Method for detecting vehicle rotation rates and device for performing the method.

The invention relates to a method for determining vehicle rotation rates by the features specified in the preamble of claim 1. Further, vehicle arrangements for performing the method of the invention.

From DE 31 11 130 Al the recovery of the vehicle yaw rate from a gyro navigation is shown. A disadvantage is the effect of a known the gyro system immanent peculiarity of a time-dependent drift, which is a time-dependent deviation from the desired course, particularly in driverless vehicles, can not be avoided.

From DE 30 03 287 Al, a self-propelled vehicle is known, are arranged at the free revolving rear wheels with encoders for the purpose to gain information on the sheet length and radius of a circle to the vehicle steering. As angle encoder wheel sensors are used here, but they are susceptible to interference, for example when the vehicle hits a pothole. By such a disturbance of the wheel sensor loses the direction information of the vehicle, because at the passage through this pothole statistically the number of revolutions of at least one wheel is changed and as a result briefly a high vehicle yaw rate is passed on accordingly, resulting in a negative reaction of the vehicle.

In contrast, it is an object of the invention to suppress the sensor principles related disorders in determining the vehicle yaw rate.

This object is achieved by the methods listed in the characterizing features of patent claim 1. Advantageous arrangements for carrying out the method are evident from the features of the subclaims.

The inventive method can be advantageously roadway-related interference, such as a pothole in identifying the existing vehicle rotation rate is no longer a negative impact. A fault caused and example occurring when driving through a pothole sensor-related high-frequency rotation rate signals of the wheel sensors are eliminated by the blocking effect when passing a low-pass filter and therefore no longer have any annoying when determining the actual vehicle yaw rate from.

Furthermore, an existing by the drift of a gyroscope low interference with respect to the determination of the actual vehicle yaw rate is thereby advantageously avoided that the rotation rate system generated by the gyro system has a high-pass filter to pass and thus the results from the drift to avoid distortions.

By linking the two above-filtered rotation rate measurement signals can be determined in a particularly simple and fast way the position angle of a driverless vehicle. In particular, low-cost commercial gyro systems can be used by the simple drift elimination. The invention will be explained in more detail with reference to two exemplary embodiments illustrated in the drawings.

It shows:<dl id="dl0001"><dt>Fig. 1</dt><dd>a driverless powered vehicle in a perspective view,</dd><dt>FIG. 2</dt><dd>a schematic representation of the filtered by high pass filter Tiefpaßund rotation rate signals,</dd><dt>Fig. 3</dt><dd>a schematic representation of the vehicle turning angle,</dd><dt>Fig. 4</dt><dd>in a schematic representation the course of the filtered through a low rotation rate signals,</dd><dt>Fig. 5</dt><dd>a schematic representation of the course of the filtered by a high pass filter rotation rate signals,</dd><dt>Fig. 6</dt><dd>the course of the rotational speed signal with a drift gyroscope and external interference from the wheel sensor,</dd><dt>Fig. 7</dt><dd>a gyro sensor and rear-related trouble-free rotation rate waveform,</dd><dt>Fig. 8</dt><dd>another embodiment, in which the disorders of the rotation rate signals are eliminated by means of a Kalman filter.</dd></dl>

In Fig. 1 an autonomous driverless transport vehicle 14 is shown which does not require physical rails for steering and therefore when changing the route has a high flexibility. For orientation and generating the steering movement carried angular and displacement measuring sensors are used. The orientation takes place on inertial systems, as the circuit 12 and / or by means of suitable sensors (encoder 10) to objects of normal operational environment.

The vehicle 14 consists essentially of the following components: A base frame 32, which forms a force-receiving cell, and in the upper region carries a mounting platform for receiving customer-specific usage devices. A not shown in front of the vehicle steering system which can for example consist of a steering wheel is equipped with drive motors and gear while the vehicle is arranged on both sides in its rear part two, containing only follower vehicle wheels 26th

In front of the vehicle known distance sensors 36 are arranged as a safety device to prevent a collision. The vehicle can automatically be operated by hand or by a learning mode, for which relevant for the learning run a camera 38 is arranged in the upper front of the vehicle. An evaluation circuit 16 includes various electric Bays 16.1, 16.2, etc., in which all functions are controlled centrally. For example, used a slide 16.1 with the purpose that the axis controller implements the steering commands in Motoransteuerbefehle. Another insert 16.2 is equipped with an electronic system for obtaining the location information from the motion sensor.

As encoder 10 are independently rotatable rear wheels 26, which are equipped with electric sensors to determine the distance covered. Any divergences between the measured path of a wheel 26 of the other, so the vehicle has 14 rotated by a rotation angle α shown in FIG. 3. In this way, the wheel sensors may be about 10 with the aid of the evaluation circuit 16, the vehicle yaw rate φ is calculated and converted into corresponding electrical angular rate signals 22nd This interference of the path, such as a pothole, do not affect the measurement results, and in particular occurring high frequency rotational rate signals 40 do not cause false triggering of the vehicle (Fig. 6) detected by the wheel sensor 10 rotation rate signal 22 is a low pass filter 24 is supplied, the only only occurring in undisturbed normal driving of the vehicle 14 rotation rate signals 22 passes. The low pass filter can be designed, for example, that he RF interference in the range between a rotation rate of 10^O/ Sec. and a rotation rate of 90^O/ Sec. suppressed and ≦ 10^O/ Sec. passes. Fig. 4 illustrates this characteristic of the low pass filter. After only reach the 40 enclosed by the curve electric signals a relatively small vehicle turning rate 22 through the low-pass filter 24th

In order to avoid the initially described, and in FIG. 6 illustrated gyro drift 19, which for example 10^O/ H may be that certain of the gyro system vehicle yaw rate signal must pass through a high pass filter 18 20th The characteristic of such a high-pass filter 20 is shown in Fig. 5. After the drift 19 correspondingly small vehicle rotation rate signals 42 is not considered by the high-pass filter 20, for example, while vehicle rotation rate signals 18 ≧ 10^O/ H to the high-pass filter to pass.

The filtered outputs of the vehicle yaw rate 18 'and 22' are then subjected to a summing amplifier 28 combined in a conventional manner.

With the above suppression of relevant sensor-related disorders 22 (Fig. 7) is an exact vehicle yaw rate determined for the target price, with 18 determines, for example, during the pothole-related disorder, freed of the drift 19 yaw rate signal to the desired course of the vehicle.

The same above-described advantageous effect of suppressing sensor-related disturbances can be generated by a high-pass and low-pass filtering generating a Kalman filter 30, as is schematically shown in Fig. 8.

Here a non-illustrated amplifier matrix of the Kalman filter acts to the generated from the gimbal assembly 12 rotation rate information 18 is sent through a Hochpaßfilterschicht and the product obtained from the wheel sensor 10 rotation rate signal 22 through a low pass also not shown.

Navigation through the Kalman filter 30 is possible in the closed switching positions II and III. Thereby pass the rotation rate signals 22 from the wheel sensor 10, after leaving a required for the wiring of network 44 and the rotation rate signals 18 of the circuit 12 after leaving a corresponding network 46 into the Kalman filter 30. As a result, the obtained sum matrix 48 with the desired values ​​50 of the car navigation is compared and used to calculate the vehicle position angle 52nd

On a smooth floor, a simple mode of operation is only possible with Radencoder according to the circuit I, while in the circuit II a gyro navigation is carried out.

The equipment used: high-pass filter 20, low pass filter 24, summing amplifier 28, Kalman filter 30, networks 44, 46, sum matrix 48 are commercially available and thus belong to the prior art, so a detailed description of these parts is unnecessary.

LIST OF REFERENCE NUMBERS

<dl id="dl0002"><dt>10</dt><dd>encoder</dd><dt>12</dt><dd>Gimbal assembly</dd><dt>14</dt><dd>vehicle</dd><dt>16</dt><dd>evaluation</dd><dt>16.1, 16.2</dt><dd>Bays</dd><dt>18</dt><dd>Rotation rate signal (centrifugal)</dd><dt>19</dt><dd>drift</dd><dt>20</dt><dd>highpass filter</dd><dt>22</dt><dd>Rotation rate signal (wheel sensor)</dd><dt>24</dt><dd>low pass filter</dd><dt>26</dt><dd>rear wheel</dd><dt>28</dt><dd>summing amplifier</dd><dt>30</dt><dd>Kalman filter</dd><dt>32</dt><dd>undercarriage</dd><dt>34</dt><dd>mounting platform</dd><dt>36</dt><dd>distance sensors</dd><dt>38</dt><dd>camera</dd><dt>40</dt><dd>signals</dd><dt>42</dt><dd>signals</dd><dt>44</dt><dd>network</dd><dt>46</dt><dd>network</dd><dt>48</dt><dd>Total matrix</dd><dt>50</dt><dd>setpoint</dd><dt>52</dt><dd>position angle</dd></dl>