CN111176275B - AUV collision avoidance function verification method - Google Patents

AUV collision avoidance function verification method Download PDF

Info

Publication number
CN111176275B
CN111176275B CN201911379876.0A CN201911379876A CN111176275B CN 111176275 B CN111176275 B CN 111176275B CN 201911379876 A CN201911379876 A CN 201911379876A CN 111176275 B CN111176275 B CN 111176275B
Authority
CN
China
Prior art keywords
auv
collision avoidance
avoidance function
navigation
barrier
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911379876.0A
Other languages
Chinese (zh)
Other versions
CN111176275A (en
Inventor
周俊
熊华乔
徐从营
邢安安
张宝贵
牛群峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
710th Research Institute of CSIC
Original Assignee
710th Research Institute of CSIC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 710th Research Institute of CSIC filed Critical 710th Research Institute of CSIC
Priority to CN201911379876.0A priority Critical patent/CN111176275B/en
Publication of CN111176275A publication Critical patent/CN111176275A/en
Application granted granted Critical
Publication of CN111176275B publication Critical patent/CN111176275B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses an AUV collision avoidance function verification method, which completes collision avoidance function verification by obtaining AUV related navigation parameters, obtaining AUV obstacle detection identification distance, and performing operations of approaching obstacle simulation avoidance, obstacle crossing collision avoidance and the like. The method can reduce the test risk and improve the test efficiency, and has the characteristics of simplicity, high efficiency, clear and feasible method, lower implementation difficulty and safe verification process.

