AUTONOMOUS ROBOTIC DEVICE AND RELATED CONTROL METHOD

Abstract

An autonomous device (110) and a method for controlling the autonomous device (110) are disclosed. The autonomous device (110) is configured to perform at least one task selected from a household task, a commercial task, and an industrial task. The autonomous device (110) includes at least one spatial sensor (112) for generating position information and at least one additional sensor (114) for determining at least one additional parameter. The autonomous device (110) further includes at least one task unit (124) configured to perform the household task, the commercial task, and / or the industrial task, and at least one electronics unit (126). The electronics unit (126) is configured to generate a map (128) using the position information. The electronics unit (126) is further configured to associate the additional parameter with the position information and add the position of the additional parameter to the map (128), thereby creating a parameter map including a position-correlated value of the at least one additional parameter. [Selected Figure] Figure 1

Description

[Technical field]

[0001] The invention relates generally to an apparatus and a method for performing at least one task related to various tasks, for example household, commercial or industrial tasks. The apparatus and method according to the invention may be employed in various areas of daily life where robots may be used, in particular cleaning, for example vacuuming or mopping, gardening, for example mowing, and / or transportation. The apparatus and method may furthermore be used for specialized purposes, in particular by way of example the apparatus and method may be used in the field of surveillance, for example security surveillance or monitoring of patients, children or elderly people in the field of professional care. Furthermore the apparatus and method may also be used in the field of transportation, for example logistics and storage. However, other applications are also possible. [Background technology]

[0002] A large number of robots or autonomous guided vehicles configured for domestic, commercial and industrial use are known from the prior art. Robots can be used for cleaning, e.g. vacuuming or mopping, weeding, surveillance, agriculture, and transportation, e.g. storage and logistics. Without narrowing the scope, the present invention is specifically described with respect to domestic use, e.g. use for performing cleaning tasks, e.g. vacuuming or mopping, and gardening tasks, e.g. mowing.

[0003] As an example, US2005 / 0171644 A1 discloses an autonomous mobile robotic cleaner that can completely clean areas along walls or other obstacles in a room. During a cleaning operation, the robotic cleaner creates map information about areas that have already been cleaned and areas where obstacles exist, and stores the map information in a memory. The robotic cleaner performs a basic cleaning operation to clean the area while moving within the area according to a predetermined movement procedure. Then, based on the map information, the robotic cleaner performs an uncleaned area cleaning operation to clean the uncleaned areas that could not be cleaned in the basic cleaning operation. After that, the robotic cleaner performs an edge cleaning operation to clean the edges of the obstacles based on the map information.

[0004] Furthermore, WO2016 / 098050 A1 describes a method of recording data from at least one sensor of a robotic vehicle responsive to the robotic vehicle transporting a portion of a parcel, and determining a confidence score associated with the recorded data for each of a plurality of potentially possible detection events. The confidence score may correspond to a probability that the recorded data corresponds to an object or feature. The method may further include generating map data including one or more objects or features that correlate to the potential detection events based at least in part on the confidence scores of each object or feature.

[0005] US 2005 / 0273226 A1 enables a self-propelled vacuum cleaner to generate geographic information while moving around a room using the detection results of passive sensors such as an autofocus (AF), and when a fire is detected by a smoke sensor and / or a temperature sensor, to move to a predetermined resident call position with the highest priority, shout a guidance message there, and guide the resident along the evacuation route to the evacuation gate. It is possible to set multiple resident call positions with a priority for each position, and if there is no response at the first resident call position, to move the self-propelled vacuum cleaner to the next resident call position.

[0006] US 2016 / 0050840 A1 describes a method for agriculture and agricultural monitoring that includes designating an area to be imaged, determining a flight path over the designated area, operating an unmanned aerial vehicle (UAV) along the flight path, acquiring area images using a camera system mounted on the UAV, and processing the acquired images.

[0007] In US 2012 / 0078417 A1, techniques are provided for detecting energy and environmental leaks in an indoor environment using one or more mobile robots. The energy leak detection system includes one or more mobile robots configured to move through at least a portion of a building and take temperature and airflow measurements at multiple locations within the building. An environmental leak detection system is also provided. The environmental leak detection system includes one or more mobile robots configured to move through at least a portion of a building and take airborne substance measurements at multiple locations within the building.

[0008] Furthermore, DE 10 2007 010 979 B3 discloses a method for cleaning floor surfaces by means of a self-propelled cleaning device, in particular a robotic vacuum cleaner, with a drive unit, a control unit for controlling the direction of movement and a sensor device for detecting a position-dependent floor condition parameter, in which the control unit controls the direction of movement as a function of the position-dependent floor condition parameter detected by the sensor device. In order to reduce the processing time of individual areas of the surface and nevertheless obtain a satisfactory result, the control device determines the degree of soiling of individual areas of the surface as a position-dependent condition parameter based on the amount of soiling recorded there during cleaning and detected by the sensor device, stores this, and controls the processing intensity of the area in subsequent processing cycles as a function of the stored degree of soiling of this area. Despite the advantages of using robots arranged to perform domestic, commercial and industrial tasks, such as cleaning, mowing, monitoring, transport, etc., some technical challenges remain. Thus, in general, the execution of a task can be a time-consuming procedure. Furthermore, many of the tasks performed by robots commonly involve complex and expensive presetting and adjustments. Summary of the Invention [Problem to be solved by the invention]

[0009] It is therefore desirable to provide means and methods that address the above-mentioned technical challenges of using robots to perform domestic, commercial or industrial tasks, in particular, devices and methods are proposed for improving the performance of tasks, particularly vacuum cleaning, mopping, weeding, etc., compared to devices and methods known in the art. [Means for solving the problem]

[0010] This problem is solved by an apparatus and a method having the features of the independent claims. Advantageous embodiments, which can be realized in separate ways or in any combination, are set out in the dependent claims.

[0011] When used below, the terms "having", "comprises" or "including" or grammatical variations thereof are used non-exclusively. Thus, these terms can refer both to the situation where, apart from the features introduced by these terms, there are no further features present in the matter described in this context, and to the situation where there are one or more further features present. As an example, the expressions "A has B" and "A comprises B" or "A includes B" can refer to the situation where, apart from B, no other elements are present in A (i.e., A is composed exclusively of B), and to the situation where, apart from B, one or more further elements are present in the entity A, for example, the element C, the elements C and D, or further elements are present.

[0012] Furthermore, it should be noted that the terms "at least one," "one or more," or similar expressions indicating that a feature or element may be present one or more times are typically used only once when introducing each feature or element. In the following, in most cases, when referring to each feature or element, the expressions "at least one" or "one or more" will not be repeated regardless of the fact that each feature or element may be present one or more times.

[0013] Furthermore, when used hereinafter, the terms "preferably", "more preferably", "particularly", "more particularly", "particularly", "more particularly" or similar terms are used in relation to any feature without limiting the possibility of substitution. Thus, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. The invention can be implemented by using alternative features, as the skilled person will recognize. Similarly, features introduced by "in one embodiment of the invention" or similar expressions are intended to be optional features, without any limitation regarding alternative embodiments of the invention, without any limitation regarding the scope of the invention, and without any limitation regarding the possibility of combining the feature thus introduced with other optional or non-optional features.

[0014] In a first aspect of the present invention, an autonomous device configured to perform at least one task selected from a domestic task, a commercial task and an industrial task is disclosed, the autonomous device comprising at least one spatial sensor for generating position information and at least one further sensor for determining at least one further parameter.

[0015] The term "autonomous device" as used herein is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to any special or specialized meaning. The term may refer, in particular, but without limitation, to any device configured to autonomously perform at least one task. Thus, an autonomous device may be configured to perform a task without external assistance or direction, e.g., without assistance from or control by a user. In particular, an autonomous device may be configured to react to a particular situation independently of external input, e.g., using pre-programmed routines, self-learning mechanisms, etc.

[0016] The term "task" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or specialized meaning. The term may particularly refer to any assignment performed by a robot, without limitation. The task may specifically relate to a household task, a commercial task, or an industrial task. In particular, the task may relate to cleaning a surface, such as, for example, vacuuming and / or wiping floors and / or washing a surface and / or waxing a surface and / or mopping a surface. The task may further relate to agriculture and / or horticulture, in particular, for example, monitoring and / or fertilizing and / or watering plants and / or mowing and / or raking and / or digging up grass. Furthermore, the task may refer to removing trash, such as, for example, digging or lifting trash. Furthermore, the task may refer to cleaning a pool, windows, gutters, etc. Furthermore, the task may refer to charging an electrical device. Further, the task may refer to carrying purchased items, luggage, or the like. Further, the task may include educational or entertainment tasks. Further, the task may include carrying luggage or following a user, such as in a smart cart application. Further, the task may refer to delivering, picking up, interacting with, or holding an object. Further, the task may include monitoring a patient, child, or elderly, such as in a professional care application. Further, the task may refer to monitoring tasks and / or transport tasks.

[0017] The term "spatial sensor" as used herein is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to any special or specialized meaning. The term may specifically refer to, without limitation, any sensor configured to generate location information. In particular, a spatial sensor may be configured to generate location information for an autonomous device.

[0018] The term "location information" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or specialized meaning. Specifically, the term may refer to any information related to the spatial location of an object and / or the orientation of an object in space, without being limited thereto. Specifically, the location information may be or include a set of numbers, letters, or symbols that specify the location of an object in a spatial reference system, such as a geographic coordinate system, a Cartesian coordinate system, a polar coordinate system, or other type of coordinate system. As an example, a spatial sensor may generate location information of an anonymous device in a Cartesian coordinate system that has its origin at the starting position of the anonymous device. Additionally or alternatively, the spatial sensor may generate location information of one or more objects in the vicinity of the autonomous device. As an example, the spatial sensor may generate location information of one or more objects in an area, specifically a circular area having a radius r. The radius r may vary from 0 to 50 m, preferably from 0 to 20 m, more preferably from 0 to 10 m, and most preferably from 0 to 5 m.

[0019] The term "further sensor" as used herein is a broad term and is given its usual and customary meaning to those skilled in the art, and is not limited to any special or specialized meaning. The term may specifically refer to, without limitation, any sensor, such as a particle sensor using spectroscopy or visible light, a dirt sensor, a dust sensor, an infrared sensor, a grass sensor, an air quality sensor, a temperature sensor, a gas sensor, a battery sensor, a voltage sensor, a current sensor, a power consumption sensor, a wheel speed sensor, a wheel encoder, a load sensor, a humidity sensor, an inertial measurement unit, a magnetic sensor, a tilt sensor, a compass, a sound sensor, a pressure sensor, a Wi-Fi connection and / or signal sensor, a mobile phone connection sensor, a baby phone signal sensor, a radio signal sensor, a color sensor, a UV sensor, a radiation sensor, an air pressure sensor, or any other sensor. In particular, the further sensor may be task-related, and thus the sensor may be configured to determine at least one further parameter related to the task, such as, for example, a particle sensor for sensing the amount of particles entering during vacuum cleaning. Alternatively, the further sensor may be non-task-related, such as, for example, determining at least one parameter not related to the task or independent of the task. In particular, the further parameters may be or may include the amount of particles in a fluid, e.g. the amount of particles in the wash water in a wiping task, the humidity of the fluid, e.g. the humidity in the air, the amount of particles in a gas, e.g. the amount of dust particles in the air, the pressure of any fluid, in particular the atmospheric pressure, a gas concentration, in particular the concentration of oxygen, carbon monoxide, ozone, carbon dioxide or nitrogen oxides, the quality of the connection, in particular the Wi-Fi connection and / or the mobile phone connection and / or the baby phone connection and / or the wireless connection, the amount of grass, the color of the grass or other parameters of the properties of the object or environmental material.

[0020] The autonomous device further includes at least one task unit configured to perform a household and / or commercial and / or industrial task. The term "task unit" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or specialized meaning. The term may specifically refer to a part and / or combination of parts configured to perform a task, without being limited thereto. In particular, a task unit may be or include an item for performing a task, such as a vacuum cleaner for vacuum cleaning.

[0021] Additionally, the autonomous device comprises at least one electronics unit configured to generate a map using the location information.

[0022] The term "electronics unit" as used herein is a broad term and is intended to be given its ordinary and customary meaning to those skilled in the art, and is not limited to any particular or specialized meaning. The term may refer to any device capable of performing logical operations, particularly, but not limited to, any device. By way of example, the electronics unit may be or include at least one computer, at least one computer network, or at least one data processing device, such as, preferably, a processor with software or hardware programming for performing at least one logical operation. Additionally or alternatively, by way of example, the electronics unit may comprise an application specific integrated circuit (ASIC). The electronics unit may further comprise one or more additional devices, such as one or more data storage devices and / or interfaces.

[0023] The term "map" as used herein is a broad term and is to be given its ordinary and customary meaning to those skilled in the art, and is not to be limited to any particular or specialized meaning. The term may refer, without limitation, to a data structure having multiple variable elements, where the variable elements are assigned to or associated with location information. Thus, by way of example, a map may include multiple elements, where the elements are variables whose values ​​may be adapted or changed, and where each element may be addressed or identified by location information. By way of example, a map may include arrays, fields, or other data structures that may implement the above-mentioned functionality of a map.

[0024] Specifically, a map may be generated of an area covered or traversed by an autonomous device while performing a task, where the location information may be generated continuously or in discrete steps, as an example. The location information used to identify elements of the map may be a group of items of spatial information from the area covered or traversed by the autonomous device while performing the task. As an example, each element of the map may be uniquely identified by at least one item of spatial information from the area covered or traversed by the autonomous device while performing the task.

[0025] In particular, the areas considered for creating the maps, such as parameter maps or statistical maps as described in more detail below, are e.g. accessible areas A a , for example, may include only accessible areas of a room, specifically areas that the autonomous device can actually traverse or pass through. Thus, as an example, location information, such as the location of the autonomous device, may be acquired by using a spatial sensor, such as a distance sensor, for example, a distance sensor measuring the walls of a room, such as, for example, a Neato Botvac D4 TM When determined by using a triangulation sensor such as a model, for example, the area A within a wall or physical boundary detected by the sensor w Specifically, Aw ≧A a So, the accessible area A a In particular, the area accessible to the autonomous device may be reduced by various obstacles, such as plants or furniture, such as chairs, if the task performed by the autonomous device is a task performed indoors. A closed door may, for example, prevent the autonomous device from entering, thereby denying access to an entire room. These obstacles may, for example, prevent the autonomous device from entering an area A within a wall or boundary. w This may prevent access to the entire area within the wall detected by the spatial sensor.

[0026] In addition, accessible area A a may change due to, for example, the movement of obstacles. In particular, the accessible area A a may specifically be or include an area that is traversed and covered at least once by an autonomous device, specifically an area that the autonomous device was able to access at least once, e.g., at least once within a given time interval.

[0027] The electronics unit is further configured to link the further parameter to the location information and add the location of the further parameter to the map, thereby creating a parameter map including a location-correlated value of at least one further parameter. The term "parameter map" as used herein is a broad term and should be given its usual and customary meaning to those skilled in the art and should not be limited to a special or specialized meaning. In particular, the term may refer, without being limited to, to a map as defined above, in which the elements of the map include values ​​of at least one further parameter assigned to location information. Thus, as an example, an element, preferably each element of the parameter map, may include at least one item of location information, for example as a unique identifier, and further at least one value of at least one further parameter, i.e. a value assigned to the location information. In particular, the parameter map may include both information regarding the value of the parameter and location information indicating where the value of the parameter was determined.

[0028] The map, specifically Area A a The map of is divided into one or more sub-areas A, e.g. by spatial discretization. s Therefore, specifically, for example, an accessible area A, which is a continuous area, a Specifically, the area is divided into a number of separate corresponding parts, e.g., sub-area A s For example, all sub-area A s The sum of Σ(A s )=A a So, the accessible area A a In particular, the spatial discretization may be or may include, for example, a regular discretization, a scattered discretization, or a mixture thereof. sis a spatial sensor and / or a further sensor, in particular an active area or measurement area of ​​one or both of the spatial sensor used to generate the position information and the further sensor used to determine at least one further parameter, e.g. a minimum unit area A u Furthermore, at least one sub-area A s Specifically, accessible area A a As an example, one sub-area may be 0.1 cm 2 ≦A s ≦50m 2 and more specifically, 0.5 cm 2 ≦A s ≦5m 2 and more specifically, may have a size in the range of 1 cm 2 ≦A s ≦1m 2 Thus, by way of example, one sub-area may have a size that is at least one tenth the size of the area of ​​the autonomous device, and may have a size that is less than or equal to 50 times the area of ​​the autonomous device.

[0029] Sub-area A s may have one or more of the following shapes: a triangle, a rectangle, a polygon. s The shape of the accessible area A may vary. a Sub-area A, located at the edge or border of a The shape of the accessible area A a Sub-area A is located in the central area of a Specifically, the shape of the accessible area A may be different from that of the accessible area B. a The shape of the sub-area at the edge or boundary of the accessible area A may, for example, match the shape of the edge or boundary of a portion of the accessible area represented by the sub-area. a The sub-areas at the edge or border of the accessible area Aa Thus, in particular, the shape of the sub-areas at the edges of the map may match, e.g. be identical to, the shape of a wall or other physical boundary of the movement of the autonomous device.

[0030] Furthermore, a temporal discretization may be used in addition to or instead of, for example, a spatial discretization. Thus, the accessible area A a Specifically, the accessible area that is continuous in time may be discretized in time. a may be determined at time or point i as well as at time i+1, i and i+1 may differ by exactly one time interval Δt. By way of example, the time interval used for the temporal discretization may range from a few minutes to a few weeks. Thus, the time interval Δt may be in the range 1 second≦Δt≦3 months, more particularly 1 minute≦Δt≦1 week. The time interval Δt, for example as a lower limit, may specifically last at least a duration for creating at least one tenth of the map. Furthermore, the time interval Δt may be, for example as an upper limit, three months. By way of example, a typical time interval for an autonomous robot, for example a domestic robot, may specifically be about four hours long. Thus, the time interval Δt may be classified, for example, as morning, midday, evening, night, etc. Additionally or alternatively, the time interval Δt may be centered around a typical meal time, for example a predefined meal time, in particular a meal time that may be programmed by the user, for example in the case of a domestic robot. Furthermore, the time interval Δt may specifically be a complete day. However, the length of the time interval Δt may, for example, vary or have different lengths over any number of time intervals. Thus, Δt may vary in length, for example, depending on the weekday or weekend. As an example, Δt may be a different length of time from Monday to Friday than it is from Saturday to Sunday.

[0031] The electronics unit may be further configured to store the parameter map. In particular, the electronics unit may be configured to store the parameter map such that the parameter map is available after resetting and / or charging and / or restarting the autonomous device. In this manner, the autonomous device performs the following steps: (i) determining location information and at least one further parameter; (ii) generating a parameter map indicating the location of the determined at least one further parameter; and (iii) storing said map so that said map is available after resetting and / or charging and / or rebooting the autonomous device; may be configured to perform the following:

[0032] The at least one further parameter may be directly related to the domestic and / or commercial and / or industrial task. In particular, the parameter may be directly related to the task. As an example, the parameter may be or include information related to the task itself, such as the amount of particles collected during vacuum cleaning.

