Methods for determining a movement path for a mobile device

Abstract

The invention relates to methods for determining a movement path for a mobile device (100), wherein the mobile device is to move along the movement path in an environment (120), comprising the steps of: providing (214) an environment map (212, 310) which comprises a processing region (302) of the environment, wherein the environment map is based on identical polygonal cells (312), wherein a dimension of the cells corresponds at least substantially to a dimension of the mobile device or of a component of the mobile device; determining (230), for at least one portion of the cells of an edge environment map (222, 320) based on the environment map, a respective one or more different largest polygonal cell regions, wherein the cell regions (328.1,328.2, 328.3) each comprise the relevant cell of the edge environment map as a corner cell (326) and extend over multiple cells of the environment map; determining (240) movement path sections (242) to be used within the scope of an optimization, on the basis of the cell regions and an orientation of movement path sections in the cell regions; determining (260) the movement path (262, 342) on the basis of the movement path sections to be used; and providing (270) the movement path (262, 342) for navigation of the mobile device.

Description

[0001] R.413993

[0002] - 1 -

[0003] Description

[0004] title

[0005] Method for determining a motion path for a mobile device

[0006] The present invention relates to a method for determining a movement path for a mobile device, in particular a vehicle or robot that moves at least partially automatically, especially a cleaning robot or a lawnmower robot, a computing unit and a computer program for carrying it out, and a mobile device.

[0007] Background of the invention

[0008] Mobile devices, such as semi-automated vehicles or robots, typically move within an environment, particularly a work area or environment to be processed, such as an apartment, garden, factory floor, or on the street, in the air, or in water. One of the fundamental challenges of such or any other mobile device is orientation—that is, knowing the nature of its surroundings, especially the location of obstacles or other objects, and its precise location. To achieve this, the mobile device can be equipped with various sensors, such as cameras, lidar sensors, or inertial sensors, which capture the environment and the device's movement in two or three dimensions. This enables the mobile device to move locally, detect obstacles in time, and navigate around them.

[0009] Disclosure of the invention

[0010] According to the invention, a method for determining a motion path for a mobile device, a computing unit and a computer program for its R.413993

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[0012] The implementation and a mobile device with the features of the independent patent claims are proposed. Advantageous embodiments are the subject of the dependent claims and the following description.

[0013] The invention relates generally to mobile devices that move, or at least can move, within an environment, for example, within a work area. Examples of such mobile devices (or mobile work equipment) include robots and / or drones and / or vehicles that move semi-automatically or (fully) automatically (on land, water, or in the air). Examples of robots include household robots such as cleaning robots (e.g., vacuuming and / or mopping robots), floor or street cleaning equipment, construction robots, or robotic lawnmowers, as well as other so-called service robots. Examples of vehicles that move at least partially automatically include passenger transport vehicles or goods transport vehicles (also known as industrial trucks, e.g., in warehouses), but also aircraft such as drones or watercraft.

[0014] Such a mobile device includes, in particular, a control unit and a drive unit for moving the device, enabling it to be moved within its environment, especially along a path. Navigation information can be determined based on this path, such as specific instructions on the direction the device should travel to follow it. These instructions can then be implemented by the control unit and the drive unit.

[0015] Furthermore, a mobile device can have one or more sensors that can detect its surroundings or information within them. As mentioned, these can be, for example, cameras, lidar sensors, or inertial sensors, which capture the environment and the movement of the mobile device, for example, in two or three dimensions.

[0016] For certain types of mobile devices, such as cleaning and lawnmower robots, it is desirable that they cover the entire R.413993 area during their movement.

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[0018] The goal is to cover an environment or an entire, predefined work area, or at least a specific part of it; this applies particularly to the so-called end effector of the mobile device, such as a cleaning brush, a suction opening, or a cutting blade. In other words, a cleaning robot should completely clean a predefined work area (e.g., an apartment, specifically the open floor areas) or another predefined area of ​​an environment, and a robotic lawnmower should completely mow a predefined work area (e.g., a lawn) or another predefined area of ​​an environment. Ideally, the mobile device should operate in dynamic, i.e., constantly changing, environments and be able to find a strategic and optimized coverage path.