Description

AUV collision avoidance function verification method
Technical Field
The invention belongs to the technical field of AUV (autonomous underwater vehicle), and particularly relates to an AUV collision avoidance function verification method.
Background
With the development and maturity of AUV technology, practical application in the fields of ocean exploration, military reconnaissance and the like has gradually started. The AUV works in the deep ocean, the ocean environment information is difficult to master completely in advance, even no master is available, accidental objects such as reefs, steep walls, sunken ships and the like on the route are difficult to avoid completely in the route planning link of the AUV, collision accidents of the AUV can be caused, consequences are difficult to bear, and the application and development of the AUV are influenced. Therefore, advanced AUVs usually have certain collision avoidance functions by configuring obstacle detection devices such as collision avoidance sonar, and the like, so that the survival capability and the environmental adaptability of the AUV equipment are improved. Therefore, in the process of developing the AUV, the collision avoidance capability of the development of the AUV is essential to be verified. Obviously, in order to verify the sufficiency and the actual condition, some real obstacles must be selected, and the autonomous navigation path of the AUV is planned to pass through the obstacles so as to verify that the collision avoidance function design meets the requirements, but once a defect exists in the design, the AUV to be tested collides with the selected obstacles, and a serious test accident occurs. In conclusion, the AUV collision avoidance function verification risk is high, and the accident consequence is serious.
Disclosure of Invention
In view of the above, the invention provides an AUV collision avoidance function verification method, which is simple, efficient, clear, feasible, low in implementation difficulty and safe in verification process.
The invention is realized by the following technical scheme:
an AUV collision avoidance function verification method comprises the following steps:
the method comprises the following steps: obtaining an AUV parking turning deceleration distance;
step two: selecting a proper target barrier according to an collision avoidance detection device configured by the AUV;
step three: planning a section of air route close to a target obstacle, and obtaining an AUV obstacle detection identification distance through AUV real navigation without starting an AUV collision avoidance function;
step four: if the AUV obstacle detection identification distance is larger than the parking turning deceleration distance, performing the fifth step, otherwise, increasing the detection identification distance or reducing the parking turning deceleration distance;
step five: planning an autonomous air route, starting an AUV collision avoidance function, and simulating and verifying the AUV collision avoidance function by approaching a target obstacle; if the AUV identifies the target barrier through the collision avoidance detection device before approaching the terminal point, and implements autonomous avoidance, and turns back in advance, the collision avoidance function of the AUV meets the requirement, and the sixth step is carried out, otherwise, the detection identification distance is increased or the parking turning deceleration distance is reduced until the collision avoidance function of the AUV meets the requirement;
step six: and planning an autonomous navigation path for passing through the barrier, starting the collision avoidance function by the AUV, navigating according to the planned navigation path, and verifying the collision avoidance function of the AUV.
Furthermore, when the autonomous navigation path is planned, the AUV sets the navigation depth to be shallower than the difference between the barrier depth and the navigation depth control error.
Further, the second step of the method for selecting the target obstacle satisfies the following requirements: the head-on width of the target barrier meets the detection and identification requirements of the collision prevention detection device; the depth of the target barrier is the sum of the AUV navigation depth and the navigation depth control error, and meanwhile, the target barrier is within the range of the vertical opening angle of the collision avoidance detection device.
Has the beneficial effects that:
the method adopts a progressive method, finishes the verification of the collision prevention function by obtaining the AUV related navigation parameters, obtaining the AUV obstacle detection identification distance and carrying out the operations of approaching the obstacle, simulating and avoiding the obstacle, passing through the obstacle, avoiding the collision and the like, can reduce the test risk and improve the test efficiency, and has the characteristics of simplicity, high efficiency, clear and feasible method, lower implementation difficulty and safe verification process.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of obtaining AUV-related navigation parameters in the embodiment;
FIGS. 3 (a), (b) are schematic diagrams of selecting a suitable target obstacle in the embodiment;
FIG. 4 is a schematic diagram of obtaining AUV obstacle detection identification distances in the embodiment;
FIG. 5 is a schematic diagram illustrating simulated avoidance of approaching obstacles in an embodiment;
fig. 6 is a schematic diagram of the verification collision avoidance function when passing through the obstacle in the embodiment.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The embodiment provides a verification method for an AUV collision avoidance function, as shown in fig. 1, the verification method includes: step one, obtaining relevant navigation parameters of an AUV; selecting a proper target barrier; step three, obtaining AUV obstacle detection and identification distance; step four, evaluating whether the collision avoidance function meets the requirements; approaching obstacles to simulate avoidance; and step six, passing through the barrier to verify the collision avoidance function.
The method comprises the following specific steps:
step one, obtaining relevant main navigation parameters of the AUV through the actual navigation of the AUV, wherein the relevant main navigation parameters mainly comprise a parking turning deceleration distance ld and a navigation depth control error dr. The parking turning deceleration distance ld is obtained by planning a forward sailing and then returning route, as shown in fig. 2, after the AUV reaches the point B, the AUV parks and rotates, the AUV will continue to move forward for a certain distance due to inertia in actual sailing, and the distance between the projection point of the farthest forward point to the A-B route and the point B is the parking turning deceleration distance ld. And after the AUV navigates stably, the maximum difference value between the actual navigation depth and the set navigation depth is the navigation depth control error dr.
And step two, selecting target obstacles required for verification according to the performances of collision prevention detection devices such as collision prevention sonar carried by the AUV. As shown in fig. 3 (a) and (b), the selection principle is as follows: pixels formed in the sonar by the head-on width of the target barrier in the horizontal plane direction meet the requirement of detecting and identifying the barrier; in the vertical plane direction, the depth h of the target barrier is the sum d + dr of the navigation depth d of the AUV and the navigation depth control error dr, and the detectable range Bq tan a of the collision-preventing sonar in the vertical direction is larger than the navigation depth control error dr.
And step three, acquiring the distance ds for the AUV to detect and identify the obstacle by taking the target obstacle selected in the step two as a target in a real navigation mode. As shown in fig. 4, an AUV route D-E-F is planned, where point E is a deceleration distance ld of the parking turn obtained in step one from the target obstacle, and point F is a point after turning in a direction away from the obstacle. The AUV does not start the collision avoidance function, navigates according to the planned route to obtain a G route point capable of detecting and identifying the obstacle, and the distance ds of the AUV capable of detecting and identifying the obstacle can be obtained by combining the coordinate point of the obstacle.
Step four, comparing the parking turning deceleration distance ld obtained in the step one with the detectable identification barrier distance ds obtained in the step three, if the AUV detectable identification barrier distance ds is larger than the parking turning deceleration distance ld, determining that the AUV collision avoidance function meets the requirement, and continuing to implement approaching barrier simulation avoidance, namely, performing the step five, otherwise, optimizing the design of the AUV collision avoidance function, increasing the detection identification distance ds or reducing the parking turning deceleration distance ld, and performing the step five after the AUV collision avoidance function meets the requirement.
Step five, taking the target barrier selected in the step two as a target, planning a route H-I-J which is close to the barrier and then returns, as shown in fig. 5, wherein the distance between the point I and the target barrier is the parking turning deceleration distance ld obtained in the step one, starting an AUV collision avoidance function, verifying whether the AUV can start to turn back automatically at the point K of detecting and recognizing the target barrier, executing a collision avoidance action, if the collision avoidance action can be executed automatically, continuously implementing the barrier crossing verification collision avoidance function, namely performing the step six, otherwise, optimizing the design of the AUV collision avoidance function, increasing the detection recognition distance ds or reducing the parking turning deceleration distance ld;
and step six, taking the target barrier selected in the step two as a target, planning a route M-N passing through the barrier, setting the navigation depth d of the AUV to be shallower than the barrier depth and the difference (h-dr) of the navigation depth control errors, as shown in fig. 6, starting an AUV collision avoidance function, verifying whether the AUV can start autonomous steering to bypass at the point P where the target barrier is detected and identified, executing barrier avoidance action, returning to the planned route after collision avoidance is completed, and thus completing AUV collision avoidance function verification.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (2)

