DE102012015188A1 - Actively or passively driven wheel for e.g. muscle power or motor operated locomotion unit in automobile field, has power generation device connected with sensor and interface for supplying sensor and interface with electrical power - Google Patents

Abstract

The wheel has an odometry sensing unit (12) comprising a three dimensional (3D)-movement sensor (20). A radio data communication interface (32) is connected with the 3D-movement sensor according to a communication protocol. A power generation device (26) generates electrical power from independent movement during movement of the wheel. The power generation device is connected with the 3D-movement sensor and the radio data communication interface for supplying the 3D-movement sensor and the radio data communication interface with the electrical power. Independent claims are also included for the following: (1) a system comprising a locomotion unit and a base device (2) a method for drop recognition in a locomotion unit (3) a method for substrate classification by a wheel (4) a method for increasing the precision of an overall location process.

Description

The present invention relates to an actively or passively driven wheel with an odometry sensor unit, a musculoskeletal or motorized means of locomotion, a system, a method for fall detection in a means of locomotion, a method for fall detection in a means of locomotion of a system, a method for background classification and a Method for increasing the precision of a global localization method.

By means of locomotion means, for example, a wheelchair, walker, scooter, bicycle, tricycle, shopping cart, but also a trailer and the entire automotive sector.

Assistants for navigation on roads and paths for motorists, but also cyclists and pedestrians, today mainly use global positioning systems such as GPS or general GNSS (Global Navigation Satellite System) to align them with precise location information in electronically available maps and thus provide navigation instructions can. The positioning information may be disturbed by tall buildings in street canyons, mountains or the like. Even with support from additional transmitters with known locations (differential GPS), it is accurate only in the meter range. It has a global measurement inaccuracy. The measurement of the self-motion of a vehicle (odometry of speed and steering angle) can be used independently to calculate a path over time. It has a measuring inaccuracy with the path length (drift). If now the global positioning information is merged with the odometry (sensor fusion), then the precision of navigation software can be significantly improved by comparing the independent measuring methods with different sensor-specific inaccuracies.

Odometry data can be calculated from comparison with the (possibly known) passing environment detected (for example by laser scanners), by self-movement of a pedestrian (for example by step counting) or by measuring the rotation of a wheel, from which the speed of a vehicle and from the comparison of the respective speed of the wheels of a pair (for example, front or rear wheels), the steering angle can be derived.

Although conventional odometry sensors on wheels are technically relatively simple (for example, reed contacts), they generally require cabling to an evaluation unit.

The invention is therefore based on the object, a position determination by means of odometry optionally in conjunction with the dead reckoning in a muscle-powered or motor-powered (ground-based) means of locomotion with an odometry sensor unit without cabling the same with an evaluation unit.

According to the invention, this object is achieved by an actively or passively driven wheel having an odometry sensor unit, wherein the sensor unit is a 3D motion sensor; a radio communication interface connected to the 3D motion sensor according to a transmission protocol; and energy generating means for generating electrical energy from self-motion upon movement of the wheel connected to the 3D motion sensor and the radio communication interface for supplying the same with electrical energy.

Furthermore, this object is achieved by a muscle-powered or motor-driven means of locomotion, comprising at least one wheel according to one of claims 1 to 12.

In addition, the present invention provides a system comprising a means of locomotion according to claim 13 and a base unit comprising a radio data transmission interface according to the same transmission protocol as the radio data transmission interface of the at least one wheel for communication with the sensor unit of the at least one wheel, a GNSS sensor, in particular a GPS sensor an evaluation device, which is designed to use the sensor unit of the at least one wheel and / or data supplied by the base unit, the position and / or the steering direction and / or the position and / or the ground and / or an impact or overthrow of the means of locomotion and / or the tire pressure of the wheel, and having a display or output device for displaying or outputting the evaluation result. The base device may be, for example, a mobile communication device, such as a mobile phone, especially a smartphone, or, for example, a tablet PC.

In addition, there is provided a method for fall detection in a means of locomotion according to claim 13, comprising for at least one wheel determining the magnitude of the acceleration component acc _z in a direction perpendicular to the wheel plane direction by means of the associated 3D motion sensor, assuming a fall when the magnitude of the acceleration component acc _z or the amount of the acceleration component acc _z resulting from a data fusion corresponds to the gravitational acceleration g, and outputting an alarm by means of an alarm device in the sensor unit or in a separate base unit, in particular a system according to claim 13, which also includes the means of transportation.

Further, the present invention provides a method for detecting a fall in a means of locomotion of a system according to claim 18, comprising for at least one wheel determining the values of the mutually perpendicular acceleration components acc _x and acc _y in the wheel plane by means of the associated 3D motion sensor, determining the values of each other vertical acceleration components acc _x and acc _y in the wheel plane by means of the base unit, determining the position by means of a fusion of odometry and determined by a global positioning method position data and the ground condition based on the merged position data, fusion of all determined values of the acceleration components and the determined ground condition and determination a fall based on the merged data.

In addition, there is provided a method for subclassing by means of at least one wheel according to one of claims 1 to 12, in particular a locomotion according to claim 13, comprising determining the length of the acceleration vector in the wheel plane as a function of time by measuring the mutually perpendicular acceleration components acc _x and Acc _y in the wheel plane by means of the associated 3D motion sensor in discrete, preferably equidistant, time intervals, preferably linear interpolation of the time course of the length of the acceleration vector, determining the frequency spectrum of the acceleration occurred over the distance covered by the wheel by transforming the interpolated time course in the Frequency space, comparison of the determined frequency spectrum with reference spectra for different backgrounds and classifying the background based on the result of the comparison.

Finally, there is also provided a method of increasing the precision of a global location method by means of a system as claimed in any one of claims 14 to 17, comprising merging values of acceleration components of the at least one wheel, odometry information regarding the vehicle to be refined, GNSS information, and classifying ground and using a particulate filter to estimate the current position of the vehicle on the basis of the merged data.

According to a particular embodiment of the wheel, the sensor unit also comprises a 3D position sensor which is also supplied with electrical energy by the energy generating device and is connected to the radio data transmission interface. The bearing sensor may result, for example, from a combination of acceleration and yaw rate sensors.

Alternatively or additionally, the sensor unit can also comprise a temperature sensor, which is also supplied with electrical energy by the energy generating device and is connected to the radio data transmission interface.

According to a particular embodiment, the wheel has a rim.

Furthermore, it is conceivable that the wheel has a pneumatic tire. In particular, the sensor unit may also include a tire pressure sensor.

Alternatively, the wheel may have a rubber tire.

According to a particular embodiment of the invention, the sensor unit is arranged or mounted in the pneumatic tire.

Alternatively, the sensor unit is disposed or mounted in the rubber tire.

Again alternatively, the sensor unit is arranged or mounted on or in the rim.

Advantageously, the sensor unit comprises a data processing device, which is connected upstream of the radio data transmission interface and is likewise supplied with electrical energy by the energy generating device.

Advantageously, the sensor unit comprises an energy management device, which is connected downstream of the energy generating device.

In the system can be provided that the base unit further comprises a position sensor and / or a compass.

Alternatively or additionally, the base unit may further comprise a 3D motion sensor.

Finally, it can be provided that the base unit also has a database with a digital map and / or with background reference data or is connected or connectable to an external database with a digital map and / or with background reference data.

The invention is based on the surprising finding that by providing an energy generating device for generating Electrical energy from self-motion during movement of the wheel, ie by creating a self-powered (energy self-sufficient by so-called "Energy Harvesting") sensor unit and by providing a wireless data transmission interface no cables, etc. are required, the sensor unit can be easier to assemble and replaced no batteries / rechargeable batteries Need to become.

Furthermore, by means of at least in a particular embodiment of the present invention, the equipment expense can be reduced. Only self-powered sensors and existing devices, such as a smartphone, are required.

The invention can be used, for example, on rollators and wheelchairs. It can also be retrofitted. This must or must be replaced depending on the embodiment, one or more wheels.

Further features and advantages of the invention will become apparent from the appended claims and the following description in which several embodiments are explained in detail with reference to the schematic drawings. It shows

1 a plan view of an active or passive driven wheel according to a particular embodiment of the invention in use;

2 a block diagram of an odometry sensor unit of the wheel of 1 ; and

3 Details of a method for increasing the precision of a global localization method according to a particular embodiment of the invention.

1 shows an active or passive driven wheel 10 a means of transport, z. B. wheelchair, with an odometry sensor unit 12 , The wheel 10 has four spokes 14 , a rim 16 as well as a tire 18 , for example pneumatic or rubber tires. The odometry sensor unit 12 is on one of the spokes 14 near the rim 16 appropriate. For power generation by self-motion, the farther the sensor unit is, the better 12 towards the periphery of the wheel 10 is arranged.

In 1 are also the weight F _G , the force F _V (a dependent on the nature of the ground underway force) and the centrifugal force F _Z drawn. Furthermore, the mutually perpendicular acceleration components acc _x and acc _{y are shown} in the wheel plane.

As is clear from the 2 results, the sensor unit 12 a 3D motion sensor 20 , a 3D position sensor 22 , a temperature sensor 24 , an energy generating device 26 to generate electrical energy from self-motion during movement of the wheel 10 , one of the power generation facility 26 downstream energy management facility 28 that with the 3D motion sensor 20 , 3D position sensor 22 and temperature sensor 24 for supplying the same is connected to electrical energy, a data processing device 30 in the form of a microprocessor / microcontroller / computer connected to the sensors 20 to 24 connected via one or more data lines and also connected to the power management device for supplying electrical energy, and one of the data processing device 30 downstream radio communication interface 32 in accordance with a transmission protocol also with the power generation facility 26 is connected to the supply of electrical energy (energy flow: dashed arrows, data flow: solid arrows). The transmission protocol may, for example, be a transmission protocol for short-range radio transmission (approximately 2 to 4 meters) for devices with low energy consumption, for example according to the Bluetooth 4.0 standard. The radio communication interface 32 serves for the transmission of unprocessed and / or by means of the data processing device 30 processed data of the 3D motion sensor 20 , 3D position sensor 22 and temperature sensor 24 to an external base unit (not shown) of one system and / or the data to and from another sensor unit (not shown) of another wheel (not shown).

The base unit may be, for example, a commercially available smartphone or a tablet PC and have a radio data transmission interface according to the same transmission protocol, a GPS sensor or general GNSS sensor for localization plus a position sensor and a compass.

Typically, the processing of the data occurs in conjunction with multiple odometry sensor units (typically arranged on two parallel wheels) with the base unit. During the communication of the odometry sensor units with one another and with the base unit, sensor raw data, but especially derived information in each case flow in both directions in order to enable each sensor unit to achieve optimal results by combining as much information as possible (sensor fusion) on the other hand by distributing the data processing and as intelligent as possible pre-processing of the data in the odometry sensor units one to achieve low data flow (in both the data volume and the data rate) in order to save energy.

• – Berechnung der Lenkrichtung aus dem Vergleich der Beschleunigung und Lage in 3D von zwei Odometrie-Sensoreinheiten auf zwei parallelen Rädern,
• – Untergrundklassifizierung auch im Vergleich der Information von zwei Rädern,
• – Lokalisation über Untergrundklassifizierung der Räder zusammen mit der Pose (Ortsbestimmung über GPS oder allgemein GNSS und Ausrichtung über Kompass) aus dem Basisgerät,
• – Lokalisation über Untergrundklassifizierung der Räder sowie Pose und (Schräg-)Lage aus dem Basisgerät im Vergleich mit der für den Ort in einer Karte hinterlegten Referenzinformation aus lokalen Daten im Basisgerät bzw. über das Basisgerät eingeholten Informationen aus externen Datenbanken,
• – Werte bzw. Vereisungsverhandlung über Temperaturmessung und Untergrundklassifizierung der Räder sowie Schräglage aus dem Basisgerät, gegebenenfalls im Vergleich mit Referenzinformation,
• – Sturzerkennung aus dem Vergleich der Beschleunigung und Lage in 3D von zwei Rädern sowie dem Basisgerät.

In the sensor fusion, data can be merged for intelligent preprocessing:

• Calculation of the steering direction from the comparison of the acceleration and position in 3D of two odometry sensor units on two parallel wheels,
• - Underground classification also in comparison of the information of two wheels,
• - localization via ground classification of the wheels together with the pose (localization via GPS or general GNSS and compass orientation) from the base unit,
• - Localization via background classification of the wheels and pose and (oblique) position from the base unit in comparison with the reference information stored for the location in a map from local data in the base unit or information obtained from external databases via the base unit,
• - Values or icing negotiation via temperature measurement and background classification of the wheels as well as inclination from the base unit, if necessary in comparison with reference information,
• - Fall detection from the comparison of the acceleration and position in 3D of two wheels and the base unit.

Falls can basically be detected via the evaluation of mutually perpendicular acceleration components acc _x , acc _y and acc _z . In this case, it is particularly easy to detect those falls in which the means of locomotion remain on one of the wheel surfaces after the fall and the value of acc _z , ie = | acc _z |, corresponds to the gravitational acceleration g. This case does not occur in normal operation since acc _{z is} always close to zero when rolling the wheel. Slight deviations can only be caused by unevenness and noise.

Falls are more difficult to detect, in which the means of transportation comes to a standstill in another situation, for example, when tilting forward or backward. For this purpose, a fusion of the values of the acceleration components acc _x and acc _y with the acceleration data of the base unit and with the information about the background condition (background classification) from the position data is useful to distinguish a fall from a roughness of the ground.

• – jedes a(t) einer geeigneten Tiefpassfilterung unterzogen wird, um dem individuellen Rauschen des gewählten Beschleunigungssensors entgegen zu wirken, und
• – jedes a(t) durch das Quadrat der zum Zeitpunkt der Aufnahme gegebenen Rotationsgeschwindigkeit des Rades geteilt wird, um F_Z näherungsweise zu normalisieren.

Hereinafter, a method for subsurface classification according to a particular embodiment of the present invention based on the 1 and 2 be explained. The odometry sensor unit 12 Measures at intervals of δt to the previous measurement the forces acting on them, resulting from the accelerations acc _x and acc _y , while the wheel 10 a total distance with the length s = s _n - s ₀ rolls. The length of the vector a (t) = | acc _x (t), acc _y (t) | Thus, at any time t from [t ₀ , t _n ] describes the combination of the at that time on the sensor unit 12 acting forces F _Z (centrifugal force), F _G (gravitational force) and F _V (a force dependent on the nature of the terrain). It should be noted that

• - Each a (t) is subjected to a suitable low-pass filtering to counteract the individual noise of the selected acceleration sensor, and
• - Each a (t) is divided by the square of the given at the time of recording rotation speed of the wheel to approximately normalize F _{Z Z.}

The vector A (t) = | a (t ₀ ), ... a (t _n ) |, whose individual components were recorded equidistant in time, is in the next step by means of linear interpolation in the vector A '(s) = | a '(s ₀ ), ... a' (s _n ) | transferred. The components of A '(s) are spatially equidistant, that is, they are the ones on the odometry sensor unit 12 describe acting forces, in each case after the wheel is rolled a further distance δs. Thereafter, A '(s) is subjected to a discrete Fourier transformation. The resulting vector B = | b ₀ , ..., b _{n / 2} | describes the frequency spectrum of the over the total distance s occurred forces. For example, for a distance traveled s, which corresponds to four times the length of the wheel circumference, in B a meaningful maximum at 4 Hz can be seen (F _Z and F _G add four times over s). The signature required for the purpose of subclass classification is given by B. In particular, structural features of the subsurface, such as track pavement with slabs of width λ in B at location s / λ can be detected.

If now the description of any, but firmly chosen, background is understood as the set of signatures that were recorded on this background (learning phase), a single signature in the following recognition phase can be assigned to the type of the underlying background. The k-nearest-neighbor method is used for this. Here, the distances between the signature to be classified and all signatures of the available background descriptions are calculated. Finally, it is determined from the k nearest distances which background description was most frequently involved. Their type is output as currently detected background.

It should be noted that any background description (subclass classification) is valid only for the combination of driver and vehicle (means of locomotion) with which the underlying signatures were also recorded (learned). Reasons for this restriction are to be found in the different vibration behavior in different vehicles and significantly different payloads.

Based on 3 Now, a method for increasing the precision of a global location method according to a particular embodiment of the present invention will be described. The global localization method may be, for example, GPS, Galileo, etc. The process evaluates a range of unprocessed and derived information. The data used includes background classification (see above), values of the acceleration components, for example the odometry sensor unit 12 , the odometry information provided by it as well as the GNSS information to be specified itself.

The method uses a classic particle filter. This presupposes that the environment in which the vehicle (vehicle) moves is sufficiently precisely mapped. This mapping includes the spatial arrangement of roads, paths, property boundaries, driveways, ramps, etc. and, if possible, the subclass classifications provided (see above) of the individual map elements. As with any particulate filter, the output of the method described herein, ie the more precise position and orientation estimate P, is represented as a discrete probability distribution by a set of weighted hypotheses H = [h ₁ ..., h _n ]. In the course of the process every hypothesis h _i from H undergoes repeated updating due to the movement (so-called motion-update), and the re-evaluation of the sensor values (sensor update) before H of the plausibility of its elements calculated by the revaluation is reconstituted according to (resampling ).

Within the motion update, the pose, ie the position and orientation components of h _i , is updated according to the vehicle's own motion measured from the last cycle (weighted average of GPS or GNSS velocity vector and odometry information). This update includes explicit modeling of possible location errors (due to GPS or GNSS measurement) and odometry errors.

• – wird die aus der Karte ausgelesene Untergrundklassifizierung mit dem aktuell erkannten Untergrund verglichen; zwei unterschiedliche Typen führen zu einer niedrigen Bewertung der Hypothese, wohingegen der gleiche Typ zu einer hohen Bewertung führt,
• – wird nach eindeutigen Signaturen in den Sensormesswerten gesucht, die den Übergang zwischen zwei markant unterschiedlichen Typen von Untergrund signalisieren; entspricht die Pose einer Hypothese h; einer solchen aktuell gemessenen Signatur, führt dies zu einer hohen Bewertung von h_i,
• – trägt die metrische Differenz zwischen aktuell gemessener GNSS-Position und der Position von h_i zur Bewertung dieser Hypothese bei.

In the subsequent reassessment of the sensor values, the plausibility of a single hypothesis h _i p is recalculated. The plausibility or probability of a hypothesis results from the differences between measured sensor values and the values to be expected on the basis of the existing map material (including hypothesized pose of the hypothesis). In this step

• The subclass classification read from the card is compared with the currently detected subsoil; two different types lead to a low evaluation of the hypothesis, whereas the same type leads to a high rating,
• - Searches for unique signatures in the sensor readings that signal the transition between two distinctly different types of subsurface; the pose corresponds to a hypothesis h; of such a currently measured signature, this leads to a high score of h _i ,
• - contributes the metric difference between the currently measured GNSS position and the position of h _i to evaluate this hypothesis.

In a final step (resampling), the set of hypotheses H is rebuilt. In this step, highly valued hypotheses have a greater probability of being taken over into the newly constructed set H '. The mean of the positions of h _i 'from H' represents the currently estimated position of the vehicle.

The 3 illustrates the method described above. The estimated pose represents the mean of the hypotheses managed in the particulate filter. Recognizing a measured peak in the sensor unit acceleration values (passing a lowered curb) may, in the reassessment, favor hypotheses located on the boundary between asphalt 34 and walkway pavement 36 are located.

In at least one particular embodiment of the present invention, a maintenance-free apparatus for precisely calculating the self-motion (odometry) of a means of transport (vehicle) by means of one intelligent sensor unit on two wheels in conjunction with further calculations, for example for background classification, the precision of Navigation software in a radio-linked evaluation unit, provided. The odometry sensor unit measures, at least in one particular embodiment, the proper motion of a wheel with the highest possible precision. It can be dispensed with a wiring for data transmission. Advantageously, the measurement data are already locally preprocessed intelligently and processed so that the lowest possible data exchange is necessary. The final processing should then be in one by radio connected evaluation unit, which compares the data, for example, a pair of wheels with each other and with location-related reference data in an electronic map, ie database.

• • Anbringung am Rad
• – auf/in der Felge, dadurch
• – Messen der Rotationsbewegung des Rades;
• – Erzeugung von elektrischer Energie aus Eigenbewegung;
• – optional unter dem Radbelag (Gummibereifung/Schlauch), damit
• – durch Belag mechanisch geschützt;
• – unter Belag verborgen (Diebstahlschutz);
• – die Möglichkeit, auch den Reifendruck zu messen (Option).
• • Eigene Energieversorgung
• – Lokale Speicherung mit Energiemanagement;
• – Lokale Ernte elektrischer Energie (aus Eigenbewegung), je nach Bauart des Rades, Energiebedarf und Kostenstruktur, durch Anbringung an
• – der Radnabe als Mini-Dynamo;
• – einer Speiche für radiale Bewegungs-Energieernte;
• – unter der Lauffläche (bzw. dem Schlauch) für Fliehkraft-Energieernte;
• – Externe Aufladung durch Induktion (optional);
• – (als Option) Ergänzung der Energieernte durch Solarsensoren (besonderes im Stand, wenn keine Bewegung vorliegt);
• • Datenübertragung durch Funk
• – Verbindung mit der Auswerteeinheit in einem handelsüblichen Mobilgerät, zum Beispiel einem Smartphone mit GPS- bzw. allgemein GNSS-Sensor und Funkanbindung;
• – Protokollstandard zur Funkübertragung im Nahbereich (ca. 2–4 m) für Geräte mit geringem Energieverbrauch, zum Beispiel Bluetooth 4.0;
• – Verbindung mit einem gleichartigen Sensorelement an einem anderen Rad zum synchronisierten Datenaustausch (optional);
• • Kombination von Sensorik zur Messung von
• – Beschleunigung (MEMS accelerometer in 3 Achsen);
• – Drehrate (MEMS gyroscope in 3 Achsen)
• – Temperatur;
• – Reifendruck (optional)
• • Intelligentes Sensorelement
• – Leistungsfähiger Mikroprozessor mit geringem Energieverbrauch;
• – Messung verschiedener Parameter (siehe Kombination von Sensorik);
• – Lokale Auswertung, Kombination (Datenfusion) und Vorverarbeitung der gemessenen Daten (Datenreduktion), so dass nur eine geringe Datenmenge zur Weiterverarbeitung nach außen gegeben werden muss.
• • Alarmmeldung
• – Sensorik für Temperatur meldet Eisgefahr;
• – Kombination der Sensorik ermöglicht die Errechnung der plötzlichen Beschleunigung sowie der vertikalen Schräglage; daraus kann die Gefahr eines Aufpralls oder Umfallens (und damit Sturzes der betroffenen Person) abgeleitet werden;
• – Sofern die Sensorik im Reifen angebracht ist (optional), kann ein Abfall des Reifendrucks gemeldet werden.
• • Odometrie
• – Sensoren für Beschleunigung und Drehrate ermöglichen Lageerkennung nach Datenfusion, d. h., es wird eine kontinuierliche Bestimmung des Winkels zwischen Sensorposition und der Bodenebene mittels eines Extended Kalman Filters durchgeführt;
• – Sensorik an einem Rad ermöglicht die Errechnung der Geschwindigkeit;
• – Kombination der Sensorik der Räder eines Räderpaares, die unabhängig laufen (zum Beispiel bei Differentialantrieb), ermöglicht die Errechnung des Lenkwinkels;
• – Sensorik an gelenkten Rädern (insbesondere passiv gelenkten, frei schwingenden Castor-Rädern) ermöglicht die Ermittlung der Stellung dieser Räder
• • Klassifikation des Untergrunds
• – Sensoren für Beschleunigung und Drehrate ermöglichen eine Klassifikation des Untergrundes nach Transformation der (un)gefilterten Beschleunigungswerte in den Frequenzraum;
• – Das (zum Beispiel an einer Speiche angebrachte) Sensorelement misst während der Fahrt Beschleunigungskräfte in Speichen-Ausrichtung, sowie in Richtung einer Orthogonalen innerhalb der Rad-Ebene.
• – Der Betrag dieses 2D-Beschleunigungsvektors |A| wird als Streckensignal (|A| über abgerollte Strecke s formuliert und in den Frequenzraum transformiert.
• – Das resultierende Spektrum (power spectrum) wird nun in einem zuvor gewählten Frequenzband als Signatur des Untergrundes verstanden.
• – Eine solche Signatur lässt sich mit einem Lernverfahren wie zum Beispiel dem Histogramm Clustering erlernen.
• • Verbesserung der Navigation
• – Die Ermittlung der Stellung (passiv) gelenkter Räder kann zur Verbesserung der Manövrierfähigkeit genutzt werden;
• – Odometrie-Daten zur Änderung von Geschwindigkeit und Lenkwinkel können zur Verfolgung eines Weges und damit zur Positionsbestimmung genutzt werden (Koppelnavigation);
• – Die Fusion von Odometrie-Daten mit einer Positionsbestimmung durch einen anderen Sensor (zum Beispiel GPS oder allgemein GNSS) kann zur Verbesserung der Genauigkeit der Positionsbestimmung genutzt werden;
• – Die Klassifikation des Untergrunds kann im Vergleich mit dazu (in einer Karte bzw. Datenbank) gespeicherten ortsbezogenen Referenz-Daten zur Ermittlung von Abweichungen vom angenommenen Weg genutzt werden;
• – Die Veränderung des Untergrunds, in Verbindung mit plötzlicher Höhenveränderung (zum Beispiel durch eine Schwelle), kann mit ortsbezogenen Referenz-Daten zur Kalibrierung genutzt werden, also zur Rückstellung akkumulierter Odometriefehler.

At least in particular embodiments, the sensor unit may be distinguished individually or in combination by the following features:

• • Attachment to the bike
• - on / in the rim, by
• - measuring the rotational movement of the wheel;
• - Generation of electrical energy from self-motion;
• - optional under the wheel cover (rubber tires / hose), so
• - mechanically protected by flooring;
• - hidden under cover (theft protection);
• - the possibility to measure the tire pressure (option).
• • Own energy supply
• - Local storage with energy management;
• - Local harvest of electrical energy (from self-motion), depending on the design of the wheel, energy requirements and cost structure, by attachment
• - the wheel hub as a mini-dynamo;
• - a spoke for radial motion energy harvesting;
• - under the tread (or hose) for centrifugal energy harvesting;
• - External charging by induction (optional);
• - (as an option) supplementing the energy harvest by solar sensors (especially in the state when there is no movement);
• • Data transmission by radio
• - Connection to the evaluation unit in a commercially available mobile device, for example a smartphone with GPS or general GNSS sensor and radio connection;
• - Protocol standard for wireless transmission in the near range (about 2-4 m) for devices with low energy consumption, for example Bluetooth 4.0;
• Connection to a similar sensor element on another wheel for synchronized data exchange (optional);
• • Combination of sensors for measuring
• - acceleration (MEMS accelerometer in 3 axes);
• - Rate of rotation (MEMS gyroscope in 3 axes)
• - temperature;
• - tire pressure (optional)
• • Intelligent sensor element
• - Powerful microprocessor with low power consumption;
• - measurement of different parameters (see combination of sensors);
• - Local evaluation, combination (data fusion) and pre-processing of the measured data (data reduction), so that only a small amount of data must be given to the outside for further processing.
• • Alarm message
• - Sensor system for temperature indicates ice danger;
• - Combination of the sensors allows the calculation of the sudden acceleration and the vertical tilt; this can be used to deduce the risk of an impact or fall (and thus fall of the person concerned);
• - If the sensor is mounted in the tire (optional), a drop in tire pressure may be reported.
• • Odometry
• Acceleration and yaw rate sensors allow for positional detection after data fusion, ie a continuous determination of the angle between sensor position and ground plane is performed by means of an extended Kalman filter;
• - Sensors on a wheel allow the calculation of the speed;
• - Combination of the sensors of the wheels of a pair of wheels that run independently (for example, differential drive), allows the calculation of the steering angle;
• - Sensors on steered wheels (especially passively steered, free-swinging Castor wheels) allows the determination of the position of these wheels
• • Classification of the underground
• - Sensors for acceleration and yaw rate allow a classification of the background after transformation of the (un) filtered acceleration values into the frequency domain;
• The sensor element (for example attached to a spoke) measures acceleration forces in the direction of the spoke as well as in the direction of an orthogonal within the wheel plane.
• The amount of this 2D acceleration vector | A | is formulated as a path signal (| A |) over unrolled path s and transformed into the frequency domain.
• - The resulting spectrum (power spectrum) is now understood in a previously selected frequency band as a signature of the background.
• - Such a signature can be learned with a learning method such as histogram clustering.
• • Improvement of navigation
• - Determining the position of (passive) steered wheels can be used to improve maneuverability;
• - Odometry data for changing speed and steering angle can be used to track a route and thus to determine the position (dead-reckoning);
• - The fusion of odometry data with another sensor (for example, GPS or general GNSS) can be used to improve the accuracy of positioning;
• - The classification of the subsoil can be used in comparison with location-based reference data (in a map or database) to determine deviations from the assumed path;
• - The change of the background, in connection with sudden height change (for example through a threshold), can be used with location-related reference data for calibration, ie for the recovery of accumulated odometry errors.

• – Vorverarbeitung der Messdaten, lokal in einer intelligenten Odometrie-Sensoreinheit eines Rades:
• – Ermittlung der Geschwindigkeit,
• – Klassifikation des Untergrunds,
• – Alarm bei Aufprall oder Umstürzen des Fortbewegungsmittels (Fahrzeugs),
• – Warnung vor Vereisung oder Abfall des Reifendrucks (optional),
• – der völligen Wartungsfreiheit durch eigene Energieernte und eigenes Energiemanagement in Verbindung mit einer Funkdatenübertragung,
• – Verbesserung der Präzision der Navigation in der durch Funk verbundenen Auswerteeinheit, die die Daten eines Räderpaars miteinander und mit ortsbezogenen Referenzdaten in einer elektronischen Karte (d. h. Datenbank) abgleicht und
• – endgültige Verarbeitung der Daten als reine Softwarelösung in einem handelsüblichen Mobilgerät, zum Beispiel einem Smartphone mit GPS- bzw. GNSS-Sensor und Funkanbindung nach dem Bluetooth-4.0-Standard.

Particular advantages result from a combination of one or preferably two odometry sensor units in a pair of wheels and the associated software in an evaluation unit, in detail

• - Pre-processing of the measurement data, locally in a smart odometry sensor unit of a wheel:
• - determination of the speed,
• - classification of the subsoil,
• Alarm in the event of impact or overturning of the vehicle (vehicle),
• - warning of icing or drop in tire pressure (optional),
• - the complete freedom from maintenance through own energy harvest and own energy management in connection with a radio data transmission,
• Improving the precision of the navigation in the radio-connected evaluation unit, which compares the data of a pair of wheels with each other and with location-related reference data in an electronic map (ie database) and
• - Final processing of the data as a pure software solution in a commercially available mobile device, for example, a smartphone with GPS or GNSS sensor and wireless connection according to the Bluetooth 4.0 standard.

The scope of the invention extends to any actively or passively powered by wheels vehicles.

In the automotive sector, it is also usable as a pure navigation solution that improves the accuracy of navigation through odometry through radio connection directly to the mobile device, as long as the radio signal reaches the driver's mobile device. In addition, however, is also to think of its own radio connection, which makes it possible to use the subgrade classification (for example, via an adapted suspension) on ride comfort, wear, driving characteristics and possibly even safety (in off-road driving) influence.

The features of the invention disclosed in the present description, in the drawings and in the claims may be essential both individually and in any desired combinations for the realization of the invention in its various embodiments.

LIST OF REFERENCE NUMBERS

10

wheel

12

Odometry sensor unit

14

spoke

16

rim

18

tires

20

3D motion sensor

22

3D position sensor

24

temperature sensor

26

Energy generator

28

Energy management device

30

Data processing device

32

Radio data transmission interface

34

asphalt

36

sidewalk paving

38

race

Claims (21)

