CN105867383A - Automatic collision preventing control method of USV - Google Patents

Automatic collision preventing control method of USV Download PDF

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CN105867383A
CN105867383A CN201610322414.5A CN201610322414A CN105867383A CN 105867383 A CN105867383 A CN 105867383A CN 201610322414 A CN201610322414 A CN 201610322414A CN 105867383 A CN105867383 A CN 105867383A
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obstacle
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王元慧
迟岑
丁福光
赵亮博
王莎莎
赵强
张赞
杨云龙
张博
张放
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Harbin Engineering University
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Abstract

一种USV自主避碰控制的方法,涉及海洋搜救和勘测技术领域,尤其涉及一种USV自主避碰控制的方法。本发明的目的是提供一种USV自主避碰控制的方法,通过整个避碰系统的各子系统间相互配合协作,实现无人艇在执行搜救、勘察等任务的时候能够自主搜索障碍物,根据障碍物的分布情况实施避碰策略。此方法按以下步骤进行:一、环境图像信息采集;二、测量障碍物与无人艇之间的距离;三、GPS定位系统测出无人艇与他无人艇的相对位置信息,测速仪测出无人艇和其他无人艇的运动速度;四、障碍物判断系统处理收到的信息;五、将海面干扰情况传送给遗传算法控制器;六、遗传算法控制器制定出避碰策略。本发明方法适用于海洋搜救和勘测技术领域。

A method for autonomous collision avoidance control of a USV relates to the technical field of marine search and rescue and survey, in particular to a method for autonomous collision avoidance control of a USV. The purpose of the present invention is to provide a method for USV autonomous collision avoidance control. Through the mutual cooperation and cooperation between the subsystems of the entire collision avoidance system, the unmanned vehicle can autonomously search for obstacles when performing tasks such as search and rescue and survey. The distribution of obstacles implements a collision avoidance strategy. This method is carried out according to the following steps: one, environmental image information collection; two, measure the distance between the obstacle and the unmanned boat; three, the GPS positioning system measures the relative position information of the unmanned boat and other unmanned boats, and Measure the movement speed of the unmanned boat and other unmanned boats; 4. The obstacle judgment system processes the received information; 5. Send the sea surface interference to the genetic algorithm controller; 6. The genetic algorithm controller formulates a collision avoidance strategy . The method of the invention is applicable to the technical field of marine search and rescue and survey.

Description

一种USV自主避碰控制的方法A method for autonomous collision avoidance control of USV

技术领域technical field

本发明涉及海洋搜救和勘测技术领域,尤其涉及一种USV自主避碰控制的方法。The invention relates to the technical field of marine search and rescue and survey, in particular to a method for autonomous collision avoidance control of a USV.

背景技术Background technique

无人艇(USV)是一种集自主规划,自主航行,可自主完成环境感知,目标探测等任务为一体的小型水面平台。海洋环境复杂多变,若想保持无人艇智能航行需要依靠其内部信息与外部环境完好的融合与交互。这种融合与交互的一个重要的前提条件就是无人艇能够进行自主避碰,并且能够在复杂的海洋环境下完成指定的使命。作为无人艇智能化的重要标志,无人艇的自主避碰技术不仅从一定程度上反应了海事无人艇智能化水平的高低,也是无人艇关键技术领域的重要研究内容。目前对于无人艇避碰的研究大多集中在避碰算法上,而对于整个避碰系统的各子系统间相互配合协作却鲜少涉及。An unmanned vehicle (USV) is a small surface platform that integrates autonomous planning, autonomous navigation, and autonomous completion of tasks such as environmental perception and target detection. The marine environment is complex and changeable. If you want to maintain the intelligent navigation of the unmanned vessel, you need to rely on the integration and interaction of its internal information and the external environment. An important prerequisite for this kind of integration and interaction is that the unmanned vehicle can perform autonomous collision avoidance, and can complete the designated mission in a complex marine environment. As an important symbol of the intelligence of unmanned boats, the autonomous collision avoidance technology of unmanned boats not only reflects the level of intelligence of maritime unmanned boats to a certain extent, but also an important research content in the key technical fields of unmanned boats. At present, most of the researches on collision avoidance of unmanned vehicles focus on the algorithm of collision avoidance, but seldom involve the cooperation among subsystems of the whole collision avoidance system.

发明内容Contents of the invention

本发明的目的是提供一种USV自主避碰控制的方法,通过整个避碰系统的各子系统间相互配合协作,实现无人艇在执行搜救、勘察等任务的时候能够自主搜索障碍物,根据障碍物的分布情况实施避碰策略,确保无人艇自主航行时的安全性。The purpose of the present invention is to provide a method for USV autonomous collision avoidance control. Through the mutual cooperation and cooperation between the subsystems of the entire collision avoidance system, the unmanned vehicle can autonomously search for obstacles when performing tasks such as search and rescue and survey. The distribution of obstacles implements a collision avoidance strategy to ensure the safety of the unmanned boat's autonomous navigation.

为实现一种USV自主避碰控制的方法,需采用一种USV自主避碰控制装置,包括:障碍物侦查装置,定位系统和遗传算法控制器,其中障碍物侦查装置包括摄像头、图像识别系统、测距仪、测速仪和障碍物判断系统;定位系统包括GPS定位系统和通信系统;遗传算法控制器包括风、浪和水流检测系统、控制器和执行机构。In order to realize a method for USV autonomous collision avoidance control, a USV autonomous collision avoidance control device is required, including: an obstacle detection device, a positioning system and a genetic algorithm controller, wherein the obstacle detection device includes a camera, an image recognition system, Rangefinder, speedometer and obstacle judgment system; positioning system includes GPS positioning system and communication system; genetic algorithm controller includes wind, wave and current detection system, controller and actuator.

通常影响无人艇避碰效果的主要因素为:无人艇的航向航速v0;障碍物的航向航速vT;无人艇与障碍物之间的距离DT;无人艇与障碍物的航向交角为障碍物相对于无人艇的真方位为θT;两船相对速度大小为vR;两船相对航向为无人艇与障碍物会遇时的最小通过距离为DCPA;无人艇与障碍物到达最近会遇点的时间为TCPA。Usually the main factor affecting the collision avoidance effect of the unmanned boat is: the course of the unmanned boat Velocity v 0 ; heading of obstacle Velocity v T ; the distance between the unmanned boat and the obstacle D T ; the heading angle between the unmanned boat and the obstacle is The true orientation of the obstacle relative to the unmanned boat is θ T ; the relative speed of the two ships is v R ; the relative course of the two ships is The minimum passing distance when the unmanned boat and the obstacle meet is DCPA; the time when the unmanned boat and the obstacle reach the closest meeting point is TCPA.

一种USV自主避碰控制的方法,按以下步骤进行:A method for USV autonomous collision avoidance control is carried out in the following steps:

一、摄像头将在以无人艇为中心的360°范围内拍摄环境图像,并把图像信息传递给图像识别系统;1. The camera will take pictures of the environment within a 360° range centered on the unmanned boat, and transmit the image information to the image recognition system;

二、图像识别系统将图像信息进行处理后将疑似障碍物信息传递给障碍物判断系统,同时测距仪测量无人艇500米内的障碍物与无人艇之间的距离,并将信息传递给障碍物判断系统;2. The image recognition system processes the image information and transmits the suspected obstacle information to the obstacle judgment system. At the same time, the rangefinder measures the distance between the obstacle and the unmanned boat within 500 meters of the unmanned boat, and transmits the information to the Obstacle judgment system;

三、GPS定位系统实时定位无人艇当前的位置,通信系统与其他无人艇建立联系,得本艇与其他无人艇的相对位置信息,由测速仪测出无人艇和其他无人艇的运动速度,将其他无人舰等效为障碍物,再将上述信息传递给障碍物判断系统;3. The GPS positioning system locates the current position of the unmanned boat in real time, the communication system establishes contact with other unmanned boats, and obtains the relative position information of the boat and other unmanned boats, and the speedometer measures the unmanned boat and other unmanned boats The movement speed of other unmanned ships is equivalent to obstacles, and then the above information is passed to the obstacle judgment system;

四、障碍物判断系统将收到的所有信息进行处理后将障碍物数据传递给遗传算法控制器;4. The obstacle judgment system processes all the received information and then transmits the obstacle data to the genetic algorithm controller;

五、风、浪和水流监测系统实时监测无人艇所处海域的海面干扰情况,并将数据传送给遗传算法控制器;5. The wind, wave and current monitoring system monitors the sea surface interference in the sea area where the unmanned boat is located, and transmits the data to the genetic algorithm controller;

六、遗传算法控制器根据障碍物数据制定出避碰策略并通过执行机构进行安全避碰。6. The genetic algorithm controller formulates a collision avoidance strategy based on the obstacle data and performs safe collision avoidance through the actuator.

本发明包括以下有益效果:The present invention comprises following beneficial effect:

1、本发明通过整个避碰系统的各子系统间相互配合协作,实现无人艇在执行搜救、勘察等任务的时候能够自主搜索障碍物,根据障碍物的分布情况实施避碰策略,确保无人艇自主航行时的安全性;1. The present invention realizes that the unmanned boat can autonomously search for obstacles when performing tasks such as search and rescue and survey through the mutual cooperation and cooperation between the subsystems of the entire collision avoidance system, and implements collision avoidance strategies according to the distribution of obstacles to ensure that no The safety of the manned boat when it sails autonomously;

2、本发明将无人艇的各个避碰子系统合理结合在一起,完成了无人艇从搜索附近航行环境,收集并分析障碍物信息,到制定避碰对策,完成对障碍物的躲避的一系列过程,有效提升了无人艇的智能化,减少了操控人员的工作量;2. The present invention rationally combines the various collision avoidance subsystems of the unmanned boat, and completes the process of the unmanned boat from searching the nearby navigation environment, collecting and analyzing obstacle information, formulating collision avoidance countermeasures, and completing the avoidance of obstacles. A series of processes have effectively improved the intelligence of the unmanned boat and reduced the workload of the operator;

3、当无人艇在执行搜救,勘测等任务时,可以根据本发明所述的方法对行进过程中的静态和动态障碍物进行安全避碰。3. When the unmanned boat is performing tasks such as search and rescue, surveying, etc., it can safely avoid collisions with static and dynamic obstacles in the process of traveling according to the method described in the present invention.

附图说明Description of drawings

图1为USV自主避碰控制装置结构框图;Figure 1 is a structural block diagram of the USV autonomous collision avoidance control device;

图2为USV与障碍物相对参数示意图;Figure 2 is a schematic diagram of the relative parameters of USV and obstacles;

图3为USV障碍物碰撞危险度解算流程图;Fig. 3 is a flowchart for calculating the USV obstacle collision risk;

图4为遗传算法流程图;Fig. 4 is a flow chart of genetic algorithm;

图5为无人艇躲避障碍物示意图。Fig. 5 is a schematic diagram of the unmanned boat avoiding obstacles.

具体实施方式detailed description

为使本发明的上述目的、特征和优点能够更加明显易懂,下面结合图1至图4和具体实施方式对本发明作进一步详细的说明,其中,图1为USV传感器安装配置俯视示意图,图2为USV与障碍物相对参数示意图,图3为USV障碍物碰撞危险度解算流程图。In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the present invention will be further described in detail below in conjunction with FIGS. It is a schematic diagram of the relative parameters of USV and obstacles, and Fig. 3 is a flow chart of calculating the collision risk of USV obstacles.

具体实施方式一、本实施方式所述的一种USV自主避碰控制的方法,按以下步骤进行:DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 1. A method for USV autonomous collision avoidance control described in this embodiment is carried out according to the following steps:

一、摄像头将在以无人艇为中心的360°范围内拍摄环境图像,并把图像信息传递给图像识别系统;1. The camera will take pictures of the environment within a 360° range centered on the unmanned boat, and transmit the image information to the image recognition system;

二、图像识别系统将图像信息进行处理后将疑似障碍物信息传递给障碍物判断系统,同时测距仪测量无人艇500米内的障碍物与无人艇之间的距离,并将信息传递给障碍物判断系统;2. The image recognition system processes the image information and transmits the suspected obstacle information to the obstacle judgment system. At the same time, the rangefinder measures the distance between the obstacle and the unmanned boat within 500 meters of the unmanned boat, and transmits the information to the Obstacle judgment system;

三、GPS定位系统实时定位无人艇当前的位置,通信系统与其他无人艇建立联系,得本艇与其他无人艇的相对位置信息,由测速仪测出无人艇和其他无人艇的运动速度,将其他无人舰等效为障碍物,再将上述信息传递给障碍物判断系统;3. The GPS positioning system locates the current position of the unmanned boat in real time, the communication system establishes contact with other unmanned boats, and obtains the relative position information of the own boat and other unmanned boats, and the speedometer measures the unmanned boat and other unmanned boats The movement speed of other unmanned ships is equivalent to obstacles, and then the above information is passed to the obstacle judgment system;

四、障碍物判断系统将收到的所有信息进行处理后将障碍物数据传递给遗传算法控制器;4. The obstacle judgment system processes all the received information and then transmits the obstacle data to the genetic algorithm controller;

五、风、浪和水流监测系统实时监测无人艇所处海域的海面干扰情况,并将数据传送给遗传算法控制器;5. The wind, wave and current monitoring system monitors the sea surface interference in the sea area where the unmanned boat is located, and transmits the data to the genetic algorithm controller;

六、遗传算法控制器根据障碍物数据制定出避碰策略并通过执行机构进行安全避碰。6. The genetic algorithm controller formulates a collision avoidance strategy based on the obstacle data and performs safe collision avoidance through the actuator.

本实施方式包括以下有益效果:This embodiment includes the following beneficial effects:

1、本实施方式通过整个避碰系统的各子系统间相互配合协作,实现无人艇在执行搜救、勘察等任务的时候能够自主搜索障碍物,根据障碍物的分布情况实施避碰策略,确保无人艇自主航行时的安全性;1. In this embodiment, through the mutual cooperation and cooperation between the subsystems of the entire collision avoidance system, the unmanned boat can autonomously search for obstacles when performing tasks such as search and rescue and survey, and implement collision avoidance strategies according to the distribution of obstacles to ensure The safety of unmanned boats when they sail autonomously;

2、本实施方式将无人艇的各个避碰子系统合理结合在一起,完成了无人艇从搜索附近航行环境,收集并分析障碍物信息,到制定避碰对策,完成对障碍物的躲避的一系列过程,有效提升了无人艇的智能化,减少了操控人员的工作量;2. In this embodiment, the various collision avoidance subsystems of the unmanned boat are reasonably combined, and the unmanned boat searches the surrounding navigation environment, collects and analyzes obstacle information, and formulates collision avoidance countermeasures to complete the avoidance of obstacles. A series of processes have effectively improved the intelligence of the unmanned boat and reduced the workload of the operator;

3、当无人艇在执行搜救,勘测等任务时,可以根据本实施方式所述的方法对行进过程中的静态和动态障碍物进行安全避碰。3. When the unmanned boat is performing tasks such as search and rescue, surveying, etc., it can safely avoid collisions with static and dynamic obstacles in the process of traveling according to the method described in this embodiment.

具体实施方式二、本实施方式是对具体实施方式一所述的一种USV自主避碰控制的方法的进一步说明,步骤一中所述拍摄环境图像的采集周期为0.05s,每次采集10个周期,共耗时0.5s。Specific Embodiment 2. This embodiment is a further description of the USV autonomous collision avoidance control method described in Specific Embodiment 1. The acquisition period of the shooting environment image described in step 1 is 0.05s, and 10 images are collected each time. cycle, taking a total of 0.5s.

具体实施方式三、本实施方式是对具体实施方式一或二所述的一种USV自主避碰控制的方法的进一步说明,步骤二中所述测距仪的测量测量周期为0.5s。Embodiment 3. This embodiment is a further description of the USV autonomous collision avoidance control method described in Embodiment 1 or 2. The measurement period of the rangefinder in step 2 is 0.5s.

