Unmanned aerial vehicle visual positioning and obstacle avoidance method and system

Abstract

The invention discloses an unmanned aerial vehicle visual positioning and obstacle avoidance method and system. The visual positioning and obstacle avoidance method for the unmanned aerial vehicle comprises the following steps that S1, a binocular camera collects images of the surrounding environment of the unmanned aerial vehicle in real time, an airborne processor conducts three-dimensional modeling on an unmanned aerial vehicle body and surrounding obstacles according to the images, and relative relation data between the unmanned aerial vehicle body and the surrounding obstacles is generated; s2, transmitting the relative relation data to an unmanned aerial vehicle remote control end through a data link; and S3, the unmanned aerial vehicle remote control end analyzes the relative relation data, and the unmanned aerial vehicle remote control end guides an operator to check the relative relation data and guides the operator to select whether to interfere with the relative relation data or not. The unmanned aerial vehicle visual positioning and obstacle avoidance method and system disclosed by the invention have the beneficial effects that when an operator selects to intervene the relative relation data, the operator operates the unmanned aerial vehicle remote control end to generate the intervened relative relation data and notices whether to directly intervene the obstacle avoidance action of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention belongs to the field of interaction between a drone and a drone remote control terminal, and in particular relates to a drone visual positioning and obstacle avoidance method and system. Background Art

[0002] The publication number is CN112965517A, the subject name is invention patent application for UAV inspection safety obstacle avoidance system and method based on binocular vision fusion laser radar and electromagnetic field detection, and its IPC classification number is G05D1 / 10. Its technical solution discloses "UAV inspection safety obstacle avoidance system based on binocular vision fusion laser radar and electromagnetic field detection: including detection module, information preprocessing module, multi-sensor information fusion module and obstacle avoidance decision module; the detection module includes a binocular vision camera, laser radar, millimeter wave radar, electromagnetic field detection sensor, ultrasonic sensor and Beidou positioning system BDS loaded on the UAV; the binocular camera calculates depth image based on binocular vision to obtain obstacle distance information in real time."

[0003] It can be seen that the above invention patent application has disclosed one of the technical solutions for the UAV visual positioning and obstacle avoidance method. However, the technical solution disclosed in the above invention patent application focuses on receiving information from the multi-sensor information fusion module, generating obstacle avoidance strategies, and controlling the UAV flight control system to achieve obstacle avoidance actions, which is equivalent to directly intervening in the obstacle avoidance actions of the UAV, rather than intervening in the relative relationship data between the UAV body and the surrounding obstacles. As a result, the relative relationship between the UAV body and the surrounding obstacles can only be assumed to be correct. At the same time, it is difficult to directly intervene in the relative relationship between the UAV body and the surrounding obstacles in special scenarios, which needs to be further improved. Summary of the invention

[0004] In view of the status of the prior art, the present invention overcomes the above defects and provides a method and system for visual positioning and obstacle avoidance of an unmanned aerial vehicle.

[0005] The present invention adopts the following technical solution, the UAV visual positioning and obstacle avoidance method comprises the following steps:

[0006] Step S1: The binocular camera collects images of the drone's surrounding environment in real time, and the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the images, and generates relative relationship data between the drone body and surrounding obstacles;

[0007] Step S2: The relative relationship data is transmitted to the drone remote control terminal via a data link;

[0008] Step S3: The drone remote control terminal parses the relative relationship data, and the drone remote control terminal guides the operator to view the relative relationship data, and guides the operator to choose whether to intervene in the relative relationship data, wherein:

[0009] When the operator chooses not to intervene in the relative relationship data, the UAV remote control terminal transmits a non-intervention relative relationship data instruction to the UAV through the data link, so that the onboard processor executes a preset obstacle avoidance method according to the relative relationship data;

[0010] When the operator chooses to intervene in the relative relationship data, the operator manually corrects or adjusts the relative relationship data through the UAV remote control terminal to generate the relative relationship data after intervention. The UAV remote control terminal transmits the relative relationship data after intervention to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the relative relationship data after intervention.