[0019] The problem of generating a path or movement path that covers the entire area accessible to the mobile device (coverage path) is known as "Coverage Path Planning" (CPP). A movement path covering an area means, in particular, that when the mobile device moves along the path, the entire area is covered by the device's end effector (although overlaps are permitted). Besides cleaning and lawnmower robots, this can also be used in other applications or robotic processes, such as area exploration, inspections, surveying, photographing or mapping entire environments, painting walls, and the like.

[0020] One challenge in this context is to determine the movement path in such a way that the mobile device, for example, has to make as few turns or turns as possible, or that the mobile device can move straight ahead as much as possible.

[0021] Against this background, a method is proposed for determining a motion path for a mobile device, whereby the mobile device is then to move along the motion path within an environment. This is specifically intended to mean that the motion path to be determined starts from the current position of the mobile device. The following is R.413993.

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[0023] The invention will be explained in particular using the example of a cleaning robot, especially a robotic vacuum cleaner, as a mobile device, although the principle can also be applied to other types of mobile devices, in particular mobile devices that are designed to perform processing, i.e., to carry out a work function. Besides cleaning, such processing or work functions could include, for example, mowing the lawn, surveying an environment, or taking pictures of the environment.

[0024] This involves providing an environment map that defines a workable area. The workable area is a portion of the environment that the mobile device is to process, for example, clean. This workable area can be a single, larger, contiguous area or several separate, smaller sub-areas that together constitute the workable area. The environment map can be provided, for example, as a single data set.

[0025] The processing area specifically includes only free areas in the surroundings that are accessible by the mobile device. These could be, for example, open spaces within a home. A further distinction can be made between different types of free areas, such as a free area that is more than a predetermined distance from an obstacle (especially a fixed or stationary one), and a free area that is at most the predetermined distance from such an obstacle. The predetermined distance is preferably determined by the mobile device or its geometry, in particular by the position of an end effector of the mobile device, which is configured to perform the processing by the mobile device, relative to the mobile device. This allows for the definition of free areas that extend precisely along or adjacent to an obstacle such as a wall. The predetermined distance can, for example,correspond to the width of the mobile device, but possibly taking into account the position of the end effector (which may, for example, protrude or not cover the entire width of the mobile device). R.413993.

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[0027] In this case, particular attention should be paid to free areas that are at most the specified distance from such an obstacle, i.e., covered by the processing area.

[0028] The environment map is based on identical polygonal cells, where a cell dimension corresponds at least substantially to a dimension of the mobile device or a component of the mobile device, in particular a dimension of an end effector of the mobile device. A concrete example of such cells are square cells. Then, an edge length of the cells (as a cell dimension) can correspond at least substantially to a width of the end effector (as a dimension of a component of the mobile device, specifically the end effector). In other words, a cell then covers the width of the end effector (typically in the direction perpendicular to the direction of movement of the mobile device).

[0029] The invention will now be explained using square cells as an example, since this is particularly suitable for rooms whose floor plan is typically based on right angles. However, other polygonal cells are also possible, e.g., rectangular or general square cells, hexagonal cells, octagonal cells, or even rhomboid or parallelogram-shaped cells. The hexagonal and octagonal cells can, in particular, be uniformly shaped, i.e., based on regular hexagons or...

[0030] Octagons are the basis. It is also generally conceivable that different shapes are used, e.g., octagonal cells and square cells.

[0031] In one embodiment, an initial environment map is provided that encompasses the processing area of ​​the environment. The environment map is then determined based on the initial environment map, with the environment map having a lower resolution compared to the initial environment map. In other words, the initial environment map is scaled down.

[0032] It is specifically intended that the environment map is such that the editing area is scaled down "conservatively," meaning that parts or areas of the editing area are always scaled to the next larger number of cells, but not to the next smaller number. An area that is very R.413993

[0033] - 6 - narrow, does not disappear when scaling down, but is exaggerated, i.e. displayed with the width of a cell.

[0034] The initial environmental map may have been obtained or determined using SLAM. SLAM (Simultaneous Localization and Mapping) is a robotics technique that allows a mobile device, such as a robot, to simultaneously create a map of its environment and estimate its spatial position within that map. This enables obstacle detection and thus supports autonomous navigation.

[0035] In SLAM, there are various approaches to representing maps and positions. Conventional SLAM methods generally rely on geometric information such as nodes and edges. Nodes and edges are typically components of the SLAM graph. The nodes and edges in the SLAM graph can be structured in various ways; traditionally, the nodes correspond, for example, to the pose (position and orientation) of the mobile device or specific environmental features at particular times, while the edges represent relative measurements between the mobile device and the environmental feature. SLAM graphs are described in more detail, for example, in "Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Wolfram Burgard, A Tutorial on Graph-Based SLAM, IEEE Intelligent Transportation Systems Magazine, Vol. 2(4), pp. 31-42, 2010".

[0036] Based on such a SLAM graph, a map of the environment (environmental map, or in this case, the initial environmental map) in which the mobile device moves can be determined or determined. With each new data set containing information about the environment, obtained from or based on a sensor of the mobile device, the map (or the SLAM graph) can be expanded or updated.

[0037] Then, for at least some of the cells of a boundary-surrounding map based on the surrounding area map, one or more different, in particular up to three, largest (this is to be understood in terms of area and thus e.g. number of cells) polygonal cell areas R.413993 are used.

[0038] - 7 - determined, wherein the cell ranges each encompass the relevant cell of the boundary environment map as a corner cell and extend over several cells of the environment map. The boundary environment map can, in particular, be determined or have been determined based on the environment map, wherein the boundary environment map only contains cells that lie at an edge of the processing area; in other words, it can be the outermost row of cells of the environment map.

[0039] A cell range thus encompasses several, especially contiguous, cells. In the case of square cells, the cell ranges will have a rectangular shape; in the case of other cell shapes, the cell range can also have a different shape. To find such a cell range, one can, for example, check for a cell in the boundary map how large the cell range can become when cells are considered in a specific direction. Then, starting from the cell in question, another direction can be considered. Examples of this are shown in the figures. In principle, such a polygonal or rectangular cell range can be only one cell wide (on its narrow side), but it is practical for it to be at least two cells wide.

[0040] Furthermore, as part of an optimization, motion path segments to be used are determined based on the cell areas and an orientation of motion path segments in the cell areas.

[0041] Based on the motion path segments to be used, the motion path is then determined, which is then provided for navigation of the mobile device. This can be solved, for example, within the framework of a so-called traveling salesman problem or general traveling salesman problem.

[0042] In this way, an easy-to-implement, computationally efficient method for determining a motion path is provided, especially with at least the same or higher quality than other methods. In particular, it can also be used to optimize for aspects other than the length of the motion path segments, e.g., minimizing the number of R.413993.

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[0044] Motion path segments below a certain length threshold can be useful for specific applications. Computing time is particularly important for online operation of the mobile device, where it is crucial to quickly replan in response to dynamic changes in the environment.

[0045] In one embodiment, the motion path segments to be used are determined in or based on the initial environment map, i.e., on the higher-resolution map; this allows for a more accurate determination of the motion path.

[0046] In one embodiment, determining the motion path segments to be used comprises assigning at least a portion of the cells of the environment map to one of several sets based on the cell areas. Each of the several sets is provided for motion path segments, in particular straight motion path segments, in one of several different directions (Oe set a direction). In the case of rectangular cell areas and square cells, for example, a first set and a second set can be provided. Then, for example, the first set is provided for first motion path segments, in particular straight motion path segments, in a first direction, and the second set is provided for second motion path segments, in particular straight motion path segments, in a second direction, the second direction being perpendicular to the first direction. For example,This can refer to a horizontal and a vertical direction (viewed in an image). Other cell or cell area shapes can have more than two sets or directions.

[0047] The movement path segments to be used are then determined accordingly or based on the assignments of the cells of the environment map to the respective set. This allows for efficient optimization.

[0048] In one embodiment, the allocation, based on the cell ranges, of at least the part of the cells of the environment map to the respective set comprises that each of at least the part of the cells to a R.413993

[0049] - 9 - which is assigned to several sets. It is then checked, for at least some of at least that part of the cells, whether a change in the assignment (i.e., if they were assigned to a different set) leads to a reduction in the total number of movement path segments. In other words, it can be checked, for example, whether changes in the assignment result in fewer but longer movement path segments; this would lead to an optimized movement path.

[0050] A computing unit according to the invention, e.g. a control unit or a control unit of a mobile device, or a server or other computer, is, in particular in terms of programming, equipped to carry out a method according to the invention.

[0051] The invention also relates to a mobile device configured to receive navigation information determined as described above, or which includes a computing unit according to the invention. The mobile device preferably also includes a drive system and a control unit for actuation according to the control information. The mobile device is preferably configured to perform processing; in particular, the mobile device may be one as described above, such as a cleaning robot or a robotic lawnmower.

[0052] Implementing a method according to the invention in the form of a computer program or computer program product with program code for carrying out all method steps is also advantageous, as this incurs particularly low costs, especially if an executing control unit is already used for other tasks and is therefore already available. Finally, a machine-readable storage medium is provided with a computer program stored on it as described above. Suitable storage media or data carriers for providing the computer program are, in particular, magnetic, optical, and electrical storage media, such as hard drives, flash memory, EEPROMs, DVDs, etc. Downloading a program via computer networks (Internet, intranet, etc.) is also possible. Such a download can be R.413993

[0053] - 10 - whether wired or wireless (e.g. via a WLAN network, a 3G, 4G, 5G or 6G connection, etc.).

[0054] Further advantages and embodiments of the invention will become apparent from the description and the accompanying drawing.

[0055] The invention is schematically illustrated in the drawing using an exemplary embodiment and is described below with reference to the drawing.

[0056] Brief description of the drawings

[0057] Figure 1 schematically shows a mobile device in an environment to illustrate the invention.

[0058] Figure 2 schematically shows a process flow in one embodiment.

[0059] Figures 3a, 3b, 3c, 3d, 3e, 3f, 3g show various cards to illustrate the invention in one embodiment.

[0060] embodiment(s) of the invention

[0061] Figure 1 schematically and by way of example shows a mobile device 100 in an environment 120, in particular a work area, to illustrate the invention. The mobile device 100 is, by way of example, a cleaning robot with a control unit 102 and a drive unit 104 (with wheels) for moving the cleaning robot 100, e.g., along a movement path 130. Furthermore, the vacuum cleaner robot 100 has, by way of example, a sensor 106 designed as a lidar sensor with a detection range. For better illustration, the detection range is chosen to be relatively small here; in practice, however, the detection range can also be up to 360° (e.g., but at least 180° or at least 270°). R.413993

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[0063] Furthermore, the cleaning robot 100 has a computing unit or data acquisition system 108, e.g., a control unit, by means of which data can be exchanged with a higher-level system 112, e.g., via a radio connection. In system 112, movement paths (or navigation information in general) can be determined, which are then transmitted to system 108 in the cleaning robot 100, which the robot is then to follow. However, it is also possible for a movement path (or navigation information in general) to be determined within system 108 itself or otherwise received there. Instead of a movement path, system 108 can also receive control information, e.g., determined based on a movement path, according to which the control unit 102 can move the cleaning robot 100 via the drive unit 104 to follow a movement path. The movement path 130 is only indicated here as an example.

[0064] The cleaning robot 100 also has an end effector 110, designed here, for example, as a suction or cleaning opening, by means of which processing (here, cleaning) can be carried out. The end effector 110 has a width 111 (viewed perpendicular to the direction of travel). The cleaning robot 100 is intended to move or navigate autonomously in the environment 120 and thereby clean, for example, a floor (e.g., vacuuming and / or mopping, or wet / damp cleaning). Furthermore, several different objects or obstacles are shown as examples in the environment, namely a wall 140 and a cabinet 142.

[0065] Figure 2 schematically illustrates the sequence of a process in one embodiment. This sequence will be explained below with reference to the maps shown in Figures 3a to 3g.

[0066] The method is used to determine a motion path for a mobile device that is to move along the motion path in an environment. This will be explained using the example of a cleaning robot, such as the cleaning robot 100 described in Figure 1. R.413993

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[0068] In step 200, an initial environment map 202 is provided. The initial environment map 202 may, for example, have been obtained using SLAM, as explained above, and it encompasses the processing area of ​​the environment.

[0069] Such an initial environment map is labeled 300 in Figure 3a. Figure 302 shows a processing area that will be processed subsequently. Figure 304 shows an area that is, for example, inaccessible to the mobile device, unreachable by the end effector, or can be processed, for example, as part of so-called edge processing (when the mobile device moves along edges such as walls). [Note: Figure 3a is simplified here.]

[0070] Figure 3b shows the initial environment map 300 again, but here only with the processing area 302, which will be examined in more detail below.