具体实施方式四、本实施方式是对具体实施方式一至三之一所述的一种USV自主避碰控制的方法的进一步说明,步骤三中所述通信系统的通讯周期为0.5s。Embodiment 4. This embodiment is a further description of the USV autonomous collision avoidance control method described in Embodiments 1 to 3. The communication cycle of the communication system described in step 3 is 0.5s.

具体实施方式五、本实施方式是对具体实施方式一至四之一所述的一种USV自主避碰控制的方法的进一步说明,步骤三中所述GPS定位系统和通信系统获得的位置信息为:由GPS定位系统测得无人艇地理坐标为O(xO,yO),通信系统获得障碍物地理坐标为T(xT,yT),其中x轴为南北方向,y轴为东西方向。Embodiment 5. This embodiment is a further description of the method for USV autonomous collision avoidance control described in Embodiment 1 to Embodiment 4. The location information obtained by the GPS positioning system and the communication system in step 3 is: The geographic coordinates of the unmanned boat measured by the GPS positioning system are O(x O ,y O ), and the geographic coordinates of obstacles obtained by the communication system are T(x T ,y T ), where the x-axis is the north-south direction and the y-axis is the east-west direction .

具体实施方式六、本实施方式是对具体实施方式一至五之一所述的一种USV自主避碰控制的方法的进一步说明,步骤三中所述测速仪测得的运动速度为:无人艇运动速度矢量为v(vOx,vOy),障碍物运动速度矢量为v(vTx,vTy)。Specific Embodiment 6. This embodiment is a further description of the method for USV autonomous collision avoidance control described in one of specific embodiments 1 to 5. The speed measured by the speedometer in step 3 is: The motion speed vector is v(v Ox , v Oy ), and the obstacle motion speed vector is v(v Tx , v Ty ).

具体实施方式七、本实施方式是对具体实施方式一至六之一所述的一种USV自主避碰控制的方法的进一步说明,步骤四中所述信息处理的具体过程为:根据前三个步骤所获得的信息,解算出无人艇与障碍物的航向:Specific Embodiment 7. This embodiment is a further description of the USV autonomous collision avoidance control method described in one of specific embodiments 1 to 6. The specific process of information processing described in step 4 is: according to the first three steps The obtained information is used to calculate the course of the unmanned boat and obstacles:

无人艇航向:Unmanned boat heading:

其中: in:

根据无人艇与障碍物的地理坐标解算出无人艇与障碍物相对距离为:According to the geographic coordinate solution of the unmanned boat and the obstacle, the relative distance between the unmanned boat and the obstacle is calculated as:

DD. TT == (( xx TT -- xx Oo )) 22 ++ (( ythe y TT -- ythe y Oo )) 22

障碍物相对于无人艇的真方位为θTThe true orientation of the obstacle relative to the UAV is θ T :

θθ TT == aa rr cc tt aa nno xx TT -- xx Oo ythe y TT -- ythe y Oo ++ αα 22

无人艇相对于障碍物的真方位为θ0The true orientation of the UAV relative to the obstacle is θ 0 :

θθ 00 == aa rr cc tt aa nno xx Oo -- xx TT ythe y Oo -- ythe y TT ++ αα 22

其中, in,

障碍物的相位方位为αTThe phase orientation of the obstacle is α T :

障碍物相对于无人艇在x,y轴上的相对速度分量为:The relative velocity components of the obstacle relative to the UAV on the x and y axes are:

vv RR xx == vv TT xx -- vv Oo xx vv RR ythe y == vv TT ythe y -- vv Oo ythe y

障碍物相对于无人艇的相对航向:The relative heading of the obstacle relative to the UAV:

其中: in:

计算无人艇与目标船的最近会遇距离DCPA:Calculate the closest encounter distance DCPA between the UAV and the target ship:

计算到达会遇最近点的时间TCPA:Calculate the time to the closest point of encounter TCPA:

其中,vR为两船相对速度大小;Among them, v R is the relative speed of the two ships;

解算空间碰撞危险度udt为:The calculated space collision risk u dt is:

uu dd tt == 11 || DD. CC PP AA || << dd 11 11 22 -- 11 22 sthe s ii nno &lsqb;&lsqb; &pi;&pi; dd 22 -- dd 11 -- DD. CC PP AA (( dd 22 ++ dd 11 )) 22 &rsqb;&rsqb; dd 11 << || DD. CC PP AA || &le;&le; dd 22 00 dd 22 << || DD. CC PP AA ||

式中d1=1.5ρ(θT),d2=2d1In the formula, d 1 =1.5ρ(θ T ), d 2 =2d 1 ;

ρ(θT)由下式求得ρ(θ T ) is obtained by the following formula

解算时间碰撞危险度utT为:The calculation time collision risk u tT is:

当TCPA>0时:When TCPA>0:

uu tt TT == 11 TT CC PP AA &le;&le; 11 (( tt 22 -- TT CC PP AA tt 22 -- tt 11 )) 22 tt 11 &le;&le; TT CC PP AA &le;&le; tt 22 00 TT CC PP AA >> tt 22

当TCPA≤0时When TCPA≤0

uu tt TT == 11 TT CC PP AA &le;&le; 11 (( tt 22 ++ TT CC PP AA tt 22 -- tt 11 )) 22 tt 11 &le;&le; TT CC PP AA &le;&le; tt 22 00 TT CC PP AA >> tt 22

其中 in

由时间碰撞危险度和空间碰撞危险度得出无人艇与障碍物的综合碰撞危险度为utAccording to the time collision risk and space collision risk, the comprehensive collision risk of the unmanned vehicle and the obstacle is u t :

uu tt == uu dd tt &CirclePlus;&CirclePlus; uu tt TT

ut取值分为三种情况,其中,若udt=0时,ut=0;The value of u t is divided into three situations, among which, if u dt =0, u t =0;

若udt≠0,utT=0时,ut=0;If u dt ≠0, when u tT =0, u t =0;

若udt≠0,utT≠0时,ut=max(udt,utT)。If u dt ≠0, u t T ≠0, u t =max(u dt , u tT ) .

具体实施方式八、本实施方式是对具体实施方式一至七之一所述的一种USV自主避碰控制的方法的进一步说明,步骤五中所述遗传算法控制器根据障碍物数据制定出避碰策略的过程为:如图4所示,Embodiment 8. This embodiment is a further description of the USV autonomous collision avoidance control method described in one of Embodiments 1 to 7. The genetic algorithm controller described in step 5 formulates the collision avoidance method according to the obstacle data. The process of the strategy is: as shown in Figure 4,

1、对种群进行编码:1. Encode the population:

每个染色体代表无人艇的一条初始路径,在种群初始化的过程中,根据无人艇的初始位置和目标位置,自动生成一个个体数为n的种群,将无人艇从起点到终点的所有可行性路径进行编码,每一条路径都是种群中的一个个体;Each chromosome represents an initial path of the unmanned vehicle. In the process of population initialization, according to the initial position and target position of the unmanned vehicle, a population with n individuals is automatically generated, and all the unmanned vehicles from the starting point to the destination are automatically generated. Feasible paths are encoded, and each path is an individual in the population;

2、求解适应度函数:2. Solve the fitness function:

适应度函数的计算要考虑路径的长度、路径的光滑性以及路径的安全性;在将目标函数转化成适应度函数的时候一般遵循以下两个原则:The calculation of the fitness function should consider the length of the path, the smoothness of the path, and the safety of the path; when converting the objective function into a fitness function, the following two principles are generally followed:

(1)适应度值非负;(1) The fitness value is non-negative;

(2)优化过程中目标函数变化方向应与种群进化过程中适应度函数变化方向一致。(2) The change direction of the objective function in the optimization process should be consistent with the change direction of the fitness function in the population evolution process.