1. An AUV collision avoidance function verification method is characterized by comprising the following steps:
the method comprises the following steps: obtaining an AUV parking turning deceleration distance;
step two: selecting a proper target barrier according to the performance of the collision avoidance detection device configured by the AUV; the selection method of the target barrier meets the following requirements: the head-on width of the target barrier meets the detection and identification requirements of the collision prevention detection device; the depth of the target barrier is the sum of the AUV navigation depth and the navigation depth control error, and meanwhile, the target barrier is in the range of the vertical opening angle of the collision avoidance detection device;
step three: planning a section of air route close to a target obstacle in an actual navigation mode, and obtaining an AUV obstacle detection identification distance through AUV actual navigation without starting an AUV collision avoidance function;
step four: if the AUV obstacle detection identification distance is larger than the parking turning deceleration distance, performing the fifth step, otherwise, increasing the detection identification distance or reducing the parking turning deceleration distance;
step five: planning an autonomous air route, starting an AUV collision avoidance function, and simulating and verifying the AUV collision avoidance function by approaching a target obstacle; if the AUV identifies the target barrier through the collision avoidance detection device before approaching the terminal point, and implements autonomous avoidance, and turns back in advance, the collision avoidance function of the AUV meets the requirement, and the sixth step is carried out, otherwise, the detection identification distance is increased or the parking turning deceleration distance is reduced until the collision avoidance function of the AUV meets the requirement;
step six: and planning an autonomous navigation path for passing through the barrier, starting the collision avoidance function by the AUV, navigating according to the planned navigation path, and verifying the collision avoidance function of the AUV.
2. The AUV collision avoidance function verification method according to claim 1, wherein the AUV sets a navigation depth shallower than a difference between the barrier depth and the navigation depth control error when planning the autonomous route.
CN201911379876.0A 2019-12-27 2019-12-27 AUV collision avoidance function verification method Active CN111176275B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911379876.0A CN111176275B (en) 2019-12-27 2019-12-27 AUV collision avoidance function verification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911379876.0A CN111176275B (en) 2019-12-27 2019-12-27 AUV collision avoidance function verification method

Publications (2)

Publication Number Publication Date
CN111176275A CN111176275A (en) 2020-05-19
CN111176275B true CN111176275B (en) 2022-10-04