[0071] In step 210, an environment map 212 is determined based on the initial environment map 202 and then provided in step 214. Compared to the initial environment map 202, environment map 212 has a lower resolution. The environment map is based on identical cells, which in this example are square cells. The side length of each cell—which can also be a single pixel—corresponds, at least essentially, to the width of the end effector, as shown in Figure 1.

[0072] Such an environment map is labelled 310 in Figure 3c, with one cell labelled 312 as an example. Although the individual cells may not be particularly easy to identify here, it is nevertheless clear that the resolution of environment map 310 is significantly lower than the resolution of the initial environment map 300.

[0073] In comparison to environment type 300 according to Figure 3b, it is particularly noticeable that the processing area is scaled down "conservatively," meaning that parts or areas of the processing area are always scaled to the next larger number of cells, but not to a next smaller number. R.413993

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[0075] A very narrow area does not disappear when scaling down, but is exaggerated, i.e., displayed with the width of a cell.

[0076] In step 220, based on the environment map 212, a boundary environment map 222 is determined. The boundary environment map only contains cells that are located at an edge of the processing area.

[0077] In Figure 3d, a boundary environment map is labeled 320. Cells located at the edge of the processing area are labeled 321.1. Other cells that are not at the edge—and thus belong to the environment map but are not solely part of the boundary environment map—are labeled 321.2. In this specific example, the boundary environment map could also be a so-called "Integral Orthogonal Polygon" (IOP) map.

[0078] In step 230, for at least some of the cells of the boundary / surrounding map 222, one or more different, in particular up to three, largest cell ranges, here exemplified as rectangular, are determined. Each cell range includes the relevant cell of the boundary / surrounding map as a corner cell and extends over several cells of the surrounding map. A corner cell is understood to mean, in particular, that the relevant cell is located at a corner of the cell range – which, of course, comprises a number of cells.

[0079] Such corner cells are marked with dots in Figure 3d, one of which is labeled 326. Figure 3d also shows cell ranges, here as rectangular cell ranges, three of which are labeled 328.1, 328.2, and 328.3 for corner cell 326. These one, two, three, or more cell ranges per cell can and will overlap. Multiple cell ranges can be determined for each cell in the boundary environment map. This can also lead to some cell ranges occurring multiple times, having been determined as corner cells from different cells. Such duplicates (or more than two) can then be removed, leaving only one instance per cell range. Furthermore, all cell ranges thus obtained can be R.413993

[0080] - 14 - sorted in descending order by size (e.g. by area, i.e. by the number of cells it contains).

[0081] In step 240, motion path segments 242 to be used are determined within the framework of an optimization and based on the cell areas and an alignment of motion path segments in the cell areas.

[0082] This can include, in step 250, assigning at least some of the cells of the environment map to one of several sets based on the cell ranges. Each of these sets is designated for movement path segments, particularly straight movement path segments, in one of several distinct directions.

[0083] Here, in step 252, each of at least some of the cells can be assigned to one of several sets. In step 254, it is then checked, for at least some of at least some of the cells, whether a change in the assignment leads to a reduction in the total number of movement path segments.

[0084] In step 256, the movement path segments to be used are then determined accordingly or based on the assignments of the cells of the environment map to the respective set.

[0085] Figure 3e shows the environment map 310 again, with different cells assigned to different sets. A first set 322 and a second set 324 are shown as examples. The first set 322 is intended for vertical movement path segments, while the second set 324 is intended for horizontal movement path segments. It should be noted that the directions "vertical" and "horizontal" here refer to the figure; in practice, these could be, for example, two perpendicular directions extending along walls, or similar configurations. R.413993

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[0087] Initially, all cells can be assigned to the first set, i.e., the vertical movement path segments, or to the second set. However, as can be seen in Figure 3e, it is more advantageous to assign some cells to the first set and some cells to the second set, i.e., the horizontal movement path segments.

[0088] This results in an overall (potentially significantly) smaller number of motion path segments to be used, which are then (at least on average) longer. This can be determined, for example, within the framework of an optimization, even with one iteration. The assignment of cells into, for example, horizontal and vertical motion segments is thus tested within the optimization process using the cell ranges (such as 328.1, 328.2, 328.3 for cell 326). In summary, it can be stated that the processing area 302 is approximated by overlapping cell ranges, and these cell ranges are the basic operators with which the processing area is divided into horizontal or vertical motion segments.

[0089] In step 260, the motion path 262 is then determined based on the motion path segments to be used. This is done in particular in the initial environment map, which has a higher resolution than the environment map.

[0090] This is shown in Figure 3f. The initial environment map 300 is shown there (as in Figure 3a). In addition, movement path segments to be used are now shown there – corresponding to the first and second sets according to Figure 3e; these include vertical movement path segments 332 and horizontal movement path segments 334 (or, corresponding to the directions of the sets).

[0091] These motion path segments can then be connected in a suitable way to obtain the motion path. For example, a (general) traveling salesman problem can be solved for this purpose. This is shown in Figure 3g. There, the initial environment map 300 is shown (as in Figure 3a or 3f). However, instead of (as in Figure 3f) the R.413993 to be used...

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[0093] Movement path segments, the continuous movement path can be seen here, labeled 342.

[0094] In step 280, the movement path is then provided for navigation of the mobile device; based on this, navigation information for the mobile device can be determined, for example, to move it.

Claims

R.413993 - 17 - Claims 1. Method for determining a motion path for a mobile device (100), in particular a vehicle or robot that moves at least partially automatically, especially a cleaning robot or a lawnmower robot, wherein the mobile device is to move along the motion path in an environment, comprising: Providing (214) an environment map (212, 310) comprising a processing area (302) of the environment, wherein the processing area is to be processed by the mobile device, wherein the environment map is based on similar polygonal, in particular square, cells (312), wherein a measure of the cells corresponds at least substantially to a measure of the mobile device or a component of the mobile device, in particular a measure (111) of an end effector (110) of the mobile device; Determine (230) for at least some of the cells of a boundary environment map (222, 320) based on the environment map, one or more different, in particular up to three, largest polygonal, in particular rectangular, cell areas, wherein the cell areas (328.1 , 328.2, 328.3) each include the relevant cell of the boundary environment map as a corner cell (326) and extend over several cells of the environment map; Determining (240) motion path segments (242) to be used in the context of an optimization, based on the cell areas and an alignment of motion path segments in the cell areas; Determining (260) the motion path (262, 342) based on the motion path segments to be used; and Providing (270) the movement path (262, 342) for navigation of the mobile device. R.413993 - 18 - 2. The method according to claim 1, further comprising: Providing (200) an initial environment map (202, 300), obtained in particular by means of SLAM, which covers the processing area of ​​the environment; and Determining (210) the environment map (212) based on the initial environment map (202), where the environment map has a lower resolution compared to the initial environment map.

3. Method according to claim 2, wherein the motion path segments to be used are determined in or based on the initial environment map (202, 300).

4. Method according to any of the preceding claims, wherein determining (240) the motion path segments (242) to be used comprises: Assigning (250), based on the cell areas, of at least a part of the cells of the environment map to one of several sets (322, 324), wherein each of the several sets is provided for movement path segments, in particular straight movement path segments, in one of several different directions. Determine (256) the movement path segments to be used, according to or based on the assignments of the cells of the environment map to the respective set.

5. The method of claim 4, wherein the allocation, based on the cell areas (250), of at least the part of the cells of the environment map to the respective set comprises: Assign (252) each of at least part of the cells to one of the several sets; and Check (254), for at least some of at least part of the cells, whether a change in the assignment leads to a reduction in the total number of movement path segments. R.413993 - 19 - 6. Method according to one of the preceding claims, wherein the boundary environment map (222, 320) is determined based on the environment map (212, 310), wherein the boundary environment map only has cells that are located at an edge of the processing area.

7. Method according to any of the foregoing claims, further comprising: Determining navigation information for the mobile device, based on the movement path.

8. System (108) for data processing, comprising means for carrying out the method according to any of the preceding claims.

9. Mobile device (100) comprising a system according to claim 8, and / or being configured to obtain navigation information determined according to a method according to claim 7, and being configured to navigate based on the navigation information, preferably further being configured to perform processing, preferably with a control or regulating unit and a drive unit for moving the mobile device according to the navigation information.

10. Mobile device according to claim 9, which is designed as a vehicle that moves at least partially automatically, in particular as a passenger transport vehicle or as a goods transport vehicle, and / or as a robot, in particular as a household robot, e.g. cleaning robot, floor or street cleaning device or lawn mowing robot, and / or as a drone.

11. Computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the process steps of a method according to any one of claims 1 to 7 when it is executed on the computer.

12. Computer-readable storage medium on which the computer program according to claim 11 is stored.

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