对于无人艇避碰路径选取的问题,可通过下式建立与目标函数存在映射关系的适应度函数:For the problem of collision avoidance path selection for unmanned vehicles, the fitness function that has a mapping relationship with the objective function can be established by the following formula:

F(x)=C-f(x)F (x) = Cf(x)

式中,F(x)为适应度函数;C为一个可调节参数,其取值应使适应度函数F(x)恒大于等于0;f(x)为优化问题的目标函数。In the formula, F (x) is the fitness function; C is an adjustable parameter whose value should make the fitness function F (x) always greater than or equal to 0; f(x) is the objective function of the optimization problem.

为确保F(x)不小于0,因此,建立适应度函数如下:In order to ensure that F (x) is not less than 0, therefore, the fitness function is established as follows:

Ff (( xx )) == CC mm aa xx -- ff (( xx )) ff (( xx )) << CC mm aa xx 00 ff (( xx )) &GreaterEqual;&Greater Equal; CC mm aa xx

式中Cmax为一个可调节的参数,Cmax可取目标函数f(x)理论上可能的最大值;在目标函数f(x)的选取上要考虑路径的长度、路径的光滑性以及路径的安全性。In the formula, C max is an adjustable parameter, and C max can take the theoretically possible maximum value of the objective function f(x); the length of the path, the smoothness of the path and the smoothness of the path should be considered in the selection of the objective function f(x). safety.

3、基本遗传操作3. Basic genetic manipulation

基本遗传操作包括选择、交叉、变异,具体操作步骤如下:Basic genetic operations include selection, crossover, and mutation. The specific steps are as follows:

(1)选择操作(1) Select operation

选择操作要从种群中选择适应度高的个体,淘汰适应度低的个体;个体i被选择的概率Pi与其适应度值成正比;采用轮盘赌模型,步骤如下:The selection operation is to select individuals with high fitness from the population and eliminate individuals with low fitness; the probability P i of individual i being selected is proportional to its fitness value; using the roulette model, the steps are as follows:

1)计算各染色体的适应度Fi,1≤i≤n;1) Calculate the fitness F i of each chromosome, 1≤i≤n;

2)累计所有染色体的适应度值,记录中间累加值Si:Si S1=F1,Si=Si-1+Fi2) Accumulate the fitness values of all chromosomes, and record the intermediate accumulated value S i : S i S 1 = F 1 , S i = S i-1 + F i ;

3)产生一个随机数x,0≤x≤Sn3) Generate a random number x, 0≤x≤S n ;

4)选择染色体,若Si-1<x≤Si,1≤i≤n,则第i个染色体被选中进入下一代种群;4) Select chromosomes, if S i-1 <x≤S i , 1≤i≤n, then the i-th chromosome is selected to enter the next generation population;

5)重复步骤3)和步骤4)直到获得足够多的染色体。5) Repeat step 3) and step 4) until enough chromosomes are obtained.

(2)交叉操作(2) Cross operation

交叉操作cross operation

对两个相互配对的染色体依据交叉概率按某种方式相互交换其部分基因,从而形成两个新的个体,交叉率在0.5~0.95之间。According to the crossover probability, two paired chromosomes exchange some of their genes in a certain way to form two new individuals, and the crossover rate is between 0.5 and 0.95.

(3)变异操作(3) Mutation operation

变异操作mutation operation

依据变异概率将个体编码中的某些基因值用其他基因值来替换,从而形成一个新的个体,变异率一般设置为0~0.5之间,以小概率进行基因突变。According to the mutation probability, some gene values in the individual code are replaced with other gene values to form a new individual. The mutation rate is generally set between 0 and 0.5, and the gene mutation is performed with a small probability.

为验证本发明的有益效果,作如下仿真实验:For verifying the beneficial effect of the present invention, do following simulation experiment:

如图5,本次实验共设置5个障碍物,位置坐标分别为(40,30),(80,80),(90,130),(120,120),(150,160),障碍物半径为11,11,6,11,11。无人艇起点为(10,10),终点坐标为(190,190)。As shown in Figure 5, a total of 5 obstacles are set up in this experiment, and the position coordinates are (40,30), (80,80), (90,130), (120,120), (150,160), and the obstacle radius is 11,11, 6, 11, 11. The starting point of the unmanned boat is (10,10), and the end coordinates are (190,190).