Active or passive driven wheel ( 10 ) with an odometry sensor unit ( 12 ), wherein the sensor unit comprises a 3D motion sensor ( 20 ); one with the 3D motion sensor ( 20 ) radio communication interface ( 32 ) according to a transmission protocol and a power generation device ( 26 ) for generating electrical energy from self-motion during movement of the wheel ( 10 ) with the 3D motion sensor ( 20 ) and the radio communication interface ( 32 ) is connected to the supply of the same with electrical energy comprises. Wheel ( 10 ) according to claim 1, characterized in that the sensor unit ( 12 ) also has a 3D position sensor ( 22 ) generated by the power generation device ( 26 ) is also supplied with electrical energy and with the radio data transmission interface ( 32 ) connected is. Wheel ( 10 ) according to claim 1 or 2, characterized in that the sensor unit ( 12 ) also has a temperature sensor ( 24 ) generated by the power generation device ( 26 ) is also supplied with electrical energy and with the radio data transmission interface ( 32 ) connected is. Wheel ( 10 ) according to one of the preceding claims, characterized in that it has a rim ( 16 ) having. Wheel ( 10 ) according to one of claims 1 to 4, characterized in that it comprises a pneumatic tire. Wheel ( 10 ) according to claim 5, characterized in that the sensor unit ( 12 ) further comprises a tire pressure sensor. Wheel ( 10 ) according to one of claims 1 to 4, characterized in that it comprises a rubber tire. Wheel ( 10 ) according to claim 5 or 6, characterized in that the sensor unit ( 12 ) is disposed or mounted in the pneumatic tire. Wheel ( 10 ) according to claim 7, characterized in that the sensor unit ( 12 ) is arranged or mounted in the rubber tire. Wheel ( 10 ) according to one of claims 4 to 7, characterized in that the sensor unit ( 12 ) on or in the rim ( 16 ) is arranged or attached. Wheel ( 10 ) according to one of the preceding claims, characterized in that the sensor unit ( 12 ) a data processing device ( 30 ) of the radio communication interface ( 32 ) and from the power generation device ( 26 ) is also supplied with electrical energy. Wheel ( 10 ) according to one of the preceding claims, characterized in that the sensor unit ( 12 ) an energy management device ( 28 ), the energy generating device ( 26 ) is connected downstream. Muscle or motorized locomotion comprising at least one wheel ( 10 ) according to one of the preceding claims. A system comprising a means of transport according to claim 13 and a base unit, in particular a mobile communication device or a tablet PC, which has a radio data transmission interface in accordance with the same transmission protocol as the radio data transmission interface ( 32 ) of the at least one wheel ( 10 ) for communication with the sensor unit ( 12 ) of the at least one wheel ( 10 ), a GNSS sensor, in particular GPS sensor, an evaluation device, which is designed to be detected by the sensor unit ( 12 ) of the at least one wheel ( 10 ) and / or data supplied by the base unit, the position and / or the steering direction and / or the position and / or the ground and / or a collision or overturning of the means of locomotion and / or the tire pressure of the wheel ( 10 ), and has a display or output device for displaying or outputting the evaluation result. System according to claim 14, characterized in that the base unit further comprises a position sensor and / or a compass. System according to claim 14 or 15, characterized in that the base unit further comprises a 3D motion sensor. System according to one of claims 14 to 16, characterized in that the base unit further comprises a database with a digital map and / or with background reference data or connected to an external database with a digital map and / or with background reference data or connectable. A method for detecting a fall in a means of locomotion according to claim 13, comprising - for at least one wheel ( 10 Determining the magnitude of the acceleration component acc _z in a direction perpendicular to the plane of the wheel by means of the associated 3D motion sensor, assuming a fall if the magnitude of the acceleration component acc _z or the amount of the acceleration component acc _z resulting from a data fusion corresponds to the gravitational acceleration g, and issuing an alarm by means of an alarm device in the sensor unit ( 12 ) or in a separate base unit, in particular a system according to claim 13, which also includes the means of transportation. A method for detecting a fall in a means of locomotion of a system according to claim 18, comprising - for at least one wheel ( 10 ) Determining the values of the mutually perpendicular acceleration components acc _x and acc _y in the wheel plane by means of the associated 3D motion sensor ( 20 ), - determining the values of the mutually perpendicular acceleration components acc _x and acc _y in the wheel plane by means of the base unit, - determining the position by means of a fusion of odometry and position data determined by means of a global localization method and the ground condition on the basis of the merged position data, - fusion of all determined values of the acceleration components and the determined ground condition and - determination of a fall based on the merged data. Method for subclass classification by means of at least one wheel ( 10 ) according to one of claims 1 to 12, in particular a means of locomotion according to claim 13, comprising - determining the length of the acceleration vector in the wheel plane as a function of time Measuring the mutually perpendicular acceleration components acc _x and acc _y in the wheel plane by means of the associated 3D motion sensor ( 20 ) in discrete, preferably equidistant, time intervals, - preferably linear interpolation of the temporal course of the length of the acceleration vector, - determination of the frequency spectrum of that of the wheel ( 10 ) traveled distance by transforming the interpolated time course in the frequency space, - Comparison of the determined frequency spectrum with reference spectra for different substrates and - Classify the background based on the result of the comparison. Method for increasing the precision of a global localization method by means of a system according to one of claims 14 to 17, comprising - fusion of values of acceleration components of the at least one wheel ( 10 ), Odometry information on the means of locating GNSS information to be refined and classification of the subsurface, and - using a particulate filter to estimate the current position of the means of locomotion on the basis of the merged data.

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