[0011] As a preferred technical solution of the above technical solution, the "binocular camera collects image information of the drone's surrounding environment in real time" in step S1 is specifically implemented as follows:

[0012] Step S1.1: The binocular camera acquires the left image I L and right image I R ;

[0013] Step S1.2: The onboard processor processes the left image I L and right image I R Perform image correction to eliminate distortion.

[0014] As a preferred technical solution of the above technical solution, the step S1 of "the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the image" is specifically implemented as follows:

[0015] Step S1.3: The onboard processor processes the left image I L Extract two-dimensional feature points (x L ,y L ), in the right image I R Extract two-dimensional feature points (x R ,y R );

[0016] Step S1.4: The onboard processor processes the left image I L and right image I R The feature points are matched to generate two-dimensional matching point pairs (x L ,y L ) and (x R ,y R );

[0017] Step S1.5: The onboard processor calculates the parallax d, which is represented by equation (1):

[0018] d = x L -x R , (1);

[0019] Among them, x L The left image I L The two-dimensional feature points (x L ,y L )’s X-axis coordinate, x R For the right image I R The two-dimensional feature points (x R ,y R )’s X-axis coordinate, y L The left image I L The two-dimensional feature points (x L ,y L )’s Y-axis coordinate, y R For the right image I R The two-dimensional feature points (x R ,y R )’s Y-axis coordinate;

[0020] Step S1.6: Given the camera baseline b, calculate the depth Z, which is represented by equation (2):

[0021]

[0022] Where, f is the focal length;

[0023] Step S1.7: The onboard processor converts the two-dimensional feature point (x L ,y L ) is converted into three-dimensional feature points (X, Y, Z), and the three-dimensional feature points (X, Y, Z) are represented by formula (3):

[0024]

[0025] Among them, (c x , c y ) is the center coordinate of the binocular camera.

[0026] As a preferred technical solution of the above technical solution, the step S1 of "generating relative relationship data between the drone body and surrounding obstacles" is specifically implemented as follows:

[0027] Step S1.8: The onboard processor calculates the relative position and attitude between the drone body and the obstacle;

[0028] Step S1.9: Generate the relative relationship data matrix R and the position vector T. The relative relationship data matrix R and the position vector T are represented by formula (4):

[0029]

[0030] As a preferred technical solution of the above technical solution, the "relative relationship data is transmitted to the drone remote control terminal via the data link" in step S2 is specifically implemented as follows:

[0031] Step S2.1: The onboard processor packages the relative relationship data matrix R and the position vector T into a data packet D. The data packet D is represented by formula (5):

[0032] D = {R, T} (5);

[0033] Step S2.2: The onboard processor encodes the data packet D to generate an encoded data packet D;

[0034] Step S2.3: Transmit the encoded data packet to the drone remote control terminal via the data link.

[0035] As a preferred technical solution of the above technical solution, the "UAV remote control terminal analyzes the relative relationship data" in step S3 is specifically implemented as follows:

[0036] Step S3.1: The drone remote controller obtains the encoded data packet D;

[0037] Step S3.2: The drone remote control terminal parses the encoded data packet D to obtain the relative relationship data matrix R and position vector T.

[0038] As a preferred technical solution of the above technical solution, the "UAV remote control terminal guides the operator to view the relative relationship data" in step S3 is specifically implemented as follows:

[0039] Step S3.3: The drone remote control terminal simulates and displays the relative position and posture of the drone body and surrounding obstacles on the display module according to the relative relationship data matrix R and the position vector T, and directly displays the relative relationship data matrix R and the position vector T on the display module;

[0040] Step S3.4: The UAV remote control terminal guides the operator to view the simulated relative position and posture of the UAV body and surrounding obstacles, as well as the directly displayed relative relationship data matrix R and position vector T;

[0041] Step S3.5: The UAV remote control terminal guides the operator whether to intervene in the relative relationship data matrix R and the position vector T. When the operator chooses not to intervene in the relative relationship data matrix R and the position vector T, step S3.6 is executed. When the operator chooses to intervene in the relative relationship data matrix R and the position vector T, step S3.7 is executed.