Family

ID=70650442

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911379876.0A Active CN111176275B (en) 2019-12-27 2019-12-27 AUV collision avoidance function verification method

Country Status (1)

Country Link
CN (1) CN111176275B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114419943B (en) * 2021-12-29 2024-05-10 宜昌测试技术研究所 Multi-AUV semi-offline tactic deduction system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101408772A (en) * 2008-11-21 2009-04-15 哈尔滨工程大学 AUV intelligent touching-avoiding apparatus and method
CN104316932A (en) * 2014-11-05 2015-01-28 哈尔滨工程大学 Height-determined sailing system and method of UUV which reaches seabed for working
CN109782807A (en) * 2019-03-08 2019-05-21 哈尔滨工程大学 A kind of AUV barrier-avoiding method under back-shaped obstacle environment

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101408772A (en) * 2008-11-21 2009-04-15 哈尔滨工程大学 AUV intelligent touching-avoiding apparatus and method
CN104316932A (en) * 2014-11-05 2015-01-28 哈尔滨工程大学 Height-determined sailing system and method of UUV which reaches seabed for working
CN109782807A (en) * 2019-03-08 2019-05-21 哈尔滨工程大学 A kind of AUV barrier-avoiding method under back-shaped obstacle environment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
深海水下机器人实时避碰方法研究;陈巩;《中国优秀硕士学位论文全文数据库•信息科技辑》;20170515;正文第50-60页 *

Also Published As

Publication number Publication date
CN111176275A (en) 2020-05-19

Similar Documents

Publication Publication Date Title
Tuncali et al. Simulation-based adversarial test generation for autonomous vehicles with machine learning components
EP3690717A1 (en) Learning method and learning device, and testing method and testing device for detecting parking spaces by using point regression results and relationship between points to thereby provide an auto-parking system
CN108445879B (en) Unmanned ship obstacle avoidance method based on collision danger prediction area
EP3686779B1 (en) Method and device for attention-based lane detection without post-processing by using lane mask and testing method and testing device using the same
KR102373487B1 (en) Learning method and learning device for determining whether to switch mode of vehicle from manual driving mode to autonomous driving mode by performing trajectory-based behavior analysis on recent driving route
US20210207977A1 (en) Vehicle position estimation device, vehicle position estimation method, and computer-readable recording medium for storing computer program programmed to perform said method
Belbachir et al. Simulation-driven validation of advanced driving-assistance systems
Zhao et al. Dynamic motion planning for autonomous vehicle in unknown environments
WO2021087242A1 (en) Training trajectory scoring neural networks to accurately assign scores
US20230150550A1 (en) Pedestrian behavior prediction with 3d human keypoints
US11703344B2 (en) Landmark location estimation apparatus and method, and computer-readable recording medium storing computer program programmed to perform method
Chen et al. An enhanced dynamic Delaunay triangulation-based path planning algorithm for autonomous mobile robot navigation
KR102569900B1 (en) Apparatus and method for performing omnidirectional sensor-fusion and vehicle including the same
Revilloud et al. An improved approach for robust road marking detection and tracking applied to multi-lane estimation
CN109948289B (en) Vehicle autonomous parking function evaluation method based on maximum global encroachment rate search
CN111176275B (en) AUV collision avoidance function verification method
CN113034970A (en) Safety system, automated driving system and method thereof
CN108387917A (en) Blind-guiding method, electronic equipment and computer program product
JP2021076584A (en) Navigation switch facility for golf course automatic operating car
KR102110939B1 (en) Apparatus and method for virtual ship traffic reproduction
Vagale et al. Evaluation of path planning algorithms of autonomous surface vehicles based on safety and collision risk assessment
Jha et al. Autonomous mooring towards autonomous maritime navigation and offshore operations
KR101750615B1 (en) Device and method for object recognition and detection
Woo et al. Design and simuation of a vehicle test bed based on intelligent transport systems
CN111460879B (en) Neural network operation method using grid generator and device using the same

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant