EP4626289A1

EP4626289A1 - Identifying a category of flooring

Info

Publication number: EP4626289A1
Application number: EP23809640.8A
Authority: EP
Inventors: Bonne Lambert BOONSTRA; Johannes Tseard Van Der Kooi; Mitsy Prada HERREÑO; Jonne Steeman
Original assignee: Versuni Holding BV
Current assignee: Versuni Holding BV
Priority date: 2022-12-02
Filing date: 2023-11-22
Publication date: 2025-10-08
Anticipated expiration: 2043-11-22
Also published as: CN120379575A; WO2024115235A1; EP4378362A1; EP4626289B1

Abstract

A method (600) and system for determining on which of a plurality of categories of flooring, each having a different hardness, a nozzle (111) of a vacuum cleaner (110) is positioned. Data representative of a torque load of a motor that rotates a brush in the nozzle is obtained and processed to generate a trimmed estimator of a parameter that measures variation in the data. A determination that the nozzle is positioned on the softest category of flooring is made in response to the trimmed estimator (620) breaching a predetermined threshold.

Description

IDENTIFYING A CATEGORY OF FLOORING

FIELD OF THE INVENTION

The present invention relates to the field of vacuum cleaners, and in particular to identifying a category of flooring on which a nozzle of a vacuum cleaner is placed.

BACKGROUND OF THE INVENTION

In the field of vacuum cleaners, a significant amount of research is being performed to improve the energy efficiency of a vacuum cleaners. This is particularly important with the increasing use and availability of battery-powered vacuum cleaners (cordless vacuum cleaners), because the runtime, weight and cost of such cleaners heavily depend upon the battery capacity.

To ensure sufficient run times with cordless vacuum cleaners, the suction power and hence air flow rate generated by such cordless vacuum cleaners are usually lower than those of conventional corded vacuum cleaners. To compensate for this decrease in suction power, most cordless vacuum cleaners include a nozzle containing a rotating brush. This increases and optimizes the cleaning performance of a cordless vacuum cleaner to make improved use of the limited amount of energy available in the battery.

In order to meet desired dust pick-up (DPU) requirements, generally more air flow rate or suction power is required on soft floor categories/types compared to hard floors categories/types. To help the consumer to automatically optimize between run time and cleaning performance on different floor categories/types, adaptive vacuum cleaning modes have been introduced in which the suction power and/or rotational speed of the brush is automatically adjusted based on the floor category /type.

It would therefore be desirable to provide a technique that can accurately identify a category /type of flooring on which the nozzle of a vacuum cleaner is positioned.

EP4059402A1 discloses a floor type identifying device for use in a vacuum cleaner. Said device comprises a current sensing unit coupled and a processing and controlling module. When a suction head is moved, a driving current of a roller brush driving motor is detected by the current sensing unit, such that the processing and controlling module judges that the suction head is moved on a specific floor that has a hard surface, a short-pile-carpeted surface or a long-pile-carpeted surface according to a variation of the driving current.

W02016/096046A1 relates to a cleaning device and a method for the cleaning device of detecting a type of a surface over which the cleaning device moves. The method for a cleaning device of detecting a type of a surface over which the cleaning device moves, comprises measuring a drive current of a rotatable cleaning member configured to remove debris from the surface over which the cleaning device moves, comparing a value of the measured drive current with at least one predetermined current value associated with a certain type of surface, and determining if the value of the measured drive current corresponds to the predetermined current value, wherein the surface over which the cleaning device moves is considered to be of said certain type.

EP2457486A2 discloses a robot cleaner includes a main brush to sweep or scatter dust off a floor, a main brush motor to rotate the main brush, a Revolution Per Minute (RPM) detector to detect an RPM of the main brush motor, and a control unit to determine a type of floor according to the RPM of the main brush motor acquired by the RPM detector and control an operation of the robot cleaner based on the determined type of floor. A carpet mode to clean only a carpet area and a hard floor mode to clean a hard floor area excluding the carpet area are given based on detected information relating to the material of a floor, which enables partial cleaning with respect to a cleaning area selected by a user and adjustment in the number of cleaning operations or the intensity of cleaning according to the material of the floor.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention, there is provided a computer-implemented method for determining on which of a plurality of categories of flooring a nozzle of a vacuum cleaner is positioned, the plurality of categories of flooring including a first category of flooring and a second, harder category of flooring, the computer-implemented method comprising: obtaining sensor data responsive to a torque load of a motor of the vacuum cleaner for rotating a brush located in the nozzle of the vacuum cleaner; processing the sensor data to generate a trimmed estimator providing a scale parameter of the sensor data; and determining that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold.

In the context of the present disclosure, the trimmed estimator is a statistical measure of dispersion that does not take account of outliers within the sensor data. Thus, the trimmed estimator is a measure of dispersion within a central portion of the sensor data. The term trimmed estimator is well established in the field of statistical analysis. A scale parameter provides a statistical measure of dispersion, e.g., range, standard deviation or variance.

It will be apparent that the sensor data comprises a plurality or sequence of values representing the torque provided by a motor of the vacuum cleaner over a particular period or window of time. The purpose of the proposed method is to determine or predict whether, during said period/window of time, the nozzle of the vacuum cleaner was located on soft (the first category) or hard (the second category) flooring.

It has been recognized that a variation in the torque provided by the brushrotating motor is greater when the nozzle is positioned on softer flooring compared to hard flooring. This is because the forward and backward movement of the nozzle during use of the vacuum cleaner causes different amounts of force to be applied between the brush and the floor, as a forward motion would increase the force between brush and floor with backward motion decreasing the force between brush and floor. When vacuum cleaning on soft floors such as carpets, the torque load during the stationary phase, forward stroke and backward stroke is significantly different. When vacuum cleaning on a soft floor, the absolute change in the torque load of the motor as the nozzle is moved forward and backward is greater when vacuum cleaning on a soft floor compared to a hard floor. This is because the brush-surface interaction on soft floors is much higher than that of hard floors.

In some examples, the trimmed estimator is a trimmed range of the sensor data.

In some examples, the trimmed estimator is an interquartile range of the sensor data. An alternative label for the interquartile range is the 25% trimmed range. An alternative form of a trimmed range is an interdecile range (i.e., a 40% trimmed range). Other suitable types of trimmed ranges would be apparent to the skilled person (e.g., the 30% trimmed range or the 35% trimmed range).

In some examples, the computer-implemented method is configured to determine that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator reaching or exceeding the first predetermined threshold.

In some examples, the sensor data is a measure of the (electrical) current drawn by the motor to rotate the brush. The current drawn by the motor is proportional to the torque load. An amount of current drawn by the motor is an indicator of the torque applied by the motor of the vacuum cleaner, i.e., of the torque load, and can be easily and accurately measured/monitored. In some examples, the computer-implemented method comprises determining that the nozzle is positioned on the second category of flooring responsive to the trimmed estimator failing to breach the first predetermined threshold.

In some examples, the computer implemented method further comprises, responsive to the trimmed estimator failing to breach the first predetermined threshold: determining, as a percentile value, the value of the sensor data representing a first predetermined percentile of the sensor data; responsive to the percentile value breaching a second threshold, determining that the nozzle is positioned on the first category of flooring; and responsive to the percentile value failing to breach the second threshold, determining that the nozzle is positioned on the second category of flooring.

As the variations in the sensor data are responsive to movement (forwards and backwards) of the nozzle, it is difficult to discriminate between a stationary nozzle on a first category of flooring and a moving/stationary nozzle on a second category of flooring. This embodiment at least partially overcomes this issue by comparing an absolute percentile value to a threshold to discriminate between harder and softer floorings.

This approach is less accurate then making use of variations in the sensor data, as well as being sensitive to different vacuum cleaners and floors. It is therefore less preferred for identifying a category of flooring compared to using variations in the sensor data.

In some examples, the first predetermined percentile is not the 0^th percentile or the 100^th percentile of the sensor data.

In some examples, the first predetermined percentile is the Xth percentile of the sensor data, wherein the value of X is from 10 to 90 and preferably from 25 to 75. Preferably, the predetermined percentile is the 75^th percentile of the sensor data.

In some examples, the computer-implemented method further comprises, responsive to the trimmed estimator breaching the first predetermined threshold, setting the second threshold to be equal to: the value of the sensor data representing a second predetermined percentile of the sensor data; a trimmed mean of the sensor data; or the average of the value of the sensor data representing a third predetermined percentile of the sensor data and the value of the sensor data representing a fourth predetermined percentile of the sensor data.

In other words, the second threshold is set based on sensor data obtained when the nozzle is determined to be on the first category of flooring. The second threshold is thus specific to a particular vacuum cleaner in a particular vacuuming session, improving a reliability of the threshold for distinguishing between the first and second categories of flooring.

In some examples, after setting the second threshold responsive to the trimmed estimator breaching the first predetermined threshold, the value of the second threshold is less than the value of the first predetermined percentile of the sensor data.

There is also proposed a computer-implemented method for controlling the suction power of the vacuum cleaner and/or rotation speed of a brush located in a nozzle of the vacuum cleaner, the computer-implemented method comprising: determining whether the nozzle is positioned on a first category of flooring or a second category of flooring by performing the method described above; and setting the suction power of the vacuum cleaner and/or rotation speed of the brush responsive to the determined category of flooring.

In some examples, the step of setting the suction power and/or rotation speed comprises setting the suction power and/or rotation speed to be higher when it is determined that the nozzle is positioned on the first category of flooring than when it is determined that the nozzle is positioned on the second category of flooring.

There is also provided computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of any of the methods described above.

According to another aspect of the invention, there is provided a processing system for determining on which of a plurality of categories of flooring a nozzle of a vacuum cleaner is positioned, the plurality of categories of flooring including a first category of flooring and a second, harder category of flooring, the processing system being configured to: obtain sensor data responsive to a current drawn by a motor of the vacuum cleaner for rotating a brush located in the nozzle of the vacuum cleaner; process the sensor data to generate a trimmed estimator providing a scale parameter of the sensor data; and determine that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which: Figure 1 illustrates a system, comprising a vacuum cleaner and a processing system for determining on which of a plurality of categories of flooring a nozzle of the vacuum cleaner is positioned, according to an embodiment of the invention;

Figure 2 illustrates example transient motor current data for a nozzle positioned on a soft floor and on a harder floor;

Figure 3 illustrates a set of box plots of motor current data for several different types of flooring;

Figure 4 illustrates example transient motor current data on a hard floor and a soft floor for both a stationary nozzle and a nozzle being moved back and forth;

Figure 5 illustrates a schematic overview of a motor control system for a brushed DC motor, according to an embodiment of the invention; and

Figure 6 illustrates a computer-implemented method for determining on which of a plurality of categories of flooring a nozzle of a vacuum cleaner is positioned, according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

The invention provides a method and system for determining on which of a plurality of categories of flooring, each having a different hardness, a nozzle of a vacuum cleaner is positioned. Data representative of a torque load of a motor that rotates a brush in the nozzle is obtained and processed to generate a trimmed estimator of a parameter that measures variation in the data. A determination that the nozzle is positioned on the softest category of flooring is made in response to the trimmed estimator breaching a predetermined threshold. Since the determination is based on data representative of a torque load of the motor that rotates the brush in the nozzle, the determination may be made at any time when the motor is running, including when the vacuum cleaner is stationary on the flooring. Embodiments are at least partly based on the realization that the interactions between the nozzle brush and the flooring result in very different torque loads for forward strokes and backward strokes of the nozzle when it is on a soft floor, but the torque load of the motor experiences very little variation when the nozzle is on a hard floor.

Illustrative embodiments may, for example, be employed in vacuum cleaners that have a rotating brush in the nozzle, and in particular, in cordless vacuum cleaners with a rotating brush in the nozzle.

Figure 1 illustrates a system 100, comprising a (cordless) vacuum cleaner 110 and a processing system 120 for determining on which of a plurality of categories of flooring 130 a nozzle 111 of the vacuum cleaner is positioned, according to an embodiment of the invention. The plurality of categories of flooring include a first category of flooring and a second, harder category of flooring. In other words, the system may be used to determine whether the nozzle of the vacuum cleaner is positioned on a “soft” floor (e.g. flooring with piles/fabrics, such as carpets) or a “hard” floor (e.g. flooring that does not involve piles or fabric, such as tiled, wooden or laminate flooring). In the context of this specification, a “soft” floor is a category of flooring that experiences a higher brush-floor interaction than a hard floor.

For illustrative purposes, the processing system 120 has been shown as separate to the vacuum cleaner 110 in Figure 1, but the processing system may, in practice, be housed within the vacuum cleaner itself. The processing system 120 is, itself, an embodiment of the invention.

The processing system 120 is configured to obtain sensor data 115 responsive to a torque load of a motor for rotating a brush 112 located in the nozzle 111 of the vacuum cleaner 110. For instance, the sensor data 115 may be a measure of the current drawn by the motor to rotate the brush, which is proportional to the torque load. The current drawn by the motor allows sensor data responsive to the torque load to be obtained easily, by measuring the voltage drop over a shunt resistor located in same circuit as the motor or by using a current sensor IC. In the case of a brush motor that is controlled at a constant torque (i.e. draws a constant current), the sensor data responsive to a torque load of the motor may comprise a measure of the rotational speed to the motor. Other types of sensor data responsive to a torque load of the motor will be apparent to the skilled person, such as a total power drawn by the motor and/or data produced by a torque transducer/sensor.

The sensor data 115 may comprise a data list of a predetermined size, e.g. a list of a predetermined number of motor current values. The processing system 120 may obtain and process the sensor data once the data list is full, and may continue to obtain and process the sensor data each time the data list is updated. Once the data list is full, the oldest entry may be dropped from the data list when a new entry is added to the data list. In other words, the sensor data may comprise a moving window of a sequence of values representative of a torque load of the motor.

Having obtained the sensor data 115, the processing system 120 processes the sensor data to generate a trimmed estimator providing a scale parameter of the sensor data. A scale parameter is a parameter that provides a statistical measure of dispersion (e.g. range, standard deviation or variance). A trimmed estimator is a statistical measure of dispersion that does not take account of outliers within the sensor data, i.e. a measure of dispersion within a central portion of the sensor data.

The trimmed estimator therefore provides a measure of variation in the torque load of the motor (e.g. a measure of variation in the motor current) that is robust against noise/outliers in the sensor data 115. The use of a measure of variation in torque load allows a category of flooring (e.g. “hard” or “soft”) to be determined more reliably, as, unlike absolute values of torque/current, it is less affected by factors such as product-to-product variation (e.g. variation in motors, brush hair stiffness, etc.), wear and pollution (e.g. hair entanglement around the brush etc.).

The variation in the torque load of the motor is much larger when vacuuming on softer floors (e.g. carpets and the like) than on harder floors (e.g. wood, tiles, laminate and the like). When vacuum cleaning on a harder floor, there is little difference between the torque load during forward and backward strokes of the nozzle 111 and while the nozzle is stationary. By contrast, the downward force applied during a forward stroke on a softer floor results in a larger indentation of the brush hairs, increasing the torque load of the motor (and therefore the motor current) compared to when the nozzle is stationary on the softer floor. During a backward stroke on a softer floor, less downward force is applied, and the nozzle is lifted slightly, resulting in a lower torque load (and therefore a lower motor current) compared to when the nozzle is stationary on the softer floor. The higher friction of the brush hairs on softer floors and the higher variation in surface resistance for softer floors also contribute to the larger variations in torque load (and therefore current) on softer floors.

The larger variation in motor current for softer floors is illustrated in Figure 2, which shows example transient motor current data 200 for a nozzle positioned on a soft floor and example transient motor current data 250 for a nozzle positioned on a harder floor. Thus, the trimmed estimator, by providing a measure of variation in the torque load of the motor, may be used to distinguish between harder and softer categories of flooring. The trimmed estimator may, for example, be a trimmed range of the sensor data 115 (i.e. the range of the values in the sensor data after truncating the lowest and highest X% of values, where X is a predetermined number). For instance, the trimmed estimator may be an interquartile range of the sensor data (i.e. X = 25). The skilled person will appreciate that other trimmed ranges may be used, such as the interdecile range (X = 10).

Figure 3 illustrates a set of box plots 300 of motor current data for a motor with a hard floor rotational speed setting for several different types of flooring. Floor #0 is a hard floor, while the other floors are carpets with different thicknesses/type of pile. The height of each box represents the interquartile range of the motor current for each type of flooring. As shown in Figure 3, the interquartile range of the motor current is much smaller for harder flooring types. The interquartile range varies between the different soft floors, according to factors such as how the pile is woven (i.e. closed loop or open).

Returning to Figure 1, the processing system 120 may determine a trimmed range by sorting the values in the sensor data 115 according to the size of the value (i.e. from the smallest value to the largest), determining the Xth percentile and the (100 - X)th percentile, and subtracting the Xth percentile from the (100 - X)th percentile to the determine the trimmed range.

Other suitable trimmed estimators providing a scale parameter of the sensor data 115 will be apparent to the skilled person. For example, the trimmed estimator may be a trimmed variance or a trimmed standard deviation (i.e. a variance or standard deviation of the values in the sensor data after truncating the lowest and highest X% of values, where X is a predetermined number).

The processing system 120 may determine a trimmed variance or standard deviation by sorting the values in the sensor data 115 according to the size of the value, truncating the sensor data by removing a predetermined percentage of values from each end of the sorted sensor data, and calculating the variance or standard deviation of the truncated sensor data.

Having determined the trimmed estimator, the processing system 120 determines which category of flooring 130 the nozzle 111 is positioned on by comparing the trimmed estimator to a threshold value. In particular, the processing system determines that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold. For instance (e.g. if the trimmed estimator is a trimmed range of the motor current), the processing system may determine that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator reaching or exceeding the first predetermined threshold. Alternatively, depending on the type of sensor data and the type of trimmed estimator, the processing system may determine that the nozzle is positioned on the first category of flooring responsive to the trimmed estimator falling below the first predetermined threshold (i.e. in cases where a lower trimmed estimator indicates a greater brush-floor interaction).

The torque loading conditions, and therefore the first predetermined threshold, may depend on the RPM/motor setting of the motor. Thus, in some examples, the first predetermined threshold may be selected from a set of first predetermined thresholds according to the RPM/motor setting of the motor. For instance, if the motor has two RPM settings (one for harder floors, i.e. floors with a lower brush-floor interaction; the other for softer floors, i..e. floors with a higher brush-floor interaction), the set of first predetermined thresholds may comprise a lower threshold for use when the motor is on the low RPM setting for harder floors (having a lower brush-floor interaction) and a higher threshold for use when the motor is on the high RPM setting for softer floors (having a higher brush-floor interaction) . The processing system 120 may determine which RPM setting the motor is on, and select the first predetermined threshold from the set responsive to the determined RPM setting.

Suitable values for the first predetermined threshold for each RPM setting will depend on various factors, including the supply voltage of the motor, the stiffness of the brush tuft, the brush tuft density and nominal indentation. For example, in the case of a motor with a supply voltage that varies between 28.8 V and 21 V, if the trimmed estimator is an interquartile range of motor current values, a first predetermined threshold in the range of 80 mA to 120 mA (e.g. 100 mA) may be used when the motor has a low RPM (i.e. is in a “hard floor setting”) and a first predetermined threshold in the range of 180 mA to 220 mA (e.g. 200 mA) may be used when the motor has a higher RPM (i.e. is in a “soft floor setting”). The skilled person will readily understand how to determine suitable threshold values for a particular supply voltage/nozzle set-up.

In some examples, the processing system 120 may determine that the nozzle 111 is positioned on the second (harder) category of flooring responsive to the trimmed estimator failing to breach the first predetermined threshold. In other words, the determination as to which category of flooring the nozzle is positioned on may simply depend on whether or not the trimmed estimator breaches the first predetermined threshold. The use of a single threshold for the trimmed estimator to determine whether the nozzle 111 is positioned on a floor belonging to the first (softer) category or the second (harder) category provides an accurate determination of the category of flooring 130 when the nozzle of the vacuum cleaner is being moved back and forth. However, if the nozzle is stationary on a softer carpet, the sensor data 115 does not exhibit the high variation caused by stroking movement. This means that the processing system 120 may inaccurately determine that the nozzle is positioned on the second category of flooring (and may adjust the rotational speed accordingly, as described below) when the nozzle is actually on the first category of flooring, but stationary.

The difference in torque load between stationary and moving (with forward and backward strokes) nozzles is shown in Figure 4, which illustrates example transient motor current data on a hard floor and a soft floor for both a stationary nozzle and a nozzle being moved back and forth. Graph 410 shows the motor current signal for a moving nozzle on a hard floor; graph 420 shows the motor current signal for a stationary nozzle on a hard floor; graph 430 shows the motor current signal for a moving nozzle on a soft floor; and graph 440 shows the motor current signal for a stationary nozzle on a soft floor.

As shown in Figure 4, the motor current signal is relatively low and experiences relatively little variation when the nozzle is positioned on a hard floor, regardless of whether the nozzle is moving (graph 410) or stationary (graph 420). When the nozzle is positioned on a soft floor, the motor current signal is relatively high for both moving and stationary nozzles, due to the higher torque load, but the variation in the current signal is very different depending on whether the nozzle is moving or stationary. The motor current signal experiences a relatively large amount of variation when the nozzle is moving (graph 430), but the variation in the motor current signal for the stationary nozzle on the soft floor (graph 440) is similar to the variation for the nozzle on the hard floor.

Returning to Figure 1, if the processing system 120 incorrectly identifies the nozzle 111 as being on a hard floor each time the nozzle stops moving back and forth on a soft floor, and immediately adjusts the rotational speed of the motor accordingly whenever the nozzle is stationary for a few seconds or less, this would result in “nervous” behavior of the nozzle.

In some examples, the processing system may be configured to monitor a movement of the nozzle (e.g., using accelerometer data or the like) and avoid or prevent use of the proposed approach for determining which category of flooring is in use whilst the nozzle is stationary, e.g., whilst a movement is below a predetermined movement threshold. Alternatively and preferably, in response to the trimmed estimator failing to breach the first predetermined threshold, the processing system 120 may further process the sensor data 115 to determine or predict whether the nozzle 111 is on the second (harder) category of flooring or is stationary (or near-stationary) on the first category of flooring.

For example, responsive to the trimmed estimator failing to breach the first predetermined threshold, the processing system 120 may determine, as a percentile value, the value of the sensor data representing a predetermined percentile of the sensor data. Since the torque load is higher for softer floors than harder floors, the percentile value will be higher when the nozzle is stationary on a softer floor than when the nozzle is on a harder floor.

The processing system 120 may therefore determine whether the nozzle 111 is positioned on the first category of flooring (despite the failure of the trimmed estimator to breach the first predetermined threshold) or on the second category of flooring by comparing the percentile value to a second threshold. In other words, the processing system may determine that the nozzle is positioned on the first category of flooring responsive to the percentile value breaching the second threshold, and that the nozzle is positioned on the second category of flooring responsive to the percentile value failing to breach the second threshold.

Preferably, the predetermined percentile for which a percentile value is determined is not the 0^th percentile or the 100^th percentile of the sensor data. The predetermined percentile may, for example, be the Xth percentile, where X is in the range from 10 to 90. Preferably X is in the range from 25 to 75. For instance, the predetermined percentile may be the 75^th percentile (i.e. the third quartile) of the sensor data.

A suitable value for the second threshold may vary between vacuum cleaners, and for a particular vacuum cleaner, may vary according to wear and contamination (e.g. hairs entangled in the brush), and between different soft floors. Therefore, the second threshold is preferably a self-learned threshold that is defined/updated during each vacuuming session. In particular, the second threshold may be determined based on sensor data obtained while the nozzle is moving on a particular soft floor (i.e. when the variation in the sensor data clearly indicates that the nozzle is on the first category of flooring).

For example, the processing system 120 may, responsive to the trimmed estimator breaching the first predetermined threshold (i.e. when the nozzle 111 is moving on a softer floor), set the second threshold to be equal to the value of the sensor data representing a second predetermined percentile of the sensor data. The second predetermined percentile should be lower than the first predetermined percentile, so that the value of the second threshold is less than the value of the first predetermined percentile of the sensor data. In other words, the second threshold should be set so that the sensor data obtained while the nozzle is moving on the first category of flooring has a percentile value that breaches the second threshold, in order that the second threshold is capable of distinguishing between the categories of flooring.

In other examples, the processing system 120 may, responsive to the trimmed estimator breaching the first predetermined threshold, set the second threshold to be equal to a trimmed mean of the sensor data (i.e. a mean of the sensor data after truncating the lowest and highest X% of values, where X is a predetermined number), or to the average of the value of the sensor data representing a third predetermined percentile of the sensor data and the value of the sensor data representing a fourth predetermined percentile of the sensor data. For instance, the second threshold may be an average of the first quartile and the third quartile of the sensor data, using sensor data for which the trimmed estimator breaches the first predetermined threshold. Again, the second threshold should be set so that the sensor data obtained while the nozzle is moving on the first category of flooring has a percentile value that breaches the second threshold.

In some examples, having determined whether the nozzle 111 of the vacuum cleaner 110 is positioned on a first category of flooring or a second category of flooring as described above, the processing system 120 sets the suction power and/or rotation speed of the brush 112 located in the nozzle responsive to the determined category of flooring. In particular, the suction power and/or rotation speed may be set to be higher when it is determined that the nozzle is positioned on the first (softer) category of flooring than when it is determined that the nozzle is positioned on the second (harder) category of flooring.

In other words, the processing system 120 may form part of a motor control system. The motor control system regulates the rotational speed of the motor for rotating the brush to maintain a desired cleaning performance. When a brushless DC motor is used to rotate the brush, the rotational speed is monitored by the motor controller. However, brushed DC motors are more commonly used due to their lower cost. Brushed motors require additional means for monitoring the rotational speed.

Figure 5 illustrates a schematic overview of a (closed-loop) motor control system 500 for a brushed DC motor, according to an embodiment of the invention. The motor control system determines a measure for the rotational speed of the brush by periodically stopping power supply to the motor for a short time (e.g. less than a millisecond), and measuring the back-emf voltage during this time. The back-emf voltage is then used as a measure for the rotational speed of the brush. The motor control system uses the feedback information about the rotational speed to operate a closed-loop system that ensures the rotational speed of the motor corresponds to the RPM setpoint.

The motor current is measured by measuring the voltage drop across a shunt resistor or by using a current sensor IC, and a computer-implemented method is used to determine on which category of flooring the nozzle of the vacuum cleaner is positioned, as described above. The RPM setpoint for the motor may then be set in response to the determined category of flooring.

For example, the vacuum cleaner may be configured to start a vacuuming session in a hard floor state, i.e., when turned on, the vacuum cleaner initially has a low RPM and aggregate (fan and motor assembly) power setpoint. Once sufficient sensor data has been obtained, a determination as to the category of flooring on which the nozzle is positioned is made. If it is determined that the vacuum cleaner is positioned on the second (harder) category of flooring, the vacuum cleaner may continue with the low RPM and aggregate power setpoint.

In response to a determination that the vacuum cleaner is positioned on the first (softer) category of flooring, either because the vacuum cleaner started on a softer floor or because the vacuum cleaner has transitioned from a harder floor to a softer floor, the RPM and aggregate power setpoint may be adjusted to a higher setting. The RPM and aggregate power setpoints for the first and second categories of flooring may be predetermined, and the setpoint may be set by selecting from the predetermined setpoints according to the determined category of flooring.

Similarly, if the vacuum cleaner is operating in a soft floor state (i.e. with the high RPM and aggregate power setpoint for the first category of flooring), the RPM and aggregate power setpoint may be adjusted to the lower setting in response to a determination that the vacuum cleaner is positioned on the second (harder) category of flooring.

When the RPM setpoint is changed from a lower setting to a higher setting (or vice versa), the brush rotational speed error increases, and the motor control system adjusts the output (PWM duty cycle) to minimize the error.

While the motor is ramping up or down to correct the rotational speed, the sensor data will not be representative of the category of flooring, due to the torque the motor is generating to accelerate or decelerate the brush. Thus, in some examples, sensor data 115 may not be obtained or processed during a predetermined ramping period immediately following a change in RPM setpoint. A ramp counter may be used to ensure that no sensor data is obtained/processed during the ramping period. Once the ramping period is ended, sensor data may continue to be obtained and processed to determine the category of flooring. Preferably, a data list containing sensor data values is emptied in response to a change of setpoint, so that the determination of the category of flooring is made using only sensor data obtained after the new setpoint has been reached.

In examples in which a second threshold is used to distinguish between a nozzle on the second (harder) category of flooring and a stationary nozzle on the first category of flooring, the processing system 120 may, in response to the trimmed estimator failing to breach the first predetermined threshold and the percentile value breaching the second threshold (i.e. in response to determining that the nozzle is stationary on the first category of flooring) keep the RPM setpoint at the higher setting unless and until a time for which the nozzle has been stationary exceeds a predetermined period. In response to a determination that the time for which the nozzle has been stationary exceeds a predetermined period, the processing system may adjust the RPM to the lower setting, in order to reduce damage to the flooring and to increase the run time of the battery. The predetermined period may be in the range of 5 seconds to 30 seconds.

Figure 6 illustrates a computer-implemented method 600 for determining on which of a plurality of categories of flooring a nozzle of a vacuum cleaner is positioned, according to an embodiment of the invention. The plurality of categories of flooring including a first category of flooring and a second, harder category of flooring.

The computer-implemented method 600 may be carried out by any kind of computer, including digital, analog and mechanical computers. For example, the method 600 may be carried out by the processing system 120 described above.

The computer- implemented method 600 begins at step 610, at which sensor data responsive to a torque load of a motor of the vacuum cleaner for rotating a brush located in the nozzle of the vacuum cleaner is obtained.

At step 620, the sensor data is processed to generate a trimmed estimator providing a scale parameter of the sensor data.

At step 630, the nozzle is determined to be positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold.

It will be understood that the disclosed methods are computer-implemented methods. As such, there is also proposed a concept of a computer program comprising code means for implementing any described method when said program is run on a processing system. The skilled person would be readily capable of developing a processing system for carrying out any herein described method. Thus, each step of a flow chart may represent a different action performed by a processing system, and may be performed by a respective module of the processing system.

As discussed above, the system makes use of a processing system to perform the data processing. The processing system can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. The processing system typically employs one or more microprocessors that may be programmed using software (e.g. microcode) to perform the required functions. The processing system may be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

Examples of circuitry that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). Thus, the processing system may be embodied as a digital and/or analog processing system.

In various implementations, the processing system may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processing systems and/or controllers, perform the required functions. Various storage media may be fixed within a processing system or controller may be transportable, such that the one or more programs stored thereon can be loaded into a processing system.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

Functions implemented by a processing system may be implemented by a single processing system or by multiple separate processing units which may together be considered to constitute a "processing system". Such processing units may in some cases be remote from each other and communicate with each other in a wired or wireless manner.

The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

If the term "adapted to" is used in the claims or description, it is noted the term "adapted to" is intended to be equivalent to the term "configured to". If the term "arrangement" is used in the claims or description, it is noted the term "arrangement" is intended to be equivalent to the term "system", and vice versa.

Any reference signs in the claims should not be construed as limiting the scope.

Claims

CLAIMS:

1. A computer-implemented method (600) for determining on which of a plurality of categories of flooring a nozzle (111) of a vacuum cleaner (110) is positioned, the plurality of categories of flooring including a first category of flooring and a second, harder category of flooring, the computer-implemented method (600) comprising: obtaining sensor data (610) responsive to a torque load of a motor of the vacuum cleaner (110) for rotating a brush (112) located in the nozzle (111) of the vacuum cleaner (100); characterized by processing the sensor data (115) to generate a trimmed estimator (620) providing a scale parameter of the sensor data (115); and determining that the nozzle (111) is positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold.

2. The computer-implemented method (600) of claim 1, wherein the trimmed estimator is a trimmed range of the sensor data (115).

3. The computer-implemented method (600) of claim 2, wherein the trimmed estimator is an interquartile range of the sensor data (115).

4. The computer-implemented method (600) of any of claims 1 to 3, wherein the computer- implemented method (600) is configured to determine that the nozzle (111) is positioned on the first category of flooring responsive to the trimmed estimator reaching or exceeding the first predetermined threshold.

5. The computer-implemented method (600) of any of claims 1 to 4, wherein the sensor data is a measure of the current drawn by the motor to rotate the brush (112).

6. The computer-implemented method (600) of any of claims 1 to 5, wherein the computer- implemented method (600) comprises determining that the nozzle (111) is positioned on the second category of flooring responsive to the trimmed estimator failing to breach the first predetermined threshold.

7. The computer-implemented method (600) of any of claims 1 to 5, wherein the computer implemented further comprises, responsive to the trimmed estimator failing to breach the first predetermined threshold: determining, as a percentile value, the value of the sensor data (115) representing a first predetermined percentile of the sensor data (115); responsive to the percentile value breaching a second threshold, determining that the nozzle (111) is positioned on the first category of flooring; and responsive to the percentile value failing to breach the second threshold, determining that the nozzle (111) is positioned on the second category of flooring.

8. The computer-implemented method (600) of claim 7, wherein the first predetermined percentile is not the 0^th percentile or the 100^th percentile of the sensor data (115).

9. The computer-implemented method (600) of claim 8, wherein the first predetermined percentile is the Xth percentile of the sensor data (115), wherein the value of X is from 10 to 90 and preferably from 25 to 75.

10. The computer-implemented method (600) of any of claims 7 to 9, further comprising, responsive to the trimmed estimator breaching the first predetermined threshold, setting the second threshold to be equal to: the value of the sensor data (115) representing a second predetermined percentile of the sensor data; a trimmed mean of the sensor data (115) ; or the average of the value of the sensor data (115) representing a third predetermined percentile of the sensor data (115) and the value of the sensor data (115) representing a fourth predetermined percentile of the sensor data (115).

11. The computer-implemented method (600) of claim 10, wherein, after setting the second threshold responsive to the trimmed estimator breaching the first predetermined threshold, the value of the second threshold is less than the value of the first predetermined percentile of the sensor data (115).

12. A computer-implemented method (600) for controlling the suction power of the vacuum cleaner (110) and/or rotation speed of a brush (112) located in a nozzle (111) of the vacuum cleaner (110), the computer-implemented method (600) comprising: determining whether the nozzle (111) is positioned on a first category of flooring or a second category of flooring by performing the method of any of claims 6 to 11; and characterized by setting the suction power of the vacuum cleaner (110) and/or rotation speed of the brush (112) responsive to the determined category of flooring.

13. The computer-implemented method (600) of claim 12, wherein the step of setting the suction power of the vacuum cleaner (110) and/or rotation speed comprises setting the suction power of the vacuum cleaner (110) and/or rotation speed to be higher when it is determined that the nozzle (111) is positioned on the first category of flooring than when it is determined that the nozzle (111) is positioned on the second category of flooring.

14. A computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps of the method according to any of claims 1 to 13.

15. A processing system for determining on which of a plurality of categories of flooring a nozzle (111) of a vacuum cleaner (110) is positioned, the plurality of categories of flooring including a first category of flooring and a second, harder category of flooring, the processing system being configured to: obtain sensor data responsive to a current drawn by a motor of the vacuum cleaner (110) for rotating a brush (112) located in the nozzle (111) of the vacuum cleaner (110); characterized by process the sensor data to generate a trimmed estimator (620) providing a scale parameter of the sensor data; and determine that the nozzle (111) is positioned on the first category of flooring responsive to the trimmed estimator breaching a first predetermined threshold.