[0042] As a preferred technical solution of the above technical solution, step S3.6 is specifically implemented as the following steps:

[0043] Step S3.6: The drone remote control generates command I and sends command I back to the onboard processor. The command I in step S3.6 is represented by formula (6):

[0044] I = {R, T}, (6);

[0045] Step S3.7 is specifically implemented as follows:

[0046] Step S3.7: The drone remote controller generates instruction I and sends instruction I back to the recording processor. The instruction I in step S3.7 is represented by formula (7):

[0047] I = {R′, T′} (7);

[0048] Among them, R′ is the relative relationship data matrix after intervention, and T′ is the position vector T after intervention.

[0049] The present invention adopts the following technical solutions, a UAV visual positioning and obstacle avoidance system, and the steps of the UAV visual positioning and obstacle avoidance method applying any one of the above technical solutions.

[0050] The unmanned aerial vehicle visual positioning and obstacle avoidance method and system disclosed in the present invention have the beneficial effect of making full use of the binocular camera mounted on the unmanned aerial vehicle, and based on the binocular camera principle, three-dimensional modeling of the unmanned aerial vehicle body and surrounding obstacles is performed, thereby generating relative relationship data between the unmanned aerial vehicle body and surrounding obstacles. The relative relationship data is transmitted to the unmanned aerial vehicle remote control terminal through a data link. The operator of the unmanned aerial vehicle remote control terminal chooses whether to intervene in the relative relationship data. When the operator chooses to intervene in the relative relationship data, the operator operates the unmanned aerial vehicle remote control terminal to generate the relative relationship data after intervention. Note that it does not directly intervene in the obstacle avoidance action of the unmanned aerial vehicle. The relative relationship data after intervention is transmitted back to the unmanned aerial vehicle, and the unmanned aerial vehicle executes the preset obstacle avoidance disposal method according to the relative relationship data after intervention, and on this basis, accurate positioning and obstacle avoidance of obstacles are achieved. DETAILED DESCRIPTION

[0051] The present invention discloses a method and system for visual positioning and obstacle avoidance of an unmanned aerial vehicle. The specific implementation manner of the present invention is further described below in conjunction with a preferred embodiment (Example 1).

[0052] Example 1.

[0053] Preferably, the UAV visual positioning and obstacle avoidance method comprises the following steps:

[0054] Step S1: The binocular camera collects images of the drone's surrounding environment in real time, and the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the images, and generates relative relationship data between the drone body and surrounding obstacles;

[0055] Step S2: The relative relationship data is transmitted to the drone remote control terminal via a data link;

[0056] Step S3: The drone remote control terminal parses the relative relationship data, and the drone remote control terminal guides the operator to view the relative relationship data, and guides the operator to choose whether to intervene in the relative relationship data, wherein:

[0057] When the operator chooses not to intervene in the relative relationship data, the UAV remote control terminal transmits a non-intervention relative relationship data instruction to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the (original) relative relationship data;

[0058] When the operator chooses to intervene in the relative relationship data, the operator manually corrects or adjusts the relative relationship data through the UAV remote control terminal to generate the relative relationship data after intervention. The UAV remote control terminal transmits the relative relationship data after intervention to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the relative relationship data after intervention.

[0059] Among them, the "binocular camera collects image information of the drone's surrounding environment in real time" in step S1 is specifically implemented as follows:

[0060] Step S1.1: The binocular camera acquires the left image I L and right image I R ;

[0061] Step S1.2: The onboard processor processes the left image I L and right image I R Perform image correction to eliminate distortion.

[0062] Among them, step S1 of "the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the image" is specifically implemented as follows:

[0063] Step S1.3: The onboard processor processes the left image I L Extract two-dimensional feature points (x L ,y L ), in the right image I R Extract two-dimensional feature points (x R ,y R );

[0064] Step S1.4: The onboard processor processes the left image I L and right image I R The feature points are matched to generate two-dimensional matching point pairs (x L ,y L ) and (x R ,y R );

[0065] Step S1.5: The onboard processor calculates the parallax d, which is represented by equation (1):

[0066] d = x L -x R , (1);

[0067] Among them, x L The left image I L The two-dimensional feature points (x L ,y L )’s X-axis coordinate, x R For the right image I R The two-dimensional feature points (x R ,y R )’s X-axis coordinate, y L The left image I L The two-dimensional feature points (x L ,y L )’s Y-axis coordinate, y R For the right image I R The two-dimensional feature points (x R ,y R )’s Y-axis coordinate;

[0068] Step S1.6: Given the camera baseline b, calculate the depth Z, which is represented by equation (2):

[0069]

[0070] Where, f is the focal length;

[0071] Step S1.7: The onboard processor converts the two-dimensional feature point (x L ,y L ) is converted into three-dimensional feature points (X, Y, Z), and the three-dimensional feature points (X, Y, Z) are represented by formula (3):

[0072]

[0073] Among them, (c x , c y ) is the center coordinate of the binocular camera.

[0074] Among them, the step S1 of "generating relative relationship data between the drone body and surrounding obstacles" is specifically implemented as follows:

[0075] Step S1.8: The onboard processor calculates the relative position and attitude (which can be represented by the rotation angle) between the drone body and the obstacle;

[0076] Step S1.9: Generate the relative relationship data matrix R and the position vector T. The relative relationship data matrix R and the position vector T are represented by formula (4):

[0077]

[0078] Among them, the "relative relationship data is transmitted to the drone remote control terminal through the data link" in step S2 is specifically implemented as follows:

[0079] Step S2.1: The onboard processor packages the relative relationship data matrix R and the position vector T into a data packet D. The data packet D is represented by formula (5):

[0080] D = {R, T} (5);

[0081] Step S2.2: The onboard processor encodes the data packet D to generate an encoded data packet D;

[0082] Step S2.3: Transmit the encoded data packet to the drone remote control terminal via the data link.

[0083] Among them, the "UAV remote control terminal analyzes the relative relationship data" in step S3 is specifically implemented as follows:

[0084] Step S3.1: The drone remote controller obtains the encoded data packet D;

[0085] Step S3.2: The drone remote control terminal parses the encoded data packet D to obtain the relative relationship data matrix R and position vector T.

[0086] Among them, step S3 of "the drone remote control terminal guides the operator to view the relative relationship data" is specifically implemented as follows:

[0087] Step S3.3: The drone remote control terminal simulates and displays the relative position and posture of the drone body and surrounding obstacles on the display module according to the relative relationship data matrix R and the position vector T, and directly displays the relative relationship data matrix R and the position vector T on the display module;

[0088] Step S3.4: The UAV remote control terminal guides the operator to view the simulated relative position and posture of the UAV body and surrounding obstacles, as well as the directly displayed relative relationship data matrix R and position vector T;

[0089] Step S3.5: The UAV remote control terminal guides the operator whether to intervene in the relative relationship data matrix R and the position vector T. When the operator chooses not to intervene in the relative relationship data matrix R and the position vector T, step S3.6 is executed. When the operator chooses to intervene in the relative relationship data matrix R and the position vector T, step S3.7 is executed.

[0090] Wherein, step S3.6 is specifically implemented as the following steps:

[0091] Step S3.6: The drone remote control generates command I and sends command I back to the onboard processor. The command I in step S3.6 is represented by formula (6):

[0092] I = {R, T}, (6).

[0093] Wherein, step S3.7 is specifically implemented as the following steps:

[0094] Step S3.7: The drone remote controller generates instruction I and sends instruction I back to the recording processor. The instruction I in step S3.7 is represented by formula (7):

[0095] I = {R′, T′} (7);

[0096] Among them, R′ is the relative relationship data matrix after intervention, and T′ is the position vector T after intervention.

[0097] Preferably, the present embodiment further discloses a drone visual positioning and obstacle avoidance system, and the steps of the drone visual positioning and obstacle avoidance method using any one of the above technical solutions of the present embodiment.

[0098] Preferably, this embodiment further discloses a UAV visual positioning and obstacle avoidance system, including a binocular camera, an airborne processor, a data link and a UAV remote control terminal, wherein:

[0099] The binocular camera collects images of the drone's surrounding environment in real time. The onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the images, and generates data on the relative relationship between the drone body and surrounding obstacles.

