阅读说明：本技术 借助自主式移动机器人进行的地板处理 (Floor treatment by means of autonomous mobile robot ) 是由 H·阿特斯 D·孔蒂 C·弗罗伊登塔勒 D·西塔勒 R·福格尔 于 2018-11-16 设计创作，主要内容包括：实施例涉及一种控制自主式移动机器人的方法,包括以下步骤：在处理模式下对机器人进行控制,以通过机器人的地板处理模块处理地板面；借助设于机器人上的污物传感器,检测代表地板面的污染水平的污物传感器信号,以及基于所述污物传感器信号改变机器人的速度。(Embodiments relate to a method of controlling an autonomous mobile robot, comprising the steps of: controlling the robot in a treatment mode to treat the floor surface by a floor treatment module of the robot; the method comprises the steps of detecting a dirt sensor signal representing a level of contamination of the floor surface by means of a dirt sensor provided on the robot, and changing the speed of the robot on the basis of the dirt sensor signal.)

1. A method of controlling an autonomous mobile robot (100), comprising the steps of:

controlling the robot (100) in a treatment mode to treat the floor surface by means of a floor treatment module (160) of the robot (100),

by means of a dirt sensor (126) provided on the robot, a dirt sensor signal representing a level of contamination of the floor surface is detected, on the basis of which the speed of the robot (100) is changed during the treatment of the floor surface.

2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

wherein the dirt sensor signal is capable of assuming a first state and a second state depending on a level of contamination of the floor surface.

3. The method of claim 2, wherein the first and second light sources are selected from the group consisting of,

wherein a first state of the soil sensor signal indicates a normal level of contamination and a second state of the soil sensor signal indicates a severe level of contamination.

4. The method according to claim 2 or 3,

wherein the processing mode corresponds to a maximum speed of the robot, an

Wherein the maximum speed depends on the state of the dirt sensor signal.

5. The method of any one of claims 2 to 4,

wherein the speed of the robot is reduced from a first value to a second value in response to a second state of the dirt sensor signal.

6. The method of claim 5, wherein the first and second light sources are selected from the group consisting of,

wherein after the speed is reduced, the speed is restored to the first value according to at least one presettable criterion.

7. The method of claim 6, wherein the first and second light sources are selected from the group consisting of,

wherein the at least one pre-settable criterion comprises at least one of: the dirt sensor signal assumes the first state again, the dirt sensor signal assumes the first state again and a presettable time has elapsed since that point; the soil sensor signal assumes the first state again and the robot has traveled a defined distance from that moment; a settable time elapses since the speed is reduced, and the robot (100) travels a settable distance since the speed is reduced.

8. The method of any one of claims 2 to 7,

wherein the robot (100) stops in response to the dirt sensor signal switching from a first state to a second state.

9. The method of any one of claims 2 to 8,

wherein the robot (100) moves back in response to the dirt sensor signal switching from a first state to a second state.

10. The method of claim 9, wherein the first and second light sources are selected from the group consisting of,

wherein the robot (100) moves backwards along a straight line or a trajectory followed in time by a presettable distance and/or duration, thereby effecting the back movement.

11. The method according to claim 9 and 10,

in which obstacles are taken into account during the return movement so that no collision occurs.

12. The method according to any one of claims 9 to 11,

wherein the robot (100) stores information about the position of an obstacle in a map, and uses the information saved in the map to avoid a collision during a backward movement, and does not use current sensor information about the obstacle.

13. The method according to any one of claims 8 to 12,

wherein after stopping or after a backward movement the processing mode is continued at a reduced speed in the normal direction of travel.

14. The method of any one of claims 1 to 13,

wherein in a treatment mode the robot (100) moves within a range of the floor surface at a speed which is equal to or less than a maximum speed corresponding to the treatment mode, and wherein

-reducing the maximum speed, thereby changing the speed of the robot (100).

15. The method of any of claims 1 to 14, wherein the controlling of the robot in processing mode comprises:

implementing path planning based on the map information and the position of the robot according to the motion pattern corresponding to the processing mode, the obstacle avoidance strategy corresponding to the processing mode and the strategy corresponding to the processing mode for performing subsequent processing on the unprocessed surface;

the planned path is converted into a drive command.

16. A method of controlling an autonomous mobile robot (100), comprising the steps of:

controlling the robot (100) to process the floor surface in a processing mode by a processing module (160) of the robot (100),

creating a treatment map in which treated areas of the floor surface are marked, and the treatment map is used to determine areas that still need treatment,

detecting a dirt sensor signal representing a level of contamination of the floor surface by means of a dirt sensor (126) provided on the robot,

in response to the dirt sensor signal, marking or not marking the area (D) as "processed" during processing of the floor surface based on the current robot position in the processing map.

17. The method of claim 16, wherein the first and second light sources are selected from the group consisting of,

wherein the dirt sensor signal is capable of assuming a first state and a second state depending on a level of contamination of the floor surface, an

Wherein a first state of the soil sensor signal indicates a normal level of contamination and a second state of the soil sensor signal indicates a severe level of contamination.

18. The method according to claim 16 or 17, wherein in response to the soil sensor signal, marking the area according to the current robot pose in the process map comprises:

in case the soil sensor signal shows a severe contamination level, the zone (D) associated with the current robot position is prevented from being marked as "processed" whether or not the zone has been processed.

19. Method according to any of claims 16-18, wherein an area (D) is marked as "processed" only if it is actually processed in relation to the current robot position and the dirt sensor signal shows a normal contamination level.

20. Method according to any one of claims 16 to 19, wherein in case the area (D) has been actually treated and the dirt sensor signal shows a severe contamination level, the area (D) is marked as "untreated", or "to be subsequently treated".

21. The method of any one of claims 16 to 20,

wherein the area (D) associated with the current robot position encloses the current position of the robot.

22. A method of controlling an autonomous mobile robot (100), comprising the steps of:

in a treatment mode, controlling the robot to treat the floor surface,

during processing of the floor surface, an intensity map is created in which different locations or areas on the floor surface specify a measure of the processing intensity of the floor surface.

23. The method of claim 22, wherein the first and second portions are selected from the group consisting of,

wherein the measure of the treatment strength of the floor surface depends on at least one of: duration of treatment, frequency of treatment, speed in process, cleaning efficiency during treatment, product of treatment duration and treatment efficiency.

24. The method according to claim 22 or 23,

wherein during treatment of the floor surface, the treatment intensity is controlled based on one of the following characteristics: the signal provided by the dirt sensor showing the level of pollution of the floor surface, the user instruction based on information about the position of the obstacle in the surroundings of the robot.

25. The method of any one of claims 22 to 24,

wherein during processing of the floor surface the robot (100) navigates according to a map of the robot application area and wherein the intensity map and/or information based on the intensity map is stored together with the map of the robot application area.

26. The method of any one of claims 22 to 25,

wherein a recommendation to perform more frequent and/or more intensive processing of the room and/or area is made to the user through the human machine interface (200) based on the intensity map.

27. A method of controlling an autonomous mobile robot (100), comprising the steps of:

controlling the robot (100) in a treatment mode to treat the floor surface by means of a floor treatment module (160) of the robot (100),

a dirt sensor signal representing a level of contamination of the floor surface is detected by means of a dirt sensor (126) provided on the robot, in response to which dirt sensor signal a treatment mode is adjusted during treatment of the floor surface.

28. The method of claim 27, wherein the adjustment of the processing mode comprises:

during the treatment of the floor surface, the speed and/or direction of travel of the robot is changed depending on the dirt sensor signal.

29. An autonomous mobile robot (100) comprising a control unit (150), the control unit (150) being adapted to cause the robot to carry out the method according to any of the preceding claims.

Technical Field

The present description relates to the field of autonomous mobile robots for treating surfaces.

Background

In recent years, autonomous mobile robots, for example, are increasingly being used to treat (particularly clean) floor surfaces. It is important that a certain face is treated entirely by means of a face treatment device (e.g. a brush) attached to the robot. Simple devices work without the need to create and use a map of the robot application area, in such a way that the device moves, for example, randomly within the range of the surface to be cleaned. More sophisticated robots use maps of the robot application area that are created for the robot itself and provided in electronic form. With these systems it is possible to remember which faces have been processed.

Modern autonomous mobile robots that use maps of the robot application range for navigation may attempt to use processing modes with as systematic a motion or processing pattern as possible during the processing (e.g. cleaning) of a surface. This movement or treatment pattern has to be adapted to the complex surroundings in the robot application area, e.g. a house with furniture. Furthermore, the robot needs to be able to respond to unexpected situations, such as a person moving in the robot's application area, an obstacle that is difficult to detect by the robot's sensors, or an area that is extremely contaminated.

The specific path plan (trajectory plan) depends, among other things, on the processing pattern and on the collision avoidance strategy used in the respective processing mode. The manner in which unprocessed partitions are handled may likewise depend on the processing mode. That is, the processing mode for processing the floor surface is mainly characterized in that: the movement pattern (e.g. meander, spiral, etc.) used by the robot when trying to cover the floor surface, the speed of travel, the collision avoidance strategy used, and the strategy for subsequent treatment of unprocessed sections of the floor surface (e.g. left over by obstacles). For example, various solutions (treatment modes) are described in the publication DE 102015119865 a1 for robot-assisted floor treatment.

In summary, it is an object of the present invention to improve the existing robot-implemented methods with respect to autonomous mobile robots for treating surfaces, for example for cleaning floor surfaces, thereby increasing the efficiency of the robot.

Disclosure of Invention

The solution of the invention to achieve the above object is a method according to any of claims 1, 16 and 20, and an autonomous mobile robot according to claim 25. Embodiments and derivations are subject matter of the dependent claims.

An exemplary embodiment relates to a method of controlling an autonomous mobile robot, comprising the steps of: controlling the robot in a treatment mode to treat the floor surface by a floor treatment module of the robot; by means of a dirt sensor provided on the robot, a dirt sensor signal representing the level of contamination of the floor surface is detected and the speed of the robot is changed on the basis of the dirt sensor signal.

Another embodiment relates to a method comprising the steps of: controlling the robot in a treatment mode to treat the floor surface by means of a treatment module of the robot; and creating a treatment map marking areas of the floor surface that have been treated, and determining from the treatment map which areas further need to be treated. The method further comprises detecting a dirt sensor signal representing a level of contamination of the floor surface by means of a dirt sensor provided on the robot, and marking an area associated with the current robot position as "processed" or unmarked in the processing map on the basis of the dirt sensor signal.

Another embodiment relates to a method comprising the steps of: the robot is controlled in the processing mode to process the floor surface by means of a processing module of the robot and to create an intensity map in which a measure of the processing intensity of the floor surface is specified for each position or area on the floor surface.

It is also proposed an autonomous mobile robot comprising a control unit, wherein said control unit is adapted to cause the robot to carry out one or several of the methods described herein.

Drawings

Exemplary embodiments will be described in detail below with reference to the accompanying drawings. The illustrations are not necessarily to scale and the invention is not limited to the illustrated aspects. Rather, the drawings are intended to illustrate the basic principles. Wherein:

fig. 1 shows an autonomous mobile robot in the context of a robot application.

Fig. 2 illustrates an exemplary design of an autonomous mobile robot in conjunction with a block diagram.

Fig. 3 shows in connection with a block diagram the functional cooperation of the sensor unit of the autonomous mobile robot with the control software.

Fig. 4 shows a flow chart of an example of the method described herein.

Fig. 5A-5E illustrate exemplary motion patterns of autonomous mobile robots in the context of processing floor surfaces according to embodiments described herein.

Detailed Description

Fig. 1 shows an example of an autonomous mobile robot 100 for treating floor surfaces. Modern autonomous mobile robots 100 are based on map navigation, i.e. they access an electronic map of the robot application area. During the movement of the robot through the application range, the robot detects an obstacle. The obstacle may be an object such as furniture, a wall, a door, etc. The robot can also detect a human or an animal as an obstacle. In the example shown, the robot 100 has identified a portion of the walls W1 and W2 of the room. Methods for creating and updating maps, and for determining the position of the autonomous mobile robot 100 in the robot application area in respect of such maps are well known. For this purpose, for example, the SLAM method (Simultaneous localization and Mapping) can be used.

Fig. 2 shows an example of each unit (module) of the autonomous mobile robot 100 in conjunction with a block diagram. The unit or module may be a separate component or part of the software for controlling the robot. A cell may have several sub-cells. The software responsible for the behavior of the robot 100 may be executed by the control unit 150 of the robot 100. In the illustrated example, the control unit 150 comprises a processor 155 adapted to execute software instructions contained in a memory 156. Some of the functions of the control unit 150 may also be implemented at least in part by means of an external computer. This means that the computing power required by the control unit 150 can be at least partially transferred to an external computer, which can be reached, for example, via a home network or via the internet (cloud).

The autonomous mobile robot 100 comprises a drive unit 170, which may for example have motors, transmissions and wheels, so that the robot 100 is (at least theoretically) able to approach each point of the application range. The drive unit 170 is adapted to convert commands or signals received by the control unit 150 into movements of the robot 100.

The autonomous mobile robot 100 further comprises a communication unit 140 to establish a communication connection 145 to a Human Machine Interface (HMI)200 and/or other external devices 300. For example, the communication connection 145 may be a direct wireless connection (e.g., bluetooth), a local wireless network connection (e.g., WLAN or ZigBee), or an internet connection (e.g., to a cloud service). For example, the human-machine interface 200 can output information (e.g., a battery state, a current work task, map information such as a cleaning map, etc.) about the autonomous mobile robot 100 to a user in a visual or audible form, and accept a user command for the work task of the autonomous mobile robot 100. Examples of HMI 200 are tablet PCs, smart phones, smart watches, and other wearable devices, computers, smart televisions or head mounted displays, and the like. Additionally or alternatively, the HMI 200 may be integrated directly in the robot such that the robot 100 may operate through keys, gestures, and/or voice input and output, for example.

Examples of the external device 300 are a computer and a server that perform calculations and/or store data, an external sensor that provides additional information, or other home appliances (e.g., other autonomous mobile robots) that can cooperate and/or exchange information with the autonomous mobile robot 100.

The autonomous mobile robot 100 may have a work unit 160, in particular a processing unit for processing (e.g. cleaning) a floor surface. Such a treatment unit may for example comprise a suction unit, a brush or other cleaning means generating an air flow for receiving the dirt. Alternatively or additionally, the robot may be adapted to apply the cleaning liquid to the floor surface and to perform the treatment.

The autonomous mobile robot 100 comprises a sensor unit 120 with various sensors, for example one or several for acquiring information about the surroundings (environment) of the robot in the application area of the robot, such as the position and extension of obstacles and other landmarks (landworks) in the application area. Sensors for collecting information about the surroundings are, for example, sensors for measuring the distance to objects (e.g. walls or other obstacles etc.) in the surroundings of the robot. For this purpose, various sensors are known, such as optical and/or acoustic sensors, which can measure distances by means of triangulation or a time of flight measurement of the emitted signal (triangulation sensors, 3D cameras, laser scanners, ultrasonic sensors, etc.). Alternatively or additionally, a camera may be used to collect information about the surroundings. In particular, when the object is observed from two or more positions, the position and extension of the object (obstacle) can be determined.

Furthermore, the robot may have sensors for detecting a (usually accidental) contact (or collision) with an obstacle. This may be by means of acceleration sensors (which for example detect changes in the speed of the robot in the event of a collision), contact switches, capacitive sensors or other tactile or touch-sensitive sensors. Furthermore, the robot may have a floor sensor (also called a fall sensor or a fall sensor) to identify edges in the floor, such as steps. Other common sensors in the field of autonomous mobile robots are sensors for determining the speed and/or the travel covered by the robot, such as odometers or inertial sensors (acceleration sensors, rotation rate sensors) for determining changes in the position and in the movement of the robot, and wheel contact switches for detecting the contact of the wheels with the floor.

Furthermore, the robot may have a sensor for detecting a contamination level of the floor surface. Such sensors are referred to herein as soil sensors. Such sensors are for example able to detect dirt received by the robot during cleaning. For example, the suction robot has a passage through which sucked air with dirt (e.g. dust) is guided from the floor to the dirt-receiving container. A dirt sensor (dirt sensor) can, for example, provide a measurement value representing the amount of dirt entrained in the air stream flowing through the passage. Another type of dirt sensor is capable of detecting vibrations and oscillations of heavy dirt particles (for example by means of a piezoelectric sensor), for example. Alternatively or additionally, the amount of dirt in the air flow may be detected optically. The contamination level can be determined directly in the imaging of the camera, for example. Another solution is a combination of one or several light sources and one or several light sensitive receivers. The dirt particles contained in the air stream scatter the emitted light according to their number and size, so that the intensity of the light detected by the receiver varies. Another approach is to detect the contamination directly on the floor surface. The contamination level may be detected directly by the camera, for example. Alternatively or additionally, the floor surface may be illuminated by means of a light source and the contamination level detected on the basis of the characteristics of the reflected light. For example, contamination by liquids may be identified based on the conductivity of the floor surface. These and other sensors for detecting the level of pollution of the floor surface are well known and will not be discussed further here.

In a simple example, the measurement signal of the dirt sensor shows at least two states. Here, the first state indicates a no pollution or normal pollution level, and the second state indicates a severe pollution level (i.e. the measured pollution exceeds a threshold). The two states are distinguished, for example, on the basis of a threshold value of the detected dirt particles. In principle, it is also possible to distinguish between more than two states (e.g. "clean", "normal contamination", "severe contamination") so that the response of the robot to contamination can be divided more finely.

Autonomous mobile robot 100 may correspond to a base 110 on which the robot is able to charge its energy storage (battery), for example. The robot 100 may return to this base 110 after the task is completed. If the robot no longer needs to process the task, the robot may wait in the base 110 for a new application.

The control unit 150 may be adapted to provide all functions required by the robot so that the robot independently moves in its range of application and performs tasks. To this end, the control unit 150 comprises, for example, a processor 155 and a storage module 156 for executing software. The control unit 150 may generate control commands (e.g., control signals) for the working unit 160 and the driving unit 170 based on information obtained by the sensor unit 120 and the communication unit 140. The drive unit 170 is capable of converting these control signals or control commands into movements of the robot as described above. The software contained in memory 156 may also be modular. The navigation module 152 provides, for example, functions for automatically creating a map of the robot application range and for path planning of the robot 100. The control software module 151 provides, for example, general (global) control functions and can constitute an interface between the modules.

In order for the robot to autonomously complete a task (task), the control unit 150 may include functionality to navigate the robot within the robot's application scope, which is provided by the navigation module 152 described above. These functions are well known and may include mainly the following:

information about the surroundings is collected by means of the sensor unit 120, for example, but not only by means of the SLAM method, whereby an (electronic) map is created,

managing the mapping of one or several maps, with which one or several robot application areas are associated,

determining the position and orientation (collectively "pose") of the robot in the map based on the environmental information measured by the sensors of the sensor unit 120,

map-based path planning (trajectory planning) from the current pose (start point) of the robot to the target point,

a contour following mode in which the robot (100) moves along the contour of one or several obstacles (e.g. walls) with a substantially constant distance d from this contour,

partition identification, during which the map is analyzed and split into partitions, wherein, for example, the boundaries of spaces such as walls and door aisles are identified such that these partitions describe rooms of the house and/or reasonable partitions of these spaces.

For example, in case of a change in the surroundings of the robot (movement of obstacles, opening of doors, etc.), the control unit 150 can continuously update the map of the robot application range during the operation of the robot by means of the navigation module 152 and based on the information of the sensor unit 120.

In summary, an (electronic) map that can be used by the robot 100 is a map data set (e.g. a database) for storing information about the application area of the robot and the position of the surroundings associated with the robot in this application area. In this regard, "location-dependent" means that the stored information corresponds to each location or pose in the map. That is, the map data and map information are always based on a specific location or a specific area within the robot application range covered by the map. That is, maps represent a large number of data sets containing map data, and the map data may contain arbitrary location-related information. In this case, the position-related information can be stored in different levels of detail and abstraction, wherein the levels can be adapted to the specific function. In particular, the respective information can be stored redundantly. A collection of several maps that relate to the same area but are stored in different forms (data structures) is often referred to as a "map".

Based on the (stored) map data, the (currently measured) sensor data and the current task of the robot, the navigation module 152 can plan the path of the robot. At this point it may be sufficient to determine the waypoint (intermediate target point) and the target point. This plan can then be transmitted by the control software module 151 with specific drive commands (drive commands). The drive unit 170 is controlled based on these drive commands and the robot is thereby moved, for example, along waypoints (from waypoint to waypoint) to a target. It is to be noted that the planned path may here comprise a complete area, a treatment trajectory and/or a direct short movement section (e.g. a few centimeters in case of an obstacle circumvention).

Autonomous mobile robots may have various operating modes for controlling the robot. The mode of operation determines the (internal and external visible) behavior of the robot. For example, a robot navigating with a map may have an operational mode for building a new map. Another operating mode can be provided for the navigation of the target point, i.e. the robot navigates from one point (e.g. the base) to a second point (target point, e.g. the position where a task, in particular a cleaning task, starts). Other operating modes (i.e. treatment modes, in particular cleaning modes) can be provided for carrying out the intended tasks of the robot.

The robot may have one or several treatment modes for treating the floor surface. That is, to perform a specific (cleaning) task, the robot selects an operation mode according to certain criteria, in which the robot operates during the performance of the task. In the simplest example, the robot is assigned a processing mode to be selected by the user. Alternatively or additionally, a fixed sequence of processing modes may also be performed (e.g. investigation of (sub-) areas, (edge cleaning of) areas, (face cleaning of) areas). For example, in a strategy for covering a floor surface, the treatment modes may differ. For example, the robot may be controlled randomly or systematically. Random control strategies typically do not employ maps. Systematic walk strategies typically use a map (or a portion thereof) of the robot application scope, which may be built during processing, or may be known before processing begins (e.g., from a longer movement pattern or from past applications). A typical example of a systematic walking strategy for covering a floor surface is based on a motion pattern corresponding to each operating mode (also referred to as a treatment/cleaning pattern in the treatment/cleaning process). A commonly used motion pattern comprises motion along parallel jointed trajectories (meandering). Another motion pattern is motion along a spiral trajectory. Another motion profile may be a contour following a presettable area, wherein the contour may be composed of, for example, real and virtual obstacles to achieve a treatment close to a wall.

Additionally or alternatively, the treatment modes may be distinguished by the selection and use of the cleaning tool. For example, there may be an operation mode (carpet cleaning mode) using a suction unit having high suction efficiency and a brush rotating quickly. Furthermore, there may be another mode of operation (hard floor cleaning mode) in which a reduced suction efficiency and a slower brush rotation are used. Furthermore, the wiping unit can be used on hard floors. Furthermore, depending on the type of floor (e.g. stone, wood), cleaning liquids (e.g. water) may be applied to the floor or not.

The operating mode (cleaning mode) may be modified based on the value of the signal of a sensor (dirt sensor) for detecting the level of contamination of the floor surface. For example, a special operating mode (stain cleaning mode) can be activated at heavily soiled locations in order to eliminate local soiling. In the present example, for example, the operation mode with the meandering-meander-like movement pattern can be switched to the operation mode with the spiral-shaped movement pattern (stain cleaning mode). But the process mode switching is disturbing for systematic cleaning, since this increases the complexity of the methods used, in particular for navigation and trajectory planning. It is therefore desirable to take into account the identified severe contamination levels using a simpler method that is easily integrated into the systematic processing model. The path planning mode adopted by the robot may depend on the current operation mode.

The mode of operation for cleaning floor surfaces (cleaning mode) is therefore characterized primarily by a movement pattern (e.g. meander-like, spiral-like, etc.) with which the robot attempts to cover as completely as possible the floor surface of current interest (e.g. a specific room or a part thereof), using collision avoidance strategies (e.g. returning, bypassing obstacles, etc.), and strategies for subsequent treatment of untreated sections of the floor surface (e.g. left over by obstacles). For example, in the case where a motion pattern (e.g., a meandering trajectory) corresponding to the processing mode is implemented, the remaining region may be approximated and cleaned. Other solutions use a processing map or cleaning map (cleaning map), in which the already cleaned areas are marked for subsequent processing at a later point in time. The purpose of the exemplary embodiments described herein is primarily to avoid mode switching as much as possible during cleaning, especially in the case of detection of high contamination.

Responding to pollution by reducing speed-figure 3 shows how sensors for detecting the pollution level of the floor surface (dirt sensors) are integrated into the architecture of an autonomous mobile robot. In the navigation module 152, map data (e.g. the position of obstacles, etc.) and the position of the robot (e.g. according to the SLAM method, see also fig. 4, step S1 below) are updated based on information about the surroundings of the robot (provided by the navigation sensor 121 comprised in the sensor unit 120) and by means of a odometry method. For odometry, the sensor unit 120 of the robot 121 may have an odometer 122 (e.g. a wheel encoder, an optical odometer, etc.). The path plan of the robot is then updated according to the current operation mode (treatment mode, cleaning mode, etc.). Path planning is based on the motion pattern corresponding to the current operating mode, the collision avoidance strategy used in the respective operating mode (e.g., return, moving along the contour of an obstacle, etc.), and the strategy used to post-process the remaining partitions. The path plan may include, for example, determining waypoints to the target, defining path segments, motion vectors, and/or other elements describing the path of the robot through the robot application. For domestic robots (as opposed to large robots that move at high speeds, such as automobiles that move automatically), the dynamics of the robot (particularly the speed and acceleration) while traversing the path are often ignored in path planning. The updating of the path plan may also comprise checking whether the robot is still on the pre-planned path. In case of a deviation, in particular greater than a tolerance, it can be determined how the robot returns to the planned path. Furthermore, the path planning update may comprise checking whether the pre-planned path can be implemented without collision. For example, the process can avoid obstacles that were not present previously or obstacles that were not taken into account in the path planning. After the update of the path plan is completed, the first step (updating the map data and the robot position) may be repeated in the navigation module 152.

The path planning result of the navigation module 152 is forwarded to the control software module 151, which creates a drive command for the drive unit 170 according to a rule that can be preset. The drive unit 170 is formed, for example, by two independently driven wheels on one axle (differential drive). Such drivers and their control for following a path are well known. For example, a linear motion is generated in the case where both wheels are driven at the same speed. Rotation about a center point between the two wheels is achieved in the case where the two wheels rotate in opposite directions at the same speed in absolute value. Other drive units 170, such as drives with wheels, chain drives or legs, are well known.

In generating the driving command by the control software module 151, specific restrictions should be noted. For example, the acceleration to which the robot is subjected may not and/or is not allowed to exceed a certain value. Another example is setting a maximum speed. These limits may be dictated, for example, by the components of the robot used, but also by the environment surrounding the robot. For example, during operation of the motor in use, care should be taken to ensure a maximum rotational speed and/or power that cannot be exceeded to ensure permanent operation. For example, the maximum speed is reached in a longer straight run. Lower speeds are typically achieved during cornering and/or obstacle avoidance. The movement speed can be reduced in particular depending on the required accuracy of the movement.

Furthermore, the control software module 151 may contain safety-related functions. Such safety-related functions can trigger the response (e.g. emergency braking, avoidance maneuvers, etc.) of the robot to installation-related events (detected hazardous situations). Possible security-related events are for example: the impact detected by a Sensor (impact Sensor 124, bump) or the falling edge detected by another Sensor (fall Sensor 125, Drop-Sensor). Hereby, a possible response to a detected collision or to a detected falling edge is an immediate stop (scram) of the robot. Furthermore, the robot can then be moved back a presettable distance (about 1-5cm) to establish a safe distance to the obstacle (or drop edge). The navigation module 152 need not be concerned with this standardized response to the security-related event. It is sufficient that the navigation module 152 can determine the current position of the robot and the position of the obstacle detected by the Sensor (bump or Drop-Sensor) and can use it to adjust and/or re-determine the path.

In order for the robot to identify particularly heavily soiled areas when treating the floor surface and to treat these areas more intensively, the robot may be equipped with a sensor (soil sensor 126) for detecting the level of soiling of the floor surface. A simple way to perform a more intensive cleaning of the surface is to reduce the speed of the robot so that the contaminated surface is treated for a longer time. Here, the speed may be reduced directly in the process of generating the driving command through the control software module 151. This eliminates the need to separately provide information about contamination to the navigation module 152. No corresponding adjustment of the processing modes and in particular of the processing strategies is necessary. Thereby enabling quick and direct response to heavily contaminated locations. This enables in particular a relatively fast response as a safety-related event. According to an additional or alternative solution, the direction of travel of the robot can also be changed instead of the speed of travel. For example, it may be moved back slightly and then forward again to repeatedly clean the floor area covered by this strategy. Additionally or alternatively, the currently planned trajectory can also be modified (and thus the direction of travel is likewise changed). For example, it can be modified as follows: so that the floor areas identified as heavily contaminated (by "detours" resulting from the modified trajectory) are repeatedly covered and then continue to drive through the originally planned trajectory.

For example, in the case of detection of a severe contamination level, the speed may be adjusted such that the maximum speed allowed is from a first value v₁To a second maximum speed v₂. In particular, the maximum speed (v) can be reduced₂<v₁E.g. v₂＝0.5·v₁). This reduces the speed significantly especially during longer straight runs. For example, in an area where the robot needs to travel more slowly due to an obstacle, only in an area where the speed is greater than the newly set maximum speed v₂The speed is reduced. Thereby avoiding unnecessary additional slowing of the robot. For example, the reduced maximum velocity v can always be maintained as long as the dirt sensor detects a heavily contaminated state₂. Alternatively or additionally, a switch to normal fouling may be made again in the event of a heavily contaminated stateAfter the dyeing phase, the reduced maximum speed v is maintained for a predefinable time (e.g. 5 seconds) or for a predefinable distance (e.g. 5cm)₂. The maximum speed is then reset to its original value v₁. This response to an increased pollution level does not affect the operation of the navigation module, in particular does not affect the path planning and/or the updating of the path planning.

Another example of changing speed in case a severe contamination level is detected is: in case a switch from a normal contamination state to a severe contamination state is detected, the robot is stopped. For example, it may be done in a similar manner to the response to a detected collision or a detected falling edge (e.g., a scram). Thereby ensuring a fast response to heavily contaminated locations.

Additionally or alternatively, the robot is capable of moving back. I.e. the direction of travel is reversed. For example, the robot can be controlled directly by the control software module to move back a presettable distance (e.g., 5 cm). This may be done, for example, in a similar manner to the response to a detected collision or a detected falling edge. In particular without complex planning by the navigation module 151. Based on the processing mode and basic processing strategy, as described above, and based on the robot location and map information, the navigation module 151 is able to receive path plans for the robot without taking into account the increased levels of contamination that may be present. The advantage of the move-back is to compensate for delays in the detection of severe contamination and to process multiple times areas where contamination has a potentially increasing tendency.

The robot must stop before it is controlled in the backward direction. This can be achieved by a sudden braking strategy similar to a dangerous situation. Alternatively, this can be achieved by slow braking and (in the reverse direction) acceleration, resulting in a "softer" visual impression of forward and backward movement. A walk-through strategy similar to the response to a dangerous situation (stop and walk back) may also be used as the standard response. In this way, the processing mode implemented in the navigation module 152 likewise does not need to be adapted to this purpose.

During the return movement, the robot can move backwards linearly or along the track of the last movement. The latter case is for example realized by inverting the last generated drive command. The distance or duration of the backward movement may be a preset value. Alternatively, the sensor measure may be taken as a condition for stopping backward movement and resuming normal movement again. For example, the signal of the dirt sensor can be used. For example, the robot may move backwards until the detection signal for the contamination level again falls below a predefinable threshold value, or until a collision with an obstacle is imminent. In one example, the robot moves back until the soil sensor no longer finds an increased level of contamination and continues to move a defined distance (or duration). During the backward motion, collision avoidance may be effective.

In an alternative embodiment, the navigation module 151 may also receive information about the contamination level and implement a path plan for the backward movement of the robot. This has the advantage that obstacles which may be located behind the robot can be noticed during the control of the robot. Alternatively or additionally, the backward movement may be controlled by the control software module 152 as described above, wherein the movement is also monitored by the safety monitoring module. The safety monitoring module can cause the movement to stop in the event of a threat, for example, that there is an obstacle that has collided or fallen into a depression. The security monitoring module may be a stand-alone module or part of the control software module 151 and operates independently of the navigation module 152.

After the robot has stopped and/or moved backwards, the robot can move forwards again. In this case, for example, a reduced speed can be used for at least one presettable distance or duration as described above. Alternatively or additionally, the backward control of the robot may be activated each time a severe contamination is newly detected, which results in a continuous reciprocating motion, similar to the way a person would have when dealing with a severe contamination.

An alternative approach to controlling an autonomous mobile robot for treating floor surfaces uses a treatment map (e.g. a cleaning map) by not noting responses to contamination levels in the treatment map-as a response to the signal of the contamination sensor. All processed areas are marked in the process map. For example, this process map may be displayed to the user so that the user obtains an overview of the job on the robot. At the same time, the robot can use this map to identify areas that still need to be processed. This makes it possible to identify, for example, an area that has not been processed due to the position of the obstacle. When the robot passes by these not yet processed regions, these regions can be taken into the current process (in case of a break in the current process pattern). Alternatively, after the processing of the area according to the processing pattern (which depends on the operation mode) is completed, the area that has not been processed may be identified based on the processing map and the robot may be guided to process it. Such methods are well known.

Fig. 4 shows an example of a solution for controlling an autonomous mobile robot in dependence of the signal of a dirt sensor without switching the treatment mode on which the current treatment strategy is based. Here, in a first step (fig. 4, S1), map data relating to the surroundings and the robot position are updated in response to information provided by the navigation sensors about the surroundings of the robot and the range sensors of the robot. In the second step (fig. 4, S2), the processing map (e.g., cleaning map) is updated. For this purpose, for example, the region between the last known position of the robot and the position determined in the preceding step S1 is marked as processed. Wherein the position of the processing unit (on the robot) can be taken into account. The data provided by the soil sensor may be taken into account in marking the area as treated (or untreated) in the treatment map. The path to be traveled by the robot is then updated as specified by the current processing mode. For example, the policy for subsequent processing of the legacy facets corresponding to the current processing mode may be: when the robot next passes by the left-behind position, the subsequent treatment of the surfaces takes place, during which the robot moves according to the movement pattern used in the respective mode (for example meandering).

In this case, a surface can be marked as processed, in particular, if the dirt sensor detects no contamination or a normal contamination level for the surface. If a severe contamination level is found, the relevant face is marked as severe contamination in the process map. The faces so marked are processed multiple times. In the simplest case, the further processing is performed by marking the relevant faces as "unprocessed". The marking of the surface is therefore the same as the area of the floor surface which has not yet been driven over. The effect of this operation is that this area is identified as not yet processed according to the processing strategy for systematic or complete coverage of the floor surface, so that the robot will be automatically redirected there in the future (according to the strategy used in the corresponding processing mode for subsequent processing of previously unprocessed faces). There is no need to modify or adjust the processing strategy and trajectory planning that takes into account the detected severe contamination directly.

Fig. 5 shows an example of a processing sequence of the floor surface, and corresponding marks in the processing map. Fig. 5A shows a robot 100 that systematically treats floor surfaces by following a trajectory in a meandering and circuitous manner. The area marked "processed" in the process map is shown in hatched lines. In the example shown, the robot travels towards a locally heavily contaminated area D. In the situation shown in fig. 5B, the robot 100 has reached the heavily contaminated area D, and this area is detected by means of the dirt sensor.

In response to detecting the heavily contaminated area D, on the one hand the current position of the robot is not marked as "processed"; on the other hand, a region previously marked as "processed" may be relabeled as "unprocessed" (or marked as "pending subsequent processing") for further processing of the region in the future. In the example shown in fig. 5C, the area immediately adjacent to the robot (e.g., having a fixedly defined width) beside the robot or behind the robot 100 is relabeled as "unprocessed". This has the advantage that heavily contaminated areas D, which may not have been identified previously, are also reprocessed. For example, a square having a side length of two times the robot diameter and a center equivalent to the center of the robot is marked as "unprocessed" (see the square drawn by a chain line in fig. 5C). It is noted that the areas marked as processed typically match the shape, size and position of the processing units in/on the robot. Similarly, the areas marked as "untreated" due to the identification of severe contamination levels match, at least partially, the shape, size, and location of the treatment units.

