CN110806261A - Collision detection method applied to automation equipment - Google Patents
Collision detection method applied to automation equipment Download PDFInfo
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- CN110806261A CN110806261A CN201911074570.4A CN201911074570A CN110806261A CN 110806261 A CN110806261 A CN 110806261A CN 201911074570 A CN201911074570 A CN 201911074570A CN 110806261 A CN110806261 A CN 110806261A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
- G01P15/18—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration in two or more dimensions
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Abstract
The invention provides a collision detection method applied to automatic equipment, wherein the automatic equipment can generate vibration at the moment of collision. The method adopted by the invention comprises the following steps: step 1, collecting measurement data of a triaxial accelerometer in real time at a fixed frequency; step 2: assuming that random noise of the accelerometer is ergodic, calculating standard deviation of the random noise of the triaxial accelerometer within a period of time to represent statistical characteristics of the triaxial noise; and step 3: calculating a vibration vector represented by a standard deviation of the three-axis accelerometer measurements; and 4, step 4: setting a threshold value for the direction and strength of the vibration vector to detect the collision. By the method, the automatic equipment can realize automatic mode conversion by detecting vibration.
Description
Technical Field
The invention discloses a collision detection method applied to automation equipment, and belongs to the field of automatic control.
Background
An automatic control process of an automation apparatus represented by a robot is not open to a state detection mechanism. For example, a robot automatically transitions from one motion state to another, a state detection mechanism is required.
Collision detection is a state detection mechanism, and with collision detection, once a robot detects a collision, it can automatically change from one motion mode to another motion mode.
Disclosure of Invention
The purpose of the invention is as follows:
the invention aims to design a collision detection method.
The technical scheme is as follows:
the invention considers that the automatic equipment can vibrate certainly at the moment of collision, so the technical scheme of the invention is to utilize the three-axis accelerometer to capture the vibration. The invention considers that the vibration is a vector, the vibration has both direction and intensity, and the three-axis accelerometer can sensitively capture the change of self acceleration caused by the vibration, thereby realizing the vibration detection and further realizing the collision detection.
The vibration detection method of the invention needs to acquire the measurement data of the triaxial accelerometer in real time at a fixed frequency. In an embodiment of the invention, the measurement data of the triaxial accelerometer is collected at a frequency of 400 Hz.
Since the measurement value of the accelerometer has random noise, the invention assumes that the random noise of the accelerometer is ergodic, so that the standard deviation of the random noise of the triaxial accelerometer within a period of time needs to be calculated, thereby representing the statistical characteristic of the triaxial noise.
Next, a method of calculating the vibration vector is described. First, a time window with a length T needs to be set. The time sliding window is a time window moving along with time, and assuming that the current time is T, the time sliding window is a window period from the time T-T to the time T, and it can be seen that the time sliding window is sliding along with time. Since the digital signal system collects signals at a fixed frequency, M sets of accelerometer data are collected over time, where each set of accelerometer data includes three quantities, x-axis measurements, y-axis measurements, and z-axis measurements, so that there are M measurements in the time sliding window for each axis. The vibration vector of the present invention is represented by the standard deviation of the three-axis accelerometer measurements.
When collision detection is carried out, firstly, a threshold value is set for the intensity and the direction of vibration according to actual requirements. Only when a shock vector meeting the threshold requirement is detected, a collision is indicated, and further mode conversion of the automation device is triggered.
The invention has the advantages and beneficial effects that:
the invention provides a collision detection method, so that automatic equipment can realize automatic mode conversion by detecting vibration.
Drawings
FIG. 1: the invention relates to a flow chart of a collision detection method.
FIG. 2: 1000 sets of images of triaxial acceleration data measured continuously while the triaxial accelerometer is stationary.
FIG. 3: 1000 sets of images of triaxial acceleration data continuously measured by the triaxial accelerometer before and after a crash.
FIG. 4: 1000 groups of images of triaxial acceleration data continuously measured by triaxial accelerometers before and after the flying robot and the wall surface generate forward collision.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The collision detection method is realized by detecting vibration, so that the measurement data of the triaxial accelerometer is required to be acquired in real time at a fixed frequency for detecting the vibration, and the detection flow is shown in the attached figure 1. In an embodiment of the invention, the measurement data of the triaxial accelerometer is collected at a frequency of 400 Hz.
FIG. 2 shows 1000 sets of accelerometer data measured while the triaxial accelerometer is at rest, with the abscissa representing the number of sets and the ordinate representing the acceleration value (unit: cm/s)2). Since the frequency of data acquisition is 400Hz in the embodiment, the time length corresponding to the 1000 sets of data is 2.5 seconds.
It can be easily seen from fig. 2 that the measured value of the accelerometer has random noise, and the random noise of the accelerometer is considered to be ergodic, so that the standard deviation of the random noise of the triaxial accelerometer within a period of time only needs to be calculated, and the standard deviation can represent the statistical characteristic of the triaxial noise. The calculation method comprises the steps of firstly placing the three-axis accelerometer in a static state, then continuously collecting N groups of three-axis accelerometer data, and calculating the standard deviation of the N groups of three-axis accelerometer data, wherein each group of accelerometer data comprises three quantities, namely an x-axis measurement value, a y-axis measurement value and a z-axis measurement value, so that for each axis, N measurement data exist. The calculation formula of the standard deviation of the triaxial random noise is as follows:
in the formula, ax(i) I data acquired for the x-axis of the accelerometer, ay(i) I data collected for the y axis of the accelerometer, az(i) The ith data acquired for the accelerometer z-axis. So that the vector of the noise standard deviation, S ═ S (S)x，Sy，Sz) The modulo length of the vector of the noise standard deviation is S. In the embodiment of the present invention, N is 100. Calculated, example Sx、Sy、SzAre all 0.2cm/s2。
Next, a method of calculating the vibration vector is described. As shown in FIG. 3, 1000 sets of measurement data of the triaxial accelerometer before and after the collision are measured by the triaxial accelerometer, the abscissa of the measurement data is the number of sets, and the ordinate of the measurement data is the acceleration value (unit: cm/s)2). With sets 400-500 being triaxial acceleration measurements while in a crash. The three-axis accelerometer measurement data image shown in FIG. 3 shows the occurrence of vibrations in all three directions, x-axis, y-axis, and z-axis.
To calculate the vibration vector, a time window of length T is first set. The time sliding window is a time window moving along with time, and assuming that the current time is T, the time sliding window is a window period from the time T-T to the time T, and it can be seen that the time sliding window is sliding along with time. Since the digital signal system collects signals at a fixed frequency, M sets of accelerometer data are collected during T time, and are known to vary with time, where each set of accelerometer data includes three quantities, x-axis measurements, y-axis measurements, and z-axis measurements, so that for each axis, there are M measurements in the time sliding window. The vibration vector of the present invention is represented by the standard deviation of the three-axis accelerometer measurements, and therefore the calculation formula of the vibration vector is as follows:
in the formula, ax(i) I data acquired for the x-axis of the accelerometer, ay(i) I data collected for the y axis of the accelerometer, az(i) The ith data acquired for the accelerometer z-axis. So that the vibration vector V becomes (V)x，Vy，Vz) The intensity of the vibration vector is | | | V | |. In the embodiment of the present invention, the set time sliding window length T is 50ms, and since the sampling frequency is 400Hz in the embodiment, M to 20 sets of accelerometer data are collected in the time sliding window. If V is less than or equal to S, no vibration occurs; if | | | V | > | S | | |, the vibration vector needs to be corrected, and V ═ after correction (V | > (V |)x-Sx，Vy-Sy，Vz-Sz) If V isx-Sx，Vy-Sy，Vz-SzIf any one of the terms is less than 0, the term is made equal to 0.
When collision detection is carried out, firstly, a threshold value is set for the intensity and the direction of vibration according to actual requirements. Only when a shock vector meeting the threshold requirement is detected, a collision is indicated, and further mode conversion of the automation device is triggered.
The collision detection method is applied to the flying robot in the embodiment. The flying robot can realize that the mode is automatic to be changed into the wall mode of crawling from the flight mode in the air. The automatic mode switching is realized through collision detection, the flying robot touches a wall surface in a flying state, and at the moment of the touch, the collision detection algorithm can detect the collision, so that the automatic mode switching of the flying robot from an air flying mode to a wall surface crawling mode is realized.
In the embodiment of the invention, the three-axis accelerometer is installed on the flying robot, and the positive x-axis direction of the accelerometer is the front of the flying robot. The forward collision of the flying robot to the wall surface needs to be detected, so that the included angle between the vibration vector and the x axis of the accelerometer and the vibration strength need to be detected. And a threshold value is set for the detected included angle and the detected strength, when the vibration vector meeting the threshold value requirement is detected, the forward collision between the flying robot and the wall surface is indicated, and then the mode conversion can be automatically triggered.
The calculation method of the included angle comprises the following steps:
as shown in the attached figure 4, the horizontal axis of the image is the group number, and the vertical axis is the acceleration value (unit: cm/s)2). With sets 400-500 being triaxial acceleration measurements while in a crash. It can be seen that only the x-axis measurement data fluctuates significantly in the event of a collision. However, in practical situations, the flying robot may not collide perfectly with the wall surface in the forward direction but have a certain inclinationOblique, or due to the body's conduction of vibrations, small amplitude vibrations are also detected in the y-axis and z-axis. A threshold value for the angle theta needs to be set.
In the embodiment of the invention, a threshold value of 40 degrees is set for theta, and a threshold value of 0.8cm/s is set for V | |2The threshold value of (1) is that when theta is less than 40 degrees and V is greater than 0.8, the vibration is considered to be transmitted from the x axis of the three-axis accelerometer in the positive direction, which means that the flying robot collides with the wall surface in the positive direction, and therefore, the mode conversion can be triggered.
Although the embodiment of the present invention is a flying robot, the collision detection method of the present invention is applicable to collision detection of an automated apparatus that is capable of generating a shock at the moment of collision.
Claims (9)
1. A collision detection method applied to an automation device, characterized by comprising the steps of:
step 1, collecting measurement data of a triaxial accelerometer in real time at a fixed frequency;
step 2: assuming that random noise of the accelerometer is ergodic, calculating standard deviation of the random noise of the triaxial accelerometer within a period of time to represent statistical characteristics of the triaxial noise;
and step 3: calculating a vibration vector represented by a standard deviation of the three-axis accelerometer measurements;
and 4, step 4: setting a threshold value for the direction and strength of the vibration vector to detect the collision.
2. The collision detection method applied to the automation device according to claim 1, wherein: the fixed frequency in step 1 was 400 Hz.
3. The collision detection method applied to the automation device according to claim 1, wherein: step 2, firstly, statically placing the triaxial accelerometer, then continuously acquiring N groups of triaxial accelerometer data, and calculating the random noise standard deviation of the N groups of triaxial accelerometers; wherein each set of accelerometer data includes x-axis measurements, y-axis measurements, and z-axis measurements, such that there are N measurements for each axis.
4. A collision detection method applied to an automation device according to claim 3, characterized in that: the calculation formula of the random noise standard deviation of the triaxial accelerometer is as follows:
in the formula, ax(i) I data acquired for the x-axis of the accelerometer, ay(i) I data collected for the y axis of the accelerometer, az(i) (ii) the ith data acquired for the accelerometer z-axis; so that the vector of the noise standard deviation, S ═ S (S)x，Sy，Sz) The modulo length of the vector of the noise standard deviation is S.
5. The collision detection method applied to the automation device according to claim 1, wherein: in the step 3, a time sliding window with the length of T needs to be set; the time sliding window is a time window moving along with time, and if the current time is T, the time sliding window is a window period starting from the T-T moment to the end of the T moment, so that the time sliding window can be seen to slide along with the time; the digital signal system collects signals at a fixed frequency, and collects M groups of accelerometer data in T time, wherein the M groups of accelerometer data are changed along with time.
6. The collision detection method applied to the automation device according to claim 5, wherein: in step 3, each set of accelerometer data includes three quantities, x-axis measurements, y-axis measurements, and z-axis measurements, so that there are M measurements in the time sliding window for each axis.
7. The collision detection method applied to the automation device according to claim 1 or 6, characterized in that: the vibration vector is calculated as follows:
in the formula, ax(i) I data acquired for the x-axis of the accelerometer, ay(i) I data collected for the y axis of the accelerometer, az(i) (ii) the ith data acquired for the accelerometer z-axis; so that the vibration vector V becomes (V)x，Vy，Vz) The intensity of the vibration vector is | | | V | |.
8. The collision detection method applied to the automation device according to claim 7, wherein: if V is less than or equal to S, no vibration occurs; if | | | V | > | S | | |, the vibration vector needs to be corrected, and V ═ after correction (V | > (V |)x-Sx，Vy-Sy，Vz-Sz) If V isx-Sx，Vy-Sy，Vz-SzIf any one of the terms is less than 0, the term is made equal to 0.
9. The collision detection method applied to the automation device according to claim 1, wherein: in step 4, firstly setting a threshold value for the intensity and direction of the vibration vector when collision detection is carried out; only when a shock vector meeting the threshold requirement is detected, a collision is indicated, and further mode conversion of the automation device is triggered.
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| CN201911074570.4A CN110806261A (en) | 2019-11-06 | 2019-11-06 | Collision detection method applied to automation equipment |
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| CN201911074570.4A CN110806261A (en) | 2019-11-06 | 2019-11-06 | Collision detection method applied to automation equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112265568A (en) * | 2020-10-26 | 2021-01-26 | 南京富岛信息工程有限公司 | Intelligent iron shoe slip alarm system |
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| US9758173B1 (en) * | 2012-12-17 | 2017-09-12 | State Farm Mutual Automobile Insurance Company | System and method for monitoring and reducing vehicle operator impairment |
| CN207123333U (en) * | 2017-04-28 | 2018-03-20 | 深圳乐行天下科技有限公司 | Collision detecting device and there is its robot |
| US10118487B1 (en) * | 2014-05-05 | 2018-11-06 | State Farm Mutual Automobile Insurance Company | System and method to monitor and alert vehicle operator of impairment |
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| US20140172467A1 (en) * | 2012-12-17 | 2014-06-19 | State Farm Mutual Automobile Insurance Company | System and method to adjust insurance rate based on real-time data about potential vehicle operator impairment |
| US9758173B1 (en) * | 2012-12-17 | 2017-09-12 | State Farm Mutual Automobile Insurance Company | System and method for monitoring and reducing vehicle operator impairment |
| US10118487B1 (en) * | 2014-05-05 | 2018-11-06 | State Farm Mutual Automobile Insurance Company | System and method to monitor and alert vehicle operator of impairment |
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Application publication date: 20200218 |