[0033] The autonomous device may further be an autonomous mobile robot and / or an autonomous guided vehicle. In the following, when referring to an autonomous mobile robot, the option that the autonomous mobile robot is or includes an autonomous guided vehicle is included. In particular, the autonomous mobile robot may be selected from the group consisting of: a domestic robot, for example a cleaning robot; a commercial robot; an industrial robot, for example a manufacturing robot. In particular, the autonomous mobile robot may change position on its own or by itself. In particular, performing a task may involve physically moving, for example running through a living room to vacuum the living room. Thus, by way of example, the mobile robot may move autonomously and thus may be powered by a power supply, for example a battery.

[0034] Furthermore, at least one further parameter may be indirectly related to the domestic and / or commercial and / or industrial task. In particular, the parameter may be indirectly related to the task and thus the parameter may include information indirectly related to the task. In particular, the parameter may be or include information about ambient conditions or measures that influence the task. For example, in the case of wiping a surface, the parameter may include air temperature or surface temperature that influences the wiping task by controlling the evaporation of the wash water.

[0035] Alternatively, the at least one further parameter may not be related to the domestic and / or commercial and / or industrial task. In particular, the parameter may include information not related to the task. In particular, the parameter may be or include information about environmental conditions or measures not directly or indirectly related to the task.

[0036] The electronics unit may be configured to add information to the parameter map, in particular, the electronics unit may be configured to add one or more further or additional parameters to the parameter map.

[0037] The electronics unit may further be configured to extract statistical information from the parameter map. The term "statistical information" as used herein is a broad term and is given its ordinary and customary meaning to those skilled in the art, and is not limited to any special or specialized meaning. The term may specifically refer to, but is not limited to, numerical facts extracted from the data set. In particular, the statistical information may numerically quantify the probability of occurrence of values ​​in the data set. Additionally or alternatively, the statistical information may identify an order or regularity in the data set. As an example, the statistical information extracted from the parameter map may be or include information on the distribution of at least one parameter in the parameter map, such as a pattern of distribution of dirt and / or dust in the map. Specifically, as an example, the statistical information may include an average value of at least one parameter. The statistical information may be preferably extracted from a parameter map including position-related parameter values ​​of a plurality of operations of a task by the autonomous device.

[0038] The statistical information may specifically be or include one or more of the following information: information on the distribution of at least one parameter measured at different time intervals within at least one spatial interval; information on the distribution of at least one parameter measured at different time intervals within at least one spatial interval; information on the distribution of at least one parameter measured at different time intervals within different spatial intervals. Thus, the statistics may be performed on one or more of the following: spatially adjacent values ​​of the parameter during one or more runs; multiple values ​​of the parameter obtained at one same location over multiple runs; multiple values ​​of the parameter obtained over multiple runs at multiple locations. Therein, a spatial interval and / or a temporal interval may be defined for the acquisition and evaluation of at least one parameter. Thus, as an example, a spatial interval may be defined because an autonomous device typically has uncertainties or errors regarding its positioning and / or its position determination, and therefore typically does not repeatedly and accurately pass one and the same spot. By defining a spatial interval, this spatial uncertainty may be compensated. Similarly, a temporal interval may be defined. The autonomous device may pass through each area, spatial interval, or location at a slow speed. In particular, the statistical information may be discretized, such as location information in a map and / or further parameters in a parameter map. In particular, the statistical information may be or include discretized information, such as spatially discretized statistical information or temporally discretized statistical information.

[0039] Furthermore, the electronics unit may be configured to store the statistical information. In particular, the electronics unit may be configured to store the statistical information in a statistical map. Thus, by way of example, the statistical map may include a location correlation value of at least one statistical information. Additionally or alternatively, the electronics unit may be configured to add the statistical information to a parameter map, thereby storing the statistical information in the parameter map. In particular, a statistical or parameter map including the statistical information may indicate, on average over time and / or space, where performing a task, such as cleaning, is most likely to be desirable.

[0040] To obtain a statistical map, by way of example, data of at least one further parameter is s In particular, the autonomous device may use or move different routes each time it performs a task. Thus, as an example, the location of all sub-areas A s The cumulative number of parameters of may specifically be a representative quantity, e.g., a meaningful sample for performing statistics. As an example, the movement path of an autonomous robot may depend on multiple factors that lead to, for example, deviations from the previous movement path. In particular, the center or midpoint of an autonomous device, e.g., a sweeper robot or a lawnmower robot, may, for example, pass exactly the same position twice or may move to an accessible sub-area A. s The autonomous device may not pass through the at least one sub-area A, but may instead move on a path and / or line according to, for example, the sweeping or mowing task it performs. Specifically, in such a case, the boundary between two sub-areas may be, for example, equal to about half the distance between two adjacent paths or lines of movement of the autonomous device, for example, two lines along which the center of the robot moves when performing the task. In this way, at least one sub-area A may be located in the at least one sub-area B. s The magnitude of may depend on the path of travel of the autonomous device.

[0041] In particular, a probability distribution model may be used to obtain the statistical information. As an example, the further parameters may be summarized using a probability distribution model of the parameter values ​​in space and time to obtain the statistical information. The term "statistical distribution model" as used herein is a broad term and should be given its usual and customary meaning to those skilled in the art. The term may refer to any mathematical configuration or model configured to simulate the chance or predictability of the occurrence of a given event, without being particularly limited thereto. In particular, the statistical distribution model may be or include at least one mathematical function that determines the probability of at least one further parameter having or including a given value. In particular, the probability distribution model may be selected from the group consisting of a normal distribution, a Bernoulli distribution, a binomial distribution, a Poisson distribution, a geometric distribution, a discrete distribution or a continuous uniform distribution. As an example, when a uniform distribution is used, each sub-area A s Specifically, the parameter value of sub-area A s In particular, only parameter values ​​recorded within a predefined or given time interval Δt may be considered, for example only parameter values ​​recorded within the last week or month.

[0042] In particular, different probability distribution models may be used for time and space. The probability distribution model for time and the probability distribution model for space may be different. In particular, a first probability distribution model for the spatial distribution and a second probability distribution model for the time distribution may be used. As an example, in the case of a cleaning robot, the cleanliness of the apartment changes before and after meals, and meals may take place at similar times every day, but not at fixed times. Furthermore, meals may or may not take place, for example, they may be postponed. Thus, probability distribution models of the distribution of further parameters with respect to time may be used to take into account changes and fluctuations over time, for example due to changes in the daily life of the household.

[0043] One or both of the statistical map and the parameter map, e.g., including the statistical information, may be specifically resolved in both space and time. To resolve the statistical information in space and time, one or more parameter maps determined or created for a given time interval may be combined into one statistical map, e.g., a statistical parameter map. Thus, as a simple example, a statistical map, in particular a statistical parameter map, may be created from all maps, in particular all parameter maps, recorded or generated within a morning interval, e.g., from 6:00 am to 11:00 am. For example, a further statistical parameter map may be recorded from all maps recorded within a day interval, e.g., from 11:00 am to 4:00 pm. Additionally or alternatively, a further statistical parameter map may be recorded from all maps recorded within an evening interval, e.g., from 4:00 pm to 9:00 pm, and a further statistical parameter map may be recorded from all maps recorded within a night interval, e.g., from 9:00 pm to 6:00 am.

[0044] Furthermore, a spatial discretization of the statistical and / or parametric maps may be performed before the temporal discretization. In particular, in the case of a temporal discretization, a spatial discretization, in particular a discretization of one sub-area A, may be performed before the temporal discretization and the application of the concatenated temporal summary. s A summarization and / or averaging of the parameter values ​​recorded in a given sub-area A may be applied. Furthermore, when applying a time discretization, the summarization and / or averaging of the parameter values ​​recorded in a given time interval may be applied. s The parameter values ​​of may be summarized.

[0045] Accessible Area A a It may be particularly necessary to construct a statistical map. Specifically, the accessible area A is used to generate the statistical map. a Instead of all areas A wUsing the method of the present invention, the summarization of the further parameter values ​​of areas that are not covered by an autonomous device, e.g., a robot, and therefore where the value of the further parameter may not have been determined or measured, may include summarization of the further parameter values ​​of areas that are not covered by an autonomous device, e.g., a robot, and therefore where the value of the further parameter may not have been determined or measured. In this way, the summarization of the further parameter values ​​of sub-areas A that do not have further parameter values, such as inaccessible areas, may be performed. s Averaging of the accessible area A may lead to an underestimation of the averaged parameter values ​​for the subarea. Therefore, when extracting statistics from the parameter map, a may be used.

[0046] The electronics unit may further be configured to adjust the operation of the autonomous device using the statistical information. In particular, the execution of a task by the autonomous device may be adjusted using the statistical information. In particular, the operation of the autonomous device, such as the process of executing a task, may be adjusted by the statistical information. More particularly, the statistical information may be used to identify areas in the map where it is desirable to perform the execution of a task more frequently than other areas in the map. As an example, the statistical information may be used to identify dirtier areas, in particular areas having a particle load higher than the average particle load, and adjust the operation of the autonomous device such that the dirtier areas are cleaned more frequently than cleaner areas, such as areas having a particle load lower than the average particle load. Additionally or alternatively, the operation of the autonomous device may be adjusted in a manner that the autonomous device can perform a quick run, for example, cleaning only the dirty areas. Additionally or alternatively, the operation of the autonomous device may be adjusted in a manner that the autonomous device can repeat the same task in a particular location during the same run, for example, cleaning a dirty area two, three or more times in one run. Additionally or alternatively, the operation of the autonomous device may be adjusted in such a way that the autonomous device can adjust the density of task execution, for example increasing the suction flow rate for suctioning dirty areas.

[0047] The electronics unit may further be configured to adjust the operation of the autonomous devices to increase the quality of the statistical information, in particular to increase the amount of further parameters used for the extraction of the statistical information. In particular, the electronics unit may be configured to only consider statistical information obtained from a sufficiently large sample size and / or a predetermined quality for adjusting the operation of the autonomous devices. Thus, depending on the sample size and quality, the robot may be configured to not consider the statistical information, e.g. the result of the statistical extraction from the parameter map, in the corresponding sub-area, but instead adjust the operation of the autonomous devices to affect the statistical map, e.g. to increase the sample size and / or the quality of the statistical information. In particular, the electronics unit may adjust the operation of the autonomous devices such that the autonomous devices move through some areas more frequently than other areas. This may result in more accurate statistical information due to the increase in the sample size, e.g. the increase in the amount of further parameters determined for that area. In this way, the electronics unit may increase the samples of these sub-areas by visiting these sub-areas more frequently. As a simple example, a room whose door is usually closed such that an autonomous device, e.g. a robot, cannot enter may be cleaned more frequently during the rare times when the room is accessible by the autonomous device. Additionally or alternatively, the electronics unit may be configured to condition the autonomous device to perform or use a standard mode in sub-areas that do not have sufficient sample size and / or quality.

[0048] Furthermore, the electronic device may be configured to select a probability distribution model depending on the quantity of further parameters, in particular the quality of the statistical information. In particular, areas without sufficient sample sizes may indicate that a uniform distribution model may not be applicable when extracting statistical information. Therefore, based on the variability of the sample sizes etc., the electronic device unit may decide to use a different probability distribution model.

[0049] Further, the electronics unit may be configured to obtain location-related environmental state information from the statistical information. Specifically, a location correlation value of the environmental state may be obtained from the statistical information. In particular, the electronics unit may be configured to obtain location-related environmental state information from the statistical information by evaluating and / or interpreting the statistical information. The term "environmental state information" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or specialized meaning. The term may refer to any data having information regarding the status or condition of a direct object or surrounding material without being particularly limited.

[0050] As an example, the environmental condition information may be or include air quality obtained from statistical information such as air composition, or particle density in the air, or air temperature, or air humidity, or atmospheric pressure, or a combination thereof. Turfgrass or lawn condition may be obtained by evaluating color occurrence, such as, for example, the occurrence of green or brown color of grass in the lawn. Further examples of environmental conditions may be or include the strength of a cellular connection and / or Wi-Fi signal strength and / or the strength of a baby phone or radio reception.

[0051] The electronics unit may be further configured to generate recommendation information from the location-related environmental condition information. The term "recommended information" as used herein is a broad term and should be given its ordinary and customary meaning to one skilled in the art and should not be limited to any special or specialized meaning. The term may refer to information including instructions and / or advice regarding actions or behaviors, without being limited to any particular one. As an example, the recommended information may include information regarding advantageous actions in a specified area in a map, where the specified area may be at least a portion of the application area of ​​the autonomous device, such as an area in the generated map. The recommended information may include advantageous suggestions to be applied to the specified area, such as, for example, recommendations for ventilating, air conditioning, or heating an area or room in a house or building, or recommendations for watering plants, specifically, recommendations for watering grass or lawns.

[0052] Furthermore, the autonomous device may include an interface, in particular an interface configured to enable information transfer to an external device, in particular the information may be selected from the group consisting of: maps; parameter maps; statistics; environmental status information; and recommendation information.

[0053] The external device may be or include a control network configured to fully or partially execute the recommendations. In particular, the external device may be a home control network. As an example, the interface may be configured to enable information transfer to external devices that control thermostats, air conditioners, window blinds, water systems, etc. In this way, the recommendations may be automatically executed by the external device.

[0054] Additionally or alternatively, the external device may be or include a user device configured to fully or partially inform the user of the recommendation information. For example, the user device may use visual and / or audio communication to inform the user about the recommendation information and / or to display maps and / or parameter maps and / or statistical information and / or statistical maps. In particular, the user device may be or include a mobile or permanently installed device, such as, for example, a control panel that is part of a home control network, a smartphone, a tablet, etc.

[0055] The autonomous device further includes an evaluation system. The term "evaluation system" as used herein is a broad term and should be given its usual and customary meaning to those skilled in the art. The term may refer to any electronic device or circuit configured to perform at least one numerical calculation by means of hardware and / or software, without being particularly limited thereto. As an example, the evaluation system may include at least one processor, e.g., a processor having one or more data storage devices, having software instructions for performing at least one numerical simulation. In particular, the evaluation system of the autonomous device may be configured to plan a travel path of the autonomous device. For path planning, a solution of the eikonal equation, e.g., an Euler path, may be used, and the autonomous device, e.g., a robot, may follow the Euler path when performing a task. In particular, the Euler path, when followed, may provide a path that is suitable for a given accessible area A, e.g., an area that the autonomous device is expected to cover or traverse without passing through any sub-area twice when performing a task. aThe Eulerian path may correspond to a moving path of the autonomous device that leads to total coverage. However, in many cases, it may not be possible to follow or find the Eulerian path. In particular, objects that were not present during the planning of the path may make it impossible to follow the Eulerian path. Furthermore, in some areas, no Eulerian path exists. Thus, for areas where it is not possible to follow or find the Eulerian path, the autonomous device may follow a path or track that has to pass through some subareas multiple times.

[0056] In particular, if at least one further parameter indicates that the task of the autonomous device needs to be performed in a more intensive manner in some subareas, the subareas that need to be passed or crossed multiple times may be selected as the subareas that require the more intensive task execution. Furthermore, if the autonomous device may pass or cross an area where the statistical information indicates that the task execution is not required, the evaluation system of the autonomous device, e.g. the electronics unit, may be configured to replan the travel path to include the subareas where the task execution is required, e.g. the subareas that require a more intensive manner, thereby reducing the total time required to perform the task.

[0057] In particular, the calculations that may be required to plan the autonomous device's travel path and to determine the statistical map and / or parameter map may be computationally demanding. In particular, the calculations may be too demanding to be performed on the autonomous device's board. Thus, the evaluation system may be located completely or partially externally. In particular, the evaluation system may be one or more of: an electronics unit; a cloud-based system; an external computer or computer network. As an example, the recorded data, such as the parameter map, the statistical map, etc., may be transmitted to a cloud-based system or an external computer or computer network, where the calculations are performed completely or partially, and, as an example, the data may be further used for product optimization and commercial purposes.

[0058] In general, knowing which areas may require the execution of more highly intensive tasks may enable, for example, better planning of routes or paths. In particular, the autonomous device may reduce the time required to perform at least one task when following a planned path rather than an unplanned or random path. As an example, a user may be informed of the total time saved by planning a route or path of movement of the autonomous device when performing a task. In this manner, the time saved as a result of the path planning and optimization may be shown as an output to the user, for example, via an external device.

[0059] In particular, artificial intelligence algorithms such as neural networks may be used to evaluate correlations in either or both of the parameter maps and statistical maps with respect to temporal and spatial resolution.

[0060] As an example, the autonomous device may specifically be floor bound. Thus, the autonomous robot may be, for example, a floor bound robot. Specifically, the autonomous robot may comprise means for moving on the floor, such as wheels, continuous tracks, etc. However, alternatively, the autonomous device may be, for example, a flying robot. Thus, the autonomous device may specifically include means for flying.

[0061] The autonomous device may be an indoor robot. Thus, the autonomous device may be configured to perform indoor tasks, such as tasks performed indoors, for example in a building. However, the autonomous robot may also be configured to perform outdoor tasks, such as tasks performed outdoors, for example tasks performed outside a building, a house or a shelter. In a further aspect of the invention, a method for controlling an autonomous device configured to perform at least one task selected from domestic tasks, commercial tasks and industrial tasks is disclosed. The method has the following steps, which are performed in a predefined order. However, different orders may also be possible. Furthermore, one or more or all of the method steps may be performed once or repeatedly. Furthermore, the method steps may be performed in a time-overlapping manner or even in parallel. The method may further comprise additional method steps not described.

[0062] The method is as follows: a) generating location information using at least one spatial sensor; b) determining at least one further parameter using at least one further sensor; c) generating a map using the location information; and d) creating a parameter map including position-correlated values ​​of at least one further parameter by associating the further parameter with the position information and adding the position of the further parameter to the map; Includes.

[0063] For further possible definitions of most of the terms used herein, reference may be made to the description of the autonomous device as disclosed above or as disclosed in more detail below. In particular, a method for controlling an autonomous device may comprise controlling an autonomous device as disclosed in the first aspect of the invention.

[0064] The method may further include storing the map and / or the parameter map, which may provide availability of the map and / or the parameter map after a reset and / or a charging and / or a restart of the autonomous device.

[0065] Additionally, the method may include adding information to the parameter map. Thus, the method may specifically include extending the parameter map by adding additional information to the parameter map.

[0066] The method may further comprise extracting statistical information from the parameter map. In particular, the method step of extracting statistical information from the parameter map may comprise creating a statistical map comprising location correlation values ​​of the statistical information. In particular, extracting the statistical information may comprise creating a map indicating where, on average, it is most desirable to perform tasks, such as where, on average, cleaning and / or mowing and / or heating and / or ventilation and / or air conditioning is most needed. The method step of extracting statistical information may further comprise saving the statistical map.

[0067] Additionally or alternatively, the method step of extracting statistical information may include averaging the information contained in the parameter map. The term "averaging" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or specialized meaning. The term may refer to, without being limited to, determining an average value that represents a mean value of a set of quantities. In particular, averaging the quantities may refer to determining a value that represents or approximates the arithmetic mean of the quantities.

[0068] Furthermore, the method step of extracting statistical information may include determining anomalies in parameter values ​​and / or correlation locations. The term "anomaly" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or specialized meaning. The term may refer to deviations from a common or normal set of values ​​without being particularly limited. In particular, values ​​in a set of values ​​that exhibit a deviation greater than a standard deviation value may be identified as anomalies, where the standard deviation may be used to quantify the amount of variation or discreteness of a set of values. More specifically, values ​​that exhibit a larger than normal deviation from the mean value of a parameter, e.g., values ​​that exhibit a deviation greater than the standard deviation, may be identified as outliers. Thus, the method step of extracting statistical information may specifically include determining locations where a determined parameter significantly differs from the mean value of the determined parameter.

[0069] The method may further include extracting environmental state information from the statistical information. In particular, extracting the environmental state information may be creating and / or storing an environmental map that includes location correlation values ​​of the environmental state information.

[0070] Methods such as the method step of environmental state information extraction may further include determining anomalies in parameter values ​​and / or location correlation values. Thus, in particular, the method step of environmental state information extraction may include determining areas or locations where determined parameters significantly differ from an average of the statistics of the determined parameters.

[0071] The method may further include generating recommendation information from the environmental state information. In particular, as described above, the recommendation information may include instructions regarding beneficial actions.

[0072] Further, the method may include transferring the information to an external device. The information may specifically be selected from the group consisting of: a map; a parameter map; statistical information; a statistical map; environmental condition information; and recommendation information.

[0073] The method may further include notifying the user of the recommendation information via a user device. In particular, the user may be notified via the user device as described above or as further outlined below.

[0074] The method may further include coordinating operation of the autonomous device. In particular, coordinating performance of a task by the autonomous device may be included in a method for controlling an autonomous device.

[0075] Further disclosed and proposed herein is a computer program comprising computer executable instructions for carrying out the method according to the invention when the program is executed by a computer or a computer network, in particular by an electronics unit of an autonomous device, for example according to any one of the embodiments disclosed above or disclosed in more detail below. In particular, the computer program may be stored on a computer readable data carrier. Thus, in particular, one, more than one or all of the method steps a) to d) shown above may be carried out using a computer or a computer network, preferably using a computer program.

[0076] Further disclosed and proposed herein is a computer program product having program code means for carrying out the method according to the invention in one or more of the embodiments contained herein when the program is executed in a computer or computer network, for example in an electronics unit of an autonomous device. In particular, the program code means may be stored on a computer readable data carrier.

[0077] Further disclosed and proposed in this specification is a data carrier having a stored data structure, which, after being loaded into a working memory or a main memory of a computer or a computer network, e.g. an electronics unit of an autonomous device, e.g. a computer or a computer network, is capable of performing the methods according to one or more embodiments disclosed in this specification.

[0078] Further disclosed and proposed herein is a computer program product having program code means stored on a machine-readable carrier for performing the method according to one or more embodiments disclosed herein when the program is executed on a computer such as an electronics unit of an autonomous device or a computer network. As used herein, a computer program product refers to a program as a tradeable product. The product may generally exist in any format, such as a paper format or a computer-readable data carrier. In particular, the computer program product may be distributed over a data network.

[0079] Finally, disclosed and suggested herein is a modulated data signal containing instructions readable by a computer system, such as an electronics unit of an autonomous device, or a computer network, for carrying out a method according to one or more embodiments disclosed herein.

[0080] With reference to computer-implemented aspects of the invention, one or more, or even all, method steps of the methods according to one or more embodiments disclosed herein may be performed using a computer or computer network, such as an electronics unit of an autonomous device. Generally, these method steps may include any method steps, except those that typically require manual intervention, such as certain aspects of providing a sample and / or performing the actual measurement.

[0081] Specifically, the present specification includes: - a computer or a computer network including at least one processor, the processor being adapted to execute a method according to one of the embodiments described herein, - a computer-loadable data structure adapted to carry out a method according to one of the embodiments described herein while said data structure is executed on a computer, - a computer program adapted to carry out a method according to one of the embodiments described herein while said program is run on a computer, a computer program comprising program means for carrying out a method according to one of the embodiments described herein while said computer program is running on a computer or on a computer network, - a computer program comprising program means according to any preceding embodiment, said program means being stored on a computer readable storage medium; - a storage medium on which a data structure is stored and adapted to carry out a method according to one of the embodiments described herein after said data structure has been loaded into a main and / or working storage area of ​​a computer or a computer network, and - a computer program product having program code means, which may be stored on a storage medium or a computer program product stored on a storage medium, for performing a method according to one of the embodiments described herein when the program code means is executed on a computer or a computer network. is further disclosed.

[0082] The device and method according to the invention may offer many advantages over known methods and devices for performing at least one task selected from household tasks, commercial tasks and industrial tasks. Thus, in particular, performing at least one task as proposed herein may be less time consuming compared to the performing processes known in the art. In particular, the invention may optimize the execution of the task, for example optimizing the operation of the autonomous device to perform the task more efficiently than robots known in the art. In particular, the proposed autonomous device may be configured to clean dirty areas more frequently or to perform quick runs cleaning only dirty areas, thereby improving the execution efficiency of the task compared to devices known from the art.

[0083] Furthermore, the proposed device and method may have an expanded task scope compared to devices and methods known in the art. In particular, the device and method according to the invention may have additional areas of responsibility in addition to performing tasks. In particular, a parameter map is generated, in particular a parameter map with local sensor data. Thus, by way of example, the parameter map may be used to analyze air quality, thereby identifying mold hazards or other threats that are usually only noticed at the time of occurrence. In this way, the invention may further provide health support. In particular, health support may be provided by generating recommendation information, such as information on which rooms need better ventilation, which areas should be cooled or heated differently, etc. Additionally or alternatively, the proposed device and method may provide health support by actively preventing the occurrence of hazards such as mold growth by transferring the recommendation information to an external device. In particular, the external device may be configured to implement the recommendation information and / or directly execute the recommendation, such as, for example, a home control network, control thermostats, air conditioning, window blinds, water supply systems, etc.

[0084] In particular, the proposed apparatus and method may be able to extract and use statistical information in contrast to apparatus and methods known in the art, for example in contrast to the method disclosed in DE 10 2007 010 979 B3. In particular, the extraction and use of statistical information may improve the execution of a task by identifying patterns or regularities in a set of data, such as for example a pattern of dirt and / or dust distribution in a map, for example a pattern of dirt and / or dust distribution in a parameter map. Thus, in the proposed method and apparatus, the execution of a task may be adjusted according to the statistical information, which may improve the execution of the task compared to known methods and apparatus. Additionally or alternatively, the extraction and use of statistical information in the proposed apparatus and method may result in an increase or extension of the field of application in comparison to methods and apparatus known in the art, in particular in comparison to the method disclosed in DE 10 2007 010 979 B3.

[0085] Furthermore, the proposed device and method can provide more reliable information than devices and methods known in the art. In particular, the statistical information that can be extracted in the proposed method and device can be much more reliable than information collected in a single measurement cycle, such as information collected in DE 10 2007 010 979 B3. In particular, the proposed method and device can reliably filter out, for example, unimportant events. Thus, in contrast to known methods and devices, the method and device can avoid paying attention to unimportant events, for example unimportant events in the information collected or generated by the proposed method and device. In particular, the device can be updated after every operation, for example after every task is performed.

[0086] Summarizing, without excluding further possible embodiments, the following embodiments are envisaged:

[0087] Embodiment 1: An autonomous device configured to perform at least one task selected from household tasks, commercial tasks, and industrial tasks, the autonomous device comprising at least one spatial sensor for generating position information and at least one further sensor for determining at least one further parameter, the autonomous device further comprising at least one task unit configured to perform household and / or commercial and / or industrial tasks, and at least one electronics unit, the electronics unit configured to generate a map using the position information, the electronics unit further configured to link the further parameter to the position information and add a position of the further parameter to the map, thereby creating a parameter map having a position correlation value of the at least one further parameter.

[0088] Example 2: The area considered for creating the map is the accessible area A a , for example including only the area that the autonomous device traverses or passes through, according to the preceding embodiment.

[0089]

[0023] Embodiment 3: The map is spatially discretized, specifically, one or more sub-areas A s 4. The autonomous device according to any one of the preceding embodiments, wherein the spatial discretization is regular discretization, sparse discretization, or a mixture thereof.

[0090] Embodiment 4: An autonomous device according to any one of the preceding embodiments, wherein the map is discretized in time.

[0091] Embodiment 5: The electronics unit is further configured to store the parameter map, and in particular, the autonomous device is (i) determining said location information and at least one said further parameter; (ii) generating a parameter map indicating the location of the determined at least one further parameter; and (iii) storing said parameter map such that said parameter map is available after a reset and / or charging and / or reboot of the autonomous device; 2. An autonomous device according to any one of the preceding embodiments,

[0092] Embodiment 6: The autonomous device according to the preceding embodiment, wherein the at least one further parameter is directly linked to the domestic and / or commercial and / or industrial task.

[0093] Embodiment 7: An autonomous device according to any one of the preceding embodiments, wherein said autonomous device is an autonomous mobile robot selected from the group consisting of: domestic robots; commercial robots; industrial robots, in particular manufacturing robots.

[0094] Embodiment 8: The autonomous device according to any one of the preceding embodiments, wherein the at least one further parameter is indirectly linked to the domestic and / or commercial and / or industrial task.

[0095] Embodiment 9: An autonomous device according to any one of the preceding embodiments, wherein the at least one further parameter is not linked to the domestic and / or commercial and / or industrial task.

[0096] Embodiment 10: An autonomous device according to any one of the preceding embodiments, wherein the electronics unit is configured to add information to the parameter map.

[0097] Embodiment 11: An autonomous device according to any one of the preceding embodiments, wherein the electronics unit is configured to extract statistical information from the parameter map.

[0098] Embodiment 12: An autonomous device according to any one of the preceding two embodiments, wherein the electronics unit is configured to store the statistical information in a statistical map and / or add the statistical information to the parameter map.

[0099] Embodiment 13: An autonomous device according to the preceding embodiment, wherein a probability distribution model is used to obtain the statistical information, the probability distribution model being selected from the group consisting of: normal distribution; Bernoulli distribution; binomial distribution; Poisson distribution; geometric distribution; discrete distribution or continuous uniform distribution.

[0100] Embodiment 14: An autonomous device according to the preceding embodiments, wherein different probability distribution models are used in space and time.

[0101] Embodiment 15: An autonomous device according to any one of the preceding three embodiments, wherein one or both of the statistical map and the parametric map are determined in time and space.

[0102] Embodiment 16: An autonomous device according to the preceding embodiments, wherein spatial discretization is performed before performing temporal discretization.

[0103] Embodiment 17: An autonomous device according to any one of the preceding six embodiments, wherein the electronics unit is configured to use the statistical information to adjust operation of the autonomous device.

[0104] Embodiment 18: An autonomous device according to the preceding embodiment, wherein the electronics unit is further configured to adjust operation of the autonomous device to improve the quality of the statistical information, in particular to increase the amount of the further parameter.

[0105] Embodiment 19: An autonomous device according to any one of the preceding six embodiments, wherein the electronics unit is configured to select the probability distribution model depending on the amount of the further parameter.

[0106] Embodiment 20: An autonomous device according to any one of the preceding two embodiments, wherein the electronics unit is configured to obtain location-related environmental condition information from the statistical information.

[0107] Embodiment 21: The autonomous device according to the preceding embodiment, wherein the electronics unit is configured to generate recommendation information from the location-related environmental condition information.

[0108] Embodiment 22: An autonomous device according to any one of the preceding four embodiments, wherein the autonomous device has an interface, the interface being configured to enable information to be transferred to an external device, the information being selected from the group consisting of: the map; the parameter map; the statistical information; the environmental state information; and the recommendation information.

[0109] Embodiment 23: The autonomous device according to the preceding embodiment, wherein the external device is a control network configured to fully or partially execute the recommendation information.

[0110] Embodiment 24: An autonomous device according to any one of the preceding two embodiments, wherein the external device is a user device configured to fully or partially inform a user of the recommendation information.

[0111] Embodiment 25: An autonomous device according to any one of the preceding embodiments, wherein the autonomous device includes an evaluation system, the evaluation system being configured to plan a travel path for the autonomous device.

[0112] Embodiment 26: An autonomous device according to the preceding embodiment, wherein the evaluation system is one or more of the electronics unit; a cloud-based system; an external computer or computer network.

[0113] Embodiment 27: An autonomous apparatus according to any one of the preceding embodiments, wherein the autonomous apparatus is a floor-cleaning robot.

[0114] Embodiment 28: An autonomous device according to any one of the preceding embodiments, wherein the autonomous device is an indoor robot.

[0115] Embodiment 29: An autonomous device according to any one of the preceding embodiments, wherein the autonomous device is configured to perform an outdoor task.

[0116] Embodiment 30: An autonomous device according to any one of the preceding embodiments, wherein the autonomous device is a flying robot.

[0117] Embodiment 31: A method for controlling an autonomous device configured to perform at least one task selected from a household task, a commercial task, and an industrial task, comprising: a) generating location information using at least one spatial sensor; b) determining at least one further parameter using at least one further sensor; c) generating a map using the location information; and d) binding said further parameters to said position information and adding the positions of said further parameters to said map, thereby creating a parameter map comprising position correlation values ​​of said at least one further parameter; The method includes:

[0118] Embodiment 32: A method according to any preceding embodiment, wherein the method includes using an autonomous device according to any one of the preceding embodiments referring to the autonomous device.

[0119] Embodiment 33: A method according to any one of the preceding method embodiments, wherein the method includes storing the map and / or the parameter map, thereby providing availability of the map and / or the parameter map after resetting and / or charging and / or restarting the autonomous device.

[0120] Embodiment 34: The method according to any one of the preceding method embodiments, wherein the method further comprises adding information to the parameter map.

[0121] Embodiment 35: The method according to any one of the preceding method embodiments, wherein the method further comprises extracting statistical information from the parameter map.

[0122] Embodiment 36: A method according to any one of the preceding two embodiments, wherein the method further comprises storing the statistical information in a statistical map and / or adding the statistical information to the parameter map.

[0123] Embodiment 37: A method according to any one of the preceding two embodiments, wherein the method step of extracting the statistical information includes creating a statistical map including position correlation values ​​of the statistical information, and wherein the method step of extracting the statistical information further includes saving the statistical map.

[0124] Embodiment 38: The method according to the preceding embodiment, wherein the method step of extracting the statistical information further comprises averaging information contained in the parameter map.

[0125] Embodiment 39: A method according to any one of the preceding four embodiments, wherein the method step of extracting the statistical information includes determining anomalies in parameter values ​​and / or correlation positions.

[0126] Embodiment 40: The method according to any one of the preceding five embodiments, wherein the method further comprises extracting environmental state information from the statistical information.

[0127] Embodiment 41: A method according to the preceding embodiment, wherein the method step of extracting the environmental state information includes creating and / or storing an environmental map comprising position correlation values ​​of the environmental state information.

[0128] Embodiment 42: A method according to any one of the preceding two embodiments, wherein the method includes generating recommendation information from the environmental state information.

[0129] Embodiment 43: A method according to the preceding embodiment, wherein the method includes transferring information to an external device, the information being selected from the group consisting of: the map; the parameter map; the statistical information; the environmental state information; and the recommendation information.

[0130] Embodiment 44: A method according to any one of the preceding two embodiments, wherein the method includes notifying a user of the recommendation information via a user device.

[0131] Embodiment 45: A method according to any one of the preceding embodiments, wherein the method further comprises coordinating operation of the autonomous device.

[0132] Embodiment 46: A computer program comprising computer executable instructions for performing the method according to any one of the preceding embodiments referring to a method, when the program is executed on a computer or a computer network, in particular on an electronics unit of an autonomous device, more particularly on the electronics unit of the autonomous device according to any one of the preceding embodiments referring to an autonomous device. [Brief description of the drawings]

[0133] Further optional features and embodiments are disclosed in more detail in the following description of the embodiments, preferably in conjunction with the dependent claims, where each optional feature may be realized in an isolated manner as well as in any possible combination as understood by a person skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are illustrated diagrammatically in the figures, where identical reference numbers in the figures refer to identical or functionally comparable elements. [Figure 1] FIG. 1 illustrates a perspective view of one embodiment of an autonomous device performing a task. [Diagram 2]2A and 2B show an embodiment of a map (2A) and a statistical map (2B) generated by an autonomous device. [Diagram 3] 1 shows a flow chart of a method for controlling an autonomous device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0134] In FIG. 1, an embodiment of an autonomous device 110 configured to perform at least one task is shown in a perspective view in which the autonomous device 110 is performing the task. In particular, FIG. 1 may show the autonomous device 110 performing the task in a hallway. The autonomous device 110 includes at least one spatial sensor 112 for generating position information. The position information may in particular relate to the spatial position and / or orientation of the autonomous device 110 in space. In particular, the position information may be or may include information that specifies the position and / or orientation of the autonomous device 110 in a spatial reference system, such as, for example, a Cartesian coordinate system, for example, the ab coordinate system shown in FIG. 1. For this purpose, as also shown in FIG. 1, a second spatial reference system, such as a second Cartesian coordinate system, for example, the xy coordinate system, may be fixed to the autonomous device 110.

[0135] The autonomous device 110 may be an autonomous mobile robot. Specifically, as shown in FIG. 1, the autonomous device 110 may be a cleaning robot, for example, a vacuum cleaning robot. In particular, the autonomous mobile robot may be capable of changing its position by itself or by itself. Specifically, performing a task may include physically moving. Thus, as shown in FIG. 1, performing a task may include traveling through an area, for example, to vacuum a hallway.

[0136] The autonomous device includes at least one additional sensor 114 for determining at least one additional parameter. As an example, the parameter may be or include an amount of particles 116 collected during vacuum cleaning. Specifically, as shown in FIG. 1, the parameter may be or include an amount of particles 116 on a hallway floor, including a door mat 118 adjacent to a front door 120. As a further example illustrated in FIG. 1, the parameter may be or include an air temperature in the hallway, including the air under a window 122.

[0137] The autonomous device 110 further includes at least one task unit 124 configured to perform household and / or commercial and / or industrial tasks. Specifically, as shown in FIG. 1, the task unit 124 may be configured to vacuum clean an area, such as a room, a hallway, a kitchen, a bathroom, or any other area. Furthermore, the autonomous device 110 includes at least one electronics unit 126 configured to generate a map 128 using position information. The position information may be, for example, shown in a spatial reference system, for example, in an ab coordinate system. An embodiment of the map 128 is shown in FIG. 2A. The map 128 may specifically include position information of an area covered or traversed by the autonomous device 110 while performing a task. In particular, as illustrated in FIG. 2A, the map 128 has an outline of an area accessible to the autonomous device 110. Specifically, as shown, the area may include two adjacent rooms, such as a first room 130 as partially illustrated in FIG. 1, and a second room 132, such as a bathroom having a shower area.

[0138] Furthermore, the electronics unit 126 is configured to associate at least one further parameter with the location information and add the location of the further parameter to the map 128. The electronics unit 126 thereby creates a parametric map that includes location-correlated values ​​of the at least one further parameter. In particular, the electronics unit 126 may, for example, extend the map 128 to become the parametric map.

[0139] The electronics unit 126 may further be configured to add information to the parameter map. In particular, the electronics unit 126 may be configured to add further or additional parameters to the parameter map. Thus, the parameter map may be configured to include one or more parameters and information regarding the respective determined positions of the parameters. For example, the parameter map may include information regarding particle density in the air, air temperature, floor humidity, ground humidity, air humidity, atmospheric pressure, battery power, changes in battery power, voltage, current, power demand, gas composition, gas concentrations such as carbon monoxide, carbon dioxide, oxygen, or ozone concentrations, any further parameters, and / or combinations thereof. The electronics unit 126 may further be configured to save the parameter map. In particular, the electronics unit 126 may be configured to save the parameter map, such that the parameter map may be available after resetting and / or charging and / or restarting the autonomous device 110.

[0140] The electronics unit 126 may further be configured to extract statistical information from the parameter map. In particular, the statistical information extracted from the parameter map may be or may include information regarding the distribution of at least one parameter in the parameter map, such as, for example, a distribution pattern of dirt and / or dust in the map. Preferably, the statistical information may be extracted from a parameter map that includes multiple location-related parameter values ​​of the execution of a task by the autonomous device.

[0141] Furthermore, the electronics unit 126 may be configured to store the statistical information. In particular, the electronics unit may be configured to store the statistical information in the statistical map 134 or in the parameter map by adding the statistical information to the statistical map. Thus, as an example, the statistical map may include a location correlation value of at least one statistical information. One embodiment of the statistical map 134 is shown in FIG. 2B. The statistical map 134 including the statistical information may indicate, among other things, where it may be most desirable to perform a task, such as cleaning, on average.

[0142] Further, the electronics unit 126 may be configured to estimate the remaining operating time of the autonomous device 110 and / or the battery of the autonomous device 110. Thus, by way of example, by evaluating one or more of the changes in the state of charge of the battery, the power demand of the motor of the autonomous device 110, or the slippage of the wheels of the autonomous device 110, the current demand of the autonomous device 110 for movement may be derived. By using this information, the electronics unit 126 may estimate or extrapolate, by way of example, how far and / or how long the autonomous device 110 may further operate. Therein, additional information, such as power consumption for household tasks, such as cleaning, may be taken into account for the estimation. At least one further parameter and a parameter map may also be taken into account, for example, to estimate the power consumption for performing the household task. By way of example, by using the parameter map or other information, future power consumption for cleaning a specifically dirty area may be estimated and / or taken into account to estimate the remaining operating time. In general, this estimation allows for planning the remaining operating time and / or operating area of ​​the autonomous device 110.

[0143] Specifically, as shown in FIG. 1, the amount of particles 116 on the door mat 118 adjacent to the front door 120 may be greater than the amount of particles 116 in an adjacent area that is, for example, less used than the door mat 118 adjacent to the front door 120. Specifically, the amount of particles 116 on the door mat 118 may be greater, on average, than the amount of particles 116 in the adjacent area. Thus, in the statistical map 134 shown in FIG. 2B, an area of ​​the door mat 118 in the first room 130, such as a hallway, may be indicated as a high particle area 136. In this manner, the high particle area 136 may specifically indicate an area where it is desirable to vacuum clean more frequently, on average, than other areas in the first room 130.

[0144] Furthermore, statistical information regarding multiple parameters may be included by the statistical map 134. As an example, the statistical map 134 may further include information regarding the air temperature in an area of ​​the map 128, for example the air temperature in the first room 130 and the second room 132. In particular, the air temperature under the window 122 may differ from the air temperature in an adjacent area or an area close to the heating system. Thus, the statistical map 134 may indicate an area proximate to the window 122 as a cold spot 138. As a further example, the statistical map 134 may further include information regarding the air humidity in an area of ​​the map 128. In particular, the air humidity in the second room 132, such as a bathroom, may be higher than the average air humidity in the area of ​​the map 128. Thus, as illustrated in FIG. 2B, the statistical map 134 may indicate an area in the second room 132 as a high humidity area 140.

[0145] The electronics unit 126 illustrated in FIG. 1 may be further configured to adjust the operation of the autonomous device 110 using the statistical information. In particular, the performance of a task by the autonomous device 110 may be adjusted using the statistical map 134. The operation of the autonomous device 110 may be adjusted, for example, such that the identified high particle area 136 in the statistical map 134 may be cleaned more frequently and / or with a higher intensity than areas with lower particle amounts. Thus, the electronics unit 126 may modify the operation of the autonomous device 110, in particular the performance of a vacuum cleaning, such that, for example, the high particle area 136, in particular the area of ​​the door mat 118 in the hallway, may be cleaned more frequently and / or with a higher intensity than the remaining areas. Thus, the autonomous device 110 may perform a quick run, which may include performing a task, for example, vacuum cleaning, only in a predetermined desired area, such as the high particle area 136. For example, the autonomous device 110 illustrated in FIG. 1 may be capable of performing vacuum cleaning only in the area of ​​the door mat 118.

[0146] Further, the electronics unit 126 may be configured to obtain location-related environmental condition information from statistical information. Specifically, the electronics unit 126 may be configured to obtain environmental condition information from the statistical map 134. As an example, as shown in FIG. 2B, statistical information regarding air humidity may indicate an elevated and / or higher risk of mold in the bathroom or second room 132 than in the hallway or first room 130. Furthermore, a combination of statistical information regarding one or more parameters may be used to obtain the location-related environmental condition. For example, statistical information regarding air temperature and / or mold spore density in the air may be further used to indicate the risk of mold. Also, other environmental condition information obtained from the statistical information may include information regarding cellular connection strength, noise level, radiation level, Li-Fi signal strength, Wi-Fi signal strength, baby phone or radio reception strength, or other environmental conditions. Specifically, for example, for a task involving mowing a lawn, environmental condition information may include the condition of the grass or lawn, e.g., the humidity rate of the grass, which may be obtained by assessing the incidence of color, e.g., the incidence of green or brown color in the grass.

[0147] The electronics unit 126 may further be configured to generate recommendation information from the location-related environmental condition information. In particular, the electronics unit 126 may be configured to generate instructions and / or advice regarding actions or practices. In particular, the recommendation information may include beneficial advice to be applied in the area, such as, for example, a recommendation to modify ventilation habits, air conditioning or heating of an area or room in a house or building more frequently, or a recommendation to apply water, fertilizer, insecticide, fungicide, or herbicide to plants, in particular, a recommendation to apply water to grass or lawn. As an example, the recommendation information may include information recommending ventilating the second room 132 to reduce the risk of mold.

[0148] As shown in FIG. 1, the autonomous device 110 may further include an interface 142. The interface 142 may be specifically configured to enable information transfer to an external device 144. In particular, the interface 142 may be configured to transfer statistical information, such as the statistical map 134, and / or location-related recommendation information to the external device 144. The external device 144 may be specifically or include a control network configured to fully or partially implement the recommendation information. As shown in FIG. 1, the external device may be a home control network that controls thermostats, air conditioners, window blinds, water systems, etc. Thus, the recommendation information may be automatically executed by the external device 144.

[0149] Additionally or alternatively, the external device 144 may be or include a user device configured to fully or partially inform the user about the recommended information. For example, the user device may inform the user about the recommended information using visual and / or audio communication. In particular, the user device may be or include a mobile or permanently installed device, such as, for example, a control panel 146 that is part of a home control network, as shown in FIG. 1. The user device may further include a smartphone, a tablet, a laptop, etc.

[0150] In FIG. 3, a flow chart of a method for controlling an autonomous device 110 configured to perform at least one task selected from a household task, a commercial task, and an industrial task is shown. In particular, the method may include controlling the autonomous device 110 shown in FIG. 1. The method includes the following method steps, which may be performed in a given order. However, they may also be performed in a different order. Furthermore, one or more method steps, or all method steps, may be performed once or repeatedly. Furthermore, the method steps may be performed in a time-overlapping manner or in parallel. The method may further include additional method steps not described.

[0151] The method for controlling the autonomous device 110 comprises a first step a) of generating position information using at least one spatial sensor 112 (method step 148). In particular, the spatial sensor 112 shown in FIG. 1 may be used. In particular, the spatial sensor 112 may be used to generate information regarding the spatial position of the autonomous device 110. Additionally or alternatively, the spatial sensor 112 may be used to generate information regarding the spatial position or location of at least one object in the vicinity of the autonomous device 110, such as an object that limits an accessible area of ​​the autonomous device 110. In this way, the object may be or include, for example, a wall, a door, a fence, a sofa, or other type of barrier that limits an accessible area of ​​the autonomous device 110.

[0152] The method further comprises a step b) of determining at least one further parameter using at least one further sensor 114 (method step 150). In particular, the further parameter may be determined using the further sensor 114 as illustrated in FIG. 1. The method comprises a step c) of generating a map 128 using the position information (method step 152). In particular, as an example, the position information may be used to generate the map 128 as illustrated in FIG. 2A. Furthermore, the method comprises a step d) of linking the further parameter to the position information and adding the position of the further parameter to the map 128 (method step 154), thereby generating a parameter map including a position correlation value of the at least one further parameter.