Fig. 5D shows the robot 100 on the next cleaning trajectory in a meandering pattern. The area D previously identified as heavily contaminated is marked as "untreated" (see fig. 5D, square shown in dotted lines). The robot 100 recognizes this based on the process map. Accordingly, when the area D marked as "unprocessed" is reached, the robot 100 processes the area again. Fig. 5E shows a possible processing pattern resulting from the mentioned re-processing of the region D. In an alternative design, the robot may also follow the trajectory in a straight line as shown in fig. 5D and return the region D marked as "unprocessed" at the end of the meandering processing pattern, thereby processing the region D again. The two treatments significantly improved the cleaning effect in the heavily contaminated zone D. No special adjustment of the processing modes is required because of the inherent nature of using map-based systematic processing strategies (identifying and processing legacy areas).

Intensity map-the aforementioned method of controlling an autonomous mobile robot aims to perform a more intense cleaning of individual areas (in particular areas identified as heavily contaminated) than of other areas. If such intensive cleaning of individual areas is required repeatedly, the valuable information obtained can be utilized for long-term optimization of the application of the robot and for better adaptation of the application to the customer's needs. For this purpose, this information must be systematically recorded and analyzed.

To this end, the first step is to record the actual local processing intensity in the map. This means that it is recorded for all positions of the application area whether these positions have already been processed (if not, the processing intensity is zero), and at what intensity.

The measure for intensity may be, for example: the duration of the process in case the robot is e.g. stopped at a heavily contaminated location, or moved forward and back. Additionally or alternatively, the processing frequency may be a measure of intensity or have an effect on intensity, in case the robot travels over a heavily polluted location, for example, multiple times. In case the robot travels over a position, for example at a reduced speed, the speed may also be a measure of the intensity or have an influence on the intensity. Finally, in case of e.g. lifting the suction efficiency of the robot, the treatment efficiency during the treatment may also be a measure of the intensity or have an influence on the intensity. If the surface is treated in a slowed manner, the time spent by the robot on this surface increases; if the faces are processed several times, the time spent by the robot on the relevant face is also increased. Hereby, a measure for the treatment (cleaning) intensity may be the product of the treatment time of the section and the used treatment efficiency (e.g. the suction efficiency of the suction unit, in general: the possible dirt transport per unit time). This product (time multiplied by processing efficiency) can also be considered as "work" that occurs in processing a unit area of the floor surface.

One simple solution for creating such an intensity map is for example: the current position of the robot is stored at regular intervals, for example once per second. This produces a map containing the point cloud. In areas where the robot is more frequently present and/or where the robot stays longer (e.g. due to a reduced speed), the points of the stored robot positions are more dense than other positions. The spatial density of robot positions (points per unit area) thus stored is an applicable measure for the processing intensity.

The reason for the higher processing intensity may (as described earlier) be: a response to the data provided by the soil sensor, thereby performing multiple and/or slowed processing of the area identified as heavily contaminated.

Another reason for the increased processing intensity may be information about the surroundings of the robot provided by navigation sensors. For example, in the vicinity of obstacles, and in particular in the context of cleaning walls and/or corners, the speed may be reduced. This has the advantage that a more accurate navigation is achieved with a reduced speed. This enables cleaning in a closer way to obstacles and in corners. Furthermore, the cleaning efficiency is additionally improved, so that the dirt accumulated at the corners and edges can be more effectively removed.

Another reason for the increased processing strength of the region is an explicit user instruction. For example, the user may instruct (e.g., via the human machine interface 200, see fig. 2) the robot 100 to perform more intensive processing and/or repeat processing of the area in which it is located. Alternatively or additionally, the user may instruct the robot to more thoroughly and intensively clean a space (e.g., an aisle) or an area (a dining area). For this purpose, for example, map data of the robot can be displayed on an HMI (e.g. a tablet) in the form of a robot application area plan. The user may then mark directly in the displayed map the areas that need to be processed with greater intensity. For example, the user may select a cleaning program (treatment mode) that performs a more intensive treatment on the floor surface.

Further, the information stored in the map may prompt the robot to process the location or area more intensely. This may be, for example, information entered by the user, such as explicit instructions indicating a more intensive processing of the room (or a part thereof). Alternatively or additionally, the user may also use indirect information, such as a room name (e.g., "kitchen"), an area name (e.g., "entrance area"), and/or an object name (e.g., "table"), for example, to adjust the intensity of the treatment. For example, from the room name "kitchen" or the area name "entrance area" or "dining area" it can be inferred that there is a particularly high cleaning requirement here.

Further, the robot can learn information about the necessity of high-intensity processing. For example, the robot can determine that an area always requires more intensive cleaning than other areas due to higher contamination levels. For this purpose, for example, an intensity map can be stored after each processing application. The stored map may be analyzed for style and changes during several processing applications. This makes it possible, for example, to identify: in almost every treatment application, the room requires (at least in part) a more intensive treatment. On this basis, the robot can always treat the whole or parts of the room in a more intensive treatment mode, either independently or after user confirmation. Alternatively, the user may also be advised to clean the room more frequently. For example, where treatment is presently only once every two days, daily treatment may be recommended.

As an alternative to storing the entire intensity map, it may be sufficient to determine and store (e.g. due to severe contamination) areas of particularly high treatment intensity after the treatment application. For example, regions and/or locations where the treatment intensity is greater than a minimum value, an average value corresponding to the total intensity, and/or the intensity of the standard treatment pattern are stored.

Such intensity maps may be used as alternative treatment maps and pollution maps (i.e. maps in which each location or area in the application area corresponds to a floor surface pollution level as determined by the sensor). Alternatively, the intensity map may be used as a beneficial supplement to the above described map to more simply and directly present the required information to the user and improve the autonomous learning characteristics of the robot.