[0100] The relative relationship data is transmitted to the UAV remote control terminal via the data link;

[0101] The drone remote control terminal parses the relative relationship data, guides the operator to view the relative relationship data, and guides the operator to choose whether to intervene in the relative relationship data, wherein:

[0102] When the operator chooses not to intervene in the relative relationship data, the UAV remote control terminal transmits a non-intervention relative relationship data instruction to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the (original) relative relationship data;

[0103] When the operator chooses to intervene in the relative relationship data, the operator manually corrects or adjusts the relative relationship data through the UAV remote control terminal to generate the relative relationship data after intervention. The UAV remote control terminal transmits the relative relationship data after intervention to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the relative relationship data after intervention.

[0104] It is worth mentioning that the technical solution of the UAV visual positioning and obstacle avoidance method disclosed in this embodiment is also applicable to the UAV visual positioning and obstacle avoidance system. The technical solution of the UAV visual positioning and obstacle avoidance system will not be repeated.

[0105] The following describes the overall concept of the UAV visual positioning and obstacle avoidance method and system disclosed in this embodiment.

[0106] Specifically, the binocular camera onboard the drone is fully utilized. According to the principle of the binocular camera, the drone body and surrounding obstacles are three-dimensionally modeled, thereby generating relative relationship data between the drone body and surrounding obstacles. The relative relationship data is transmitted to the drone remote control via a data link. The operator of the drone remote control chooses whether to intervene in the relative relationship data. When the operator chooses to intervene in the relative relationship data, the operator operates the drone remote control to generate the relative relationship data after intervention. Note that it does not directly intervene in the obstacle avoidance action of the drone. The relative relationship data after intervention is transmitted back to the drone, and the drone executes the preset obstacle avoidance method based on the relative relationship data after intervention, and on this basis, accurate obstacle positioning and obstacle avoidance are achieved.

[0107] It is worth mentioning that the technical features such as the preset obstacle avoidance method involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected according to the conventional methods in the field, and should not be regarded as the inventive point of the patent of this invention. The patent of this invention will not be further elaborated.

[0108] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for visual positioning and obstacle avoidance of an unmanned aerial vehicle, characterized in that: The following steps are involved: Step S1: The binocular camera collects images of the drone's surrounding environment in real time, and the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the images, and generates relative relationship data between the drone body and surrounding obstacles; Step S2: The relative relationship data is transmitted to the drone remote control terminal via a data link; Step S3: The drone remote control terminal parses the relative relationship data, and the drone remote control terminal guides the operator to view the relative relationship data, and guides the operator to choose whether to intervene in the relative relationship data, wherein: When the operator chooses not to intervene in the relative relationship data, the UAV remote control terminal transmits a non-intervention relative relationship data instruction to the UAV through the data link, so that the onboard processor executes a preset obstacle avoidance method according to the relative relationship data; When the operator chooses to intervene in the relative relationship data, the operator manually corrects or adjusts the relative relationship data through the UAV remote control terminal to generate the relative relationship data after intervention. The UAV remote control terminal transmits the relative relationship data after intervention to the UAV through the data link, so that the onboard processor executes the preset obstacle avoidance method according to the relative relationship data after intervention.

2. The UAV visual positioning and obstacle avoidance method according to claim 1, characterized in that: The "binocular camera collects image information of the drone's surrounding environment in real time" in step S1 is specifically implemented as follows: Step S1.1: The binocular camera acquires the left image I L and right image I R ; Step S1.2: The onboard processor processes the left image I L and right image I R Perform image correction to eliminate distortion.

3. The UAV visual positioning and obstacle avoidance method according to claim 2, characterized in that: Step S1 of "the onboard processor performs three-dimensional modeling of the drone body and surrounding obstacles based on the image" is specifically implemented as follows: Step S1.3: The onboard processor processes the left image I L Extract two-dimensional feature points (x L ,y L ), in the right image I R Extract two-dimensional feature points (x R ,y R ); Step S1.4: The onboard processor processes the left image I L and right image I R The feature points are matched to generate two-dimensional matching point pairs (x L ,y L ) and (x R ,y R ); Step S1.5: The onboard processor calculates the parallax d, which is represented by equation (1): d=x L -x R (1); Among them, x L For the left image I L The two-dimensional feature points (x L ,y L )’s X-axis coordinate, x R For the right image I R The two-dimensional feature points (x R ,y R )’s X-axis coordinate, y L The left image I L The two-dimensional feature points (x L ,y L )’s Y-axis coordinate, y R For the right image I R The two-dimensional feature points (x R ,y R )’s Y-axis coordinate; Step S1.6: Given the camera baseline b, calculate the depth Z, which is represented by equation (2): Where, f is the focal length; Step S1.7: The onboard processor converts the two-dimensional feature point (x L ,y L ) is converted into three-dimensional feature points (X, Y, Z), and the three-dimensional feature points (X, Y, Z) are represented by formula (3): Among them, (c x , c y ) is the center coordinate of the binocular camera.

4. The UAV visual positioning and obstacle avoidance method according to claim 3, characterized in that: The step S1 of "generating relative relationship data between the drone body and surrounding obstacles" is specifically implemented as follows: Step S1.8: The onboard processor calculates the relative position and attitude between the drone body and the obstacle; Step S1.9: Generate the relative relationship data matrix R and the position vector T. The relative relationship data matrix R and the position vector T are represented by formula (4):

5. The UAV visual positioning and obstacle avoidance method according to claim 4, characterized in that: The "relative relationship data is transmitted to the drone remote control terminal via the data link" in step S2 is specifically implemented as follows: Step S2.1: The onboard processor packages the relative relationship data matrix R and the position vector T into a data packet D. The data packet D is represented by formula (5): D = {R, T} (5); Step S2.2: The onboard processor encodes the data packet D to generate an encoded data packet D; Step S2.3: Transmit the encoded data packet to the drone remote control terminal via the data link.

6. The UAV visual positioning and obstacle avoidance method according to claim 5, characterized in that: The specific implementation of step S3 of "the drone remote control terminal analyzes the relative relationship data" is as follows: Step S3.1: The drone remote controller obtains the encoded data packet D; Step S3.2: The drone remote control terminal parses the encoded data packet D to obtain the relative relationship data matrix R and position vector T.

7. The UAV visual positioning and obstacle avoidance method according to claim 6, characterized in that: Step S3 of "the drone remote control terminal guides the operator to view the relative relationship data" is specifically implemented as follows: Step S3.3: The drone remote control terminal simulates and displays the relative position and posture of the drone body and surrounding obstacles on the display module according to the relative relationship data matrix R and the position vector T, and directly displays the relative relationship data matrix R and the position vector T on the display module; Step S3.4: The UAV remote control terminal guides the operator to view the simulated relative position and posture of the UAV body and surrounding obstacles, as well as the directly displayed relative relationship data matrix R and position vector T; Step S3.5: The UAV remote control terminal guides the operator whether to intervene in the relative relationship data matrix R and the position vector T. When the operator chooses not to intervene in the relative relationship data matrix R and the position vector T, step S3.6 is executed. When the operator chooses to intervene in the relative relationship data matrix R and the position vector T, step S3.7 is executed.

8. The UAV visual positioning and obstacle avoidance method according to claim 7, characterized in that: Step S3.6 is specifically implemented as follows: Step S3.6: The drone remote control generates command I and transmits command I back to the onboard processor. The command I in step S3.6 is represented by formula (6): I = {R, T} (6); Step S3.7 is specifically implemented as follows: Step S3.7: The drone remote controller generates instruction I and sends instruction I back to the recording processor. The instruction I in step S3.7 is represented by formula (7): I = {R′, T′} (7); Among them, R′ is the relative relationship data matrix after intervention, and T′ is the position vector T after intervention.

9. A UAV visual positioning and obstacle avoidance system, characterized in that: The steps of applying the UAV visual positioning and obstacle avoidance method described in any one of claims 1 to 8.

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