[0153] The method may further include a method step 156 of saving the map 128 and / or the parameter map. In particular, the method step 156 may ensure availability of the map 128 and / or the parameter map after a reset and / or a charging and / or a restart of the autonomous device 110.

[0154] Additionally, the method may include a method step 158 of adding information to the parameter map. Thus, the method may specifically include extending the parameter map by adding additional information to the parameter map.

[0155] The method may further include a method step 160 of extracting statistical information from the parameter map. As shown in Fig. 3, the method step 160 may include four sub-steps. Specifically, the first sub-step (method step 162) may include creating a statistical map 134. In particular, as illustrated in Fig. 2B, the statistical map 134 may be created, which includes position correlation values ​​of the statistical information.

[0156] Method step 160 may have a second sub-step (method step 164) that includes saving the statistical map 134. In particular, method step 164 may ensure availability of the statistical map 134 after a reset and / or charging and / or reboot of the autonomous device 110.

[0157] Furthermore, method step 160 may have a third sub-step (method step 166) which involves averaging the information contained in the parameter map, thereby indicating, for example, a value which may approximate the arithmetic mean of a quantity, such as the average air temperature in the area of ​​the first room 130 and the second room 132 shown in Figures 2A and 2B.

[0158] Additionally, method step 160 may include a fourth sub-step (method step 168) that includes determining anomalies in parameter values ​​and / or correlation locations. Specifically, method step 168 may include determining locations where the determined parameters significantly differ from the average values ​​of the determined parameters, such as high particle areas 136, cold spots 138, and high humidity areas 140 illustrated in FIG. 2B.

[0159] The method may further include a method step 170 including extracting environmental state information from the statistical information. As shown in FIG. 3, the method step 170 may further include two sub-steps. The first sub-step (method step 172) may include creating an environmental map including location correlation values ​​of the environmental state information. Furthermore, the method step 170 may include a second sub-step (method step 174) including storing the environmental map including location correlation values ​​of the environmental state information. In this way, the method step 174 may in particular ensure the availability of the environmental state map after resetting and / or charging and / or restarting the autonomous device 110.

[0160] The method may further include a method step 176 of generating recommendation information from the environmental condition information. In particular, recommendation information may be generated from the environmental condition information if the environmental condition information indicates a desired change in environmental conditions. For example, if it is deemed desirable to reduce the risk of mold, the generated recommendation information may include suggestions for modifying ventilation practices to reduce the risk of mold.

[0161] Additionally, the method may include a method step 178 of transferring information to the external device 144. Specifically, the method step 178 may include transferring information to the external device 144, as shown in FIG 1. By way of example, the information may be or may include maps 128 and / or parameter maps, statistical information, such as statistical maps 134, and / or environmental condition information and / or recommendation information.

[0162] The method may further include a method step 180 of notifying a user about the recommendation information via a user device. In particular, the user device may be the control panel 146 as shown in FIG.

[0163] The method may further include a method step 182 of coordinating operation of the autonomous device 110. In particular, performance of tasks by the autonomous device 110 may be coordinated such that, for example, high particle areas 136 in the statistical map 134 shown in FIG. 2B may be cleaned more frequently than areas with lower particle amounts. [Explanation of symbols]

[0164] 110 Autonomous devices 112 Spatial Sensor 114 More Sensors 116 particles 118 Doormat 120 Entrance Door 122 Window 124 Task Units 126 Electronics Unit 128 Maps 130 Room 1 132 Second Room 134 Statistical Map 136 High Particle Area 138 Cold Spot 140 High Humidity Area 142 Interface 144 External device 146 Control Panel 148 Method step a) of generating position information using at least one spatial sensor 150 Method step b) of determining at least one further parameter using at least one further sensor 152 Method step c) generating a map using location information 154 Method step d) of associating a further parameter with the location information and adding the location of the further parameter to the map. 156 How to save a map and / or a parameter map Steps 158 How to add information to a parameter map Steps 160 How to extract statistics from a parameter map Steps 162 How to Create a Statistical Map Steps 164 How to save a statistical map Steps 166 Method steps for averaging information contained in parameter maps 168 Method steps for determining anomalies in parameter values ​​and / or correlation positions 170 Method steps for extracting environmental state information from statistical information 172 How to Create an Environment Map Step by Step 174 How to Save an Environment Map Steps 176 Steps for generating recommendations from environmental status information 178 Method steps for transferring information to an external device 180 Method steps for notifying a user of recommended information via a user device 182 Method steps for coordinating operation of an autonomous device

Claims

1. An autonomous device (110) configured to perform at least one task selected from a household task, a commercial task, and an industrial task, The autonomous device (110) comprises at least one spatial sensor (112) for generating position information of one or more objects within a circular area in the vicinity of the autonomous device and at least one further sensor (114) for determining at least one further parameter, The autonomous device (110) further comprises at least one task unit (124) configured to perform the domestic and / or commercial and / or industrial tasks, and at least one electronics unit (126), the electronics unit (126) is configured to generate a map (128) using the location information; the area considered for creating the map includes only accessible areas traversed or passed by the autonomous device; the electronics unit (126) is further configured to associate the further parameter with the position information and add the position of the further parameter to the map (128), thereby creating a parameter map having a position correlation value of the at least one further parameter; the electronics unit (126) is configured to add information to the parameter map; The electronics unit (126) is further configured to extract statistical information from the parameter map; The electronics unit (126) is further configured to store the statistical information in a statistical map and / or add the statistical information to the parameter map; the electronics unit (126) is configured to use the statistical information to regulate operation of the tasks of the autonomous device (110); An autonomous device (110) where a probability distribution model is used to obtain the statistical information, the probability distribution model being selected from the group consisting of: normal distribution; Bernoulli distribution; binomial distribution; Poisson distribution; geometric distribution; discrete distribution or continuous uniform distribution.

2. The autonomous device (110) of claim 1, wherein the circular area has a radius of 0 to 5 m.

3. The autonomous device (110) of claim 1 or 2, wherein the electronics unit (126) is further configured to store the parameter map.

4. 4. The autonomous device (110) of claim 3, wherein said at least one further parameter is directly or indirectly linked to said domestic and / or commercial and / or industrial task.

5. The autonomous device (110) of any one of claims 1 to 4, wherein the autonomous device (110) is an autonomous mobile robot selected from the group consisting of: domestic robots; commercial robots; industrial robots.

6. The autonomous device (110) of any one of claims 1 to 5, wherein the electronics unit (126) is configured to obtain location-related environmental state information from the statistical information, and the electronics unit (126) is further configured to generate recommendation information from the location-related environmental state information.

7. The autonomous device (110) of claim 6, wherein the autonomous device (110) has an interface, the interface configured to enable information to be transferred to an external device (144), the information being selected from the group consisting of the map (128); the parameter map; the statistical information; the location-related environmental condition information; and the recommendation information.

8. 8. The autonomous device (110) of claim 7, wherein the external device (144) is selected from the group consisting of a control network configured to fully or partially execute the recommendation information; and a user device configured to fully or partially inform a user of the recommendation information.

9. The autonomous device (110) of any one of claims 1 to 8, wherein the autonomous device (110) is configured to perform an outdoor task.

10. 1. A method for controlling an autonomous device (110) configured to perform at least one task selected from a household task, a commercial task, and an industrial task, comprising: a) generating position information for one or more objects within a circular area in the vicinity of the autonomous device using at least one spatial sensor (112); b) determining at least one further parameter using at least one further sensor (114); c) generating a map (128) using said location information; and d) binding said further parameters to said position information and adding the positions of said further parameters to said map, thereby creating a parameter map comprising position correlation values ​​of said at least one further parameter; the area considered for creating the map includes only accessible areas traversed or passed by the autonomous device; the method further comprises adding information to the parameter map, the method further comprises extracting statistical information from the parameter map, and the method further comprises storing and / or adding the statistical information to a statistical map; The method further includes using the statistical information to adjust operation of the task of the autonomous device (110); A method in which a probability distribution model is used to obtain the statistical information, and the probability distribution model is selected from the group consisting of: normal distribution; Bernoulli distribution; binomial distribution; Poisson distribution; geometric distribution; discrete distribution or continuous uniform distribution.

11. The method of claim 10, wherein the circular area has a radius of 0 to 5 m.

12. The method according to claim 10 or 11, wherein the method includes storing the map and / or the parameter map, thereby providing availability of the map and / or the parameter map after a reset and / or a charging and / or a restart of the autonomous device (110), and the method further includes adding information to the parameter map.

13. 13. The method according to any one of claims 10 to 12, wherein the method further comprises extracting statistical information from the parameter map, the method step of extracting statistical information comprising creating a statistical map comprising position correlation values ​​of the statistical information, the method step of extracting statistical information further comprising saving the statistical map, the method step of extracting statistical information further comprising averaging information contained in the parameter map, and the method step of extracting statistical information further comprising determining anomalies in parameter values ​​and / or correlation positions.

14. 14. The method of claim 13, wherein the method further comprises extracting environmental state information from the statistical information, the method step of extracting the environmental state information comprising creating and / or storing an environmental map comprising location correlation values ​​of the environmental state information.

15. The method of claim 14 , comprising generating recommendation information from the environmental condition information.

16. 16. The method of claim 15, wherein the method includes transferring information to an external device, the information being selected from the group consisting of: the map (128); the parameter map; the statistical information; the environmental condition information; and the recommendation information.

17. 17. A method according to claim 15 or 16, comprising notifying a user of the recommendation information via a user device.

18. A computer program comprising computer executable instructions for carrying out the method according to any one of claims 10 to 17, when the program is run on a computer or a computer network.

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