, designs, embodiments and advantages relating to the method also apply in connection with the mobile, self-driving device according to the invention, and vice versa.

[0031] An evaluation unit is understood to mean, in particular, any device capable of classifying obstacles and / or objects as passable or impassable, especially based on the obstacle's position within its environment. A more detailed classification of the obstacles is not strictly necessary, but may be implemented.

[0032] To detect an obstacle before the mobile, self-driving device collides with it, sensors similar to those used in cliff sensors can be employed. If their distance reading increases, the mobile, self-driving device is either at the edge of a precipice or has been lifted by the user. Conversely, if the reading drops by a certain amount, it indicates a threshold.

[0033] In addition to using the cliff sensors integrated into the device, other installation methods are available that enable reliable threshold detection. Elevated positions and / or angled installation orientations of the sensors ensure that the threshold is detected before contact with the device. Positions positioned further forward, particularly those that can be spring-loaded and retracted, allow for detection at close range.

[0034] Furthermore, the change in sensor values ​​over time can be recorded to differentiate between flat obstacles, such as the ground-level supports of cantilever chairs, the flat legs of tables, or similar objects, and carpets. With smooth obstacles, a continuous profile shape can be detected in the sensor values ​​over time, whereas the rough structure of carpets produces irregular sensor values.

[0035] Alternatively, the obstacle can be detected by a second bumper positioned at the same height as the obstacle on the ramp. The spring force is chosen so that a soft obstacle like a carpet can be recognized as such and distinguished from an obstacle like a chair leg.

[0036] Using the available map data of the rooms in the surrounding area, a threshold is reliably detected as such, allowing the device to check and classify whether it is a door threshold or an obstacle located within the room. Door thresholds are traversed by the device, while other obstacles are avoided.

[0037] The invention is explained in more detail with reference to the following examples. These examples show: Figure 1: a schematic view of an embodiment of an environment map created when carrying out the inventive method for the automatic processing of ground surfaces using a mobile, self-propelled device; Figure 2: a schematic section of the environment map of the embodiment of the Figure 1 Figures 3A-3C: a schematic cross-section of an embodiment of a mobile, self-propelled device during the execution of the inventive method for the automatic processing of floor surfaces, and Figure 4: a schematic flowchart of the process of the inventive method for the automatic processing of floor surfaces.

[0038] In Figure 1Figure 10 shows an environment map created by a mobile, self-driving device, specifically a robotic vacuum cleaner, during an exploration run. During this exploration run, all obstacles, such as furniture, door thresholds, carpets, and hanging curtains, within a predetermined cleaning area are detected by the robotic vacuum cleaner's detection system. Based on the geometry of the environment map, the robotic vacuum cleaner can independently assess which areas correspond to actual rooms. Specifically, individual boundaries in the environment map correspond to individual room sections and / or individual rooms 1a - 1i.

[0039] As an alternative to the robot vacuum cleaner automatically classifying the rooms, the user can use an app, for example on their mobile device, to specify which boundaries of the environment map correspond to which rooms 1a - 1i of their apartment.

[0040] In addition to the rooms themselves, the robot vacuum detects any obstacles within them. It classifies narrowed areas between adjacent rooms on the map as doors, doorways, and / or thresholds, and distinguishes them from obstacles within a room. If the robot vacuum detects an obstacle (2, 3) ahead of it, it can use its known position on the map to determine its location. If the obstacle is near a door or between two adjacent rooms, it is classified as a threshold that the robot can cross. If the detected obstacle is within a room, it is classified as a piece of furniture, which the robot will then navigate around during cleaning.No attempt will be made to drive over or overcome obstacle 3 in order to prevent damage and / or the robot vacuum getting stuck.

[0041] Specifically, the robot vacuum classifies obstacles 2 and 3 as passable or impassable, depending on their position in the generated environment map. Obstacles 2 near the door are classified as door thresholds, and obstacles 3 further away are classified as furniture. As a result of this classification, obstacles 2 near the door are driven over, and obstacles 3 further away are avoided, before an attempt is made to drive over the obstacles 3 further away. Following the classification of obstacles 2 and 3, obstacles 2 classified as passable are driven over, and obstacles 3 classified as impassable are avoided.

[0042] Figure 2shows an excerpt from the environment map 10 of the exemplary embodiment of the Figure 1 The robot vacuum cleaner has identified individual rooms 1c to 1g in the environment map and saved them accordingly. Between individual rooms 1d and 1e, and 1c and 1d, the robot vacuum cleaner has detected obstacles 2 and classified them as door thresholds based on their position. These obstacles 2, located near doors or walls, can be driven over during the robot vacuum cleaner's cleaning program without causing damage to the obstacle or the robot vacuum cleaner getting stuck. Obstacles 3, located within a room, are classified as furniture. These obstacles 3, located farther from doors or walls, are avoided during the robot vacuum cleaner's cleaning program without being driven over, thus preventing damage to the obstacle 3 or the robot vacuum cleaner getting stuck.

[0043] In Figure 3Figure 4 shows a side view of a robotic vacuum cleaner that detects an obstacle 2 as a door threshold and classifies it as such. To detect a door threshold before the robotic vacuum cleaner 4 touches or drives onto it, it includes at least one distance-measuring sensor 5, similar to known cliff sensors. If the distance reading increases, the robotic vacuum cleaner 4 is at a precipice or has been lifted by the user. Conversely, if the distance reading 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 robotic vacuum cleaner physically touches or reaches them.

[0044] In Figure 3A The robotic vacuum cleaner 4 is traveling in direction F and detects obstacles along its laser beam 6 with its distance-measuring sensor 5. Figure 3Bshows an alternative arrangement of the distance-measuring sensor 5 in the robotic vacuum cleaner 4. Figure 3C A forward-mounted and spring-loaded retractable arrangement of the sensor 5 is used on the robotic vacuum cleaner 4, enabling obstacle detection even at close range. For this purpose, the sensor 5 is attached to a spring 7. The spring 7 can be retracted in the direction of K, allowing the sensor 5 to be retracted into the robotic vacuum cleaner, thus protecting it from impacts and damage when retracted.

[0045] Additionally, the sensor 5 preferably records changes over time in its distance measurements to differentiate between smooth obstacles, such as the ground-level struts of cantilever chairs or the flat legs of tables, and rough obstacles, such as carpets. Smooth obstacles produce a continuous profile over time in the sensor readings, whereas rough surfaces result in irregular sensor readings.

[0046] Alternatively, the obstacle to be detected can be detected by a bumper positioned at its height on the ramp (not shown). In this case, the bumper's spring force is chosen so that a soft obstacle, such as a carpet, is recognized as such and distinguishable from an obstacle such as a chair crossbar.

[0047] If a map of the environment with recognized rooms is available and a door threshold is detected as such by the robot vacuum, the robot vacuum can check and classify whether it is a door threshold or an obstacle within the room. The robot vacuum will cross door thresholds and navigate around other obstacles.

[0048] In Figure 4A flowchart is shown here. Initially, in process step 11, the robotic vacuum cleaner performs an exploratory run in a designated cleaning area and creates a map of its surroundings. In process step 12, the robotic vacuum cleaner moves within its known environment, for example, to complete a cleaning task. The robotic vacuum cleaner can access the map of recognized or specified rooms. In process step 13, the robotic vacuum cleaner uses its sensors to detect an obstacle, such as a door threshold, in front of it. Using its map, the robotic vacuum cleaner checks whether the obstacle is located in a doorway (step 14). If the obstacle is located in an area between two rooms or in an area authorized by the user (path 15a), it is classified as passable, for example, as a door threshold. In process step 16a, the robotic vacuum cleaner attempts to overcome the obstacle.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 recognizes the obstacle as such and seeks 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.

[0049] Based on the position or location of the detected obstacle on the environment map, the obstacle is classified, and the robot vacuum's subsequent course of action is determined accordingly. This significantly reduces the risk of the robot vacuum getting stuck on low obstacles such as chair legs. It also reduces the risk of damaging valuable furniture by driving over or attempting to drive over obstacles. Furthermore, the robot vacuum itself experiences less wear and tear due to less direct contact with obstacles. This results in quieter operation and a lower risk of the robot getting stuck during cleaning. Overall, cleaning time can be reduced because attempts by the robot vacuum to overcome obstacles are prevented from the outset.

Claims

1. Method for autonomously processing floor surfaces with the aid of a mobile, self-propelled appliance, in particular a floor cleaning appliance such as a suction robot (4) and / or a sweeping and / or mopping robot, comprising the following method steps: - performing an exploratory tour by the mobile, self-propelled appliance in a designated floor processing area to create a surroundings map (10), - detecting obstacles (2, 3) by means of a detection facility, wherein the mobile, self-propelled appliance determines a position of detected obstacles (2, 3) in the surroundings map (10), - classifying the obstacles (2, 3) as passable or not passable, and - driving over an obstacle (2) classified as passable and / or driving around an obstacle (3) classified as not passable, characterised in that - obstacles near to a door are classified as door thresholds and obstacles far from a door are classified as furniture.

2. Method according to claim 1, wherein the classification of the obstacles (2, 3) is performed with the aid of existing map data obtained by the exploratory tour.

3. Method according to claim 2, wherein the classification is performed by comparing information from the exploratory tour 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) is performed before an attempt is made to drive over the obstacle (2, 3).

5. Method according to one of the preceding claims, wherein in order to perform the classification the mobile, self-propelled appliance automatically identifies rooms (1a-1i) as such on the basis of information from its exploratory tour.

6. Method according to one of the preceding claims, wherein obstacles near to a door are driven over and obstacles far from a door are driven around before an attempt is made to drive over the obstacles far from a door.