其中基本遗传操作的步骤:The steps of the basic genetic operation:

1、初始化:主要设置进化参数,设置最大进化代数和随机产生n个个体作为初始群体P(0);1. Initialization: mainly set the evolution parameters, set the maximum evolution algebra and randomly generate n individuals as the initial population P(0);

2、个体评价:通过一定大方法计算种群P(t)中各个个体的适应度,t代表代数;2. Individual evaluation: Calculate the fitness of each individual in the population P(t) through a certain method, and t represents the algebra;

3、选择:将选择算子作用于群体;3. Selection: apply the selection operator to the group;

4、交叉:将交叉算子作用于群体;4. Crossover: apply the crossover operator to the group;

5、变异:将变异算子作用与群体;5. Mutation: apply the mutation operator to the group;

6、算法终止条件:可以设置两种,一种是若进化代数已达到最大值,则以进化过程中的所得到的具有最大适应度个体作为最优解输出,终止计算;另一种则是设置一个误差,若种群中某个个体的误差已到达要求,则输出适应度最优的个体作为最优近似解,终止计算。6. Algorithm termination conditions: two types can be set, one is that if the evolution algebra has reached the maximum value, the individual with the maximum fitness obtained during the evolution process will be output as the optimal solution, and the calculation will be terminated; the other is Set an error, if the error of an individual in the population has reached the requirement, the individual with the best fitness will be output as the optimal approximate solution, and the calculation will be terminated.

本实验实现了发明效果,无人艇向目标点行驶过程中,通过各模块相互之间的协作,使无人艇在航行途中遇到障碍物时能够进行安全自主避碰,顺利到达预设目标点。This experiment achieved the effect of the invention. During the process of driving the unmanned boat to the target point, through the cooperation between the modules, the unmanned boat can safely and autonomously avoid collisions when encountering obstacles during navigation, and successfully reach the preset target point.

Claims (8)

1. A method for controlling autonomous collision avoidance of a USV (Universal Serial bus) is realized by adopting an autonomous collision avoidance control device of the USV, and the autonomous collision avoidance control device of the USV comprises the following steps: the system comprises an obstacle detection device, a positioning system and a genetic algorithm controller, wherein the obstacle detection device comprises a camera, an image identification system, a distance meter, a velocimeter and an obstacle judgment system; the positioning system comprises a GPS positioning system and a communication system; the genetic algorithm controller comprises a wind, wave and water flow detection system, a controller and an execution mechanism;
the method is characterized by comprising the following steps of:
the camera shoots an environment image within a 360-degree range taking an unmanned boat as a center, and transmits image information to an image recognition system;
secondly, the image recognition system processes the image information and then transmits the suspected obstacle information to the obstacle judgment system, and meanwhile, the range finder measures the distance between the unmanned ship and an obstacle within 500 meters of the unmanned ship and transmits the information to the obstacle judgment system;
thirdly, a GPS positioning system positions the current position of the unmanned ship in real time, a communication system establishes contact with other unmanned ships to obtain relative position information of the unmanned ship and other unmanned ships, a velocimeter measures the movement speed of the unmanned ship and other unmanned ships, the unmanned ships are equivalent to obstacles, and the information is transmitted to an obstacle judgment system;
fourthly, the barrier judgment system processes all the received information and then transmits the barrier data to the genetic algorithm controller;
fifthly, monitoring the sea surface interference condition of the sea area where the unmanned ship is located by a wind, wave and water flow monitoring system in real time, and transmitting data to a genetic algorithm controller;
and sixthly, the genetic algorithm controller works out a collision prevention strategy according to the barrier data and carries out safe collision prevention through an execution mechanism.
2. The method for autonomous collision avoidance control of an USV according to claim 1, wherein in the first step, the capturing period of the captured environment image is 0.05s, and each capturing period is 10 periods, which takes 0.5 s.
3. The method for autonomous collision avoidance control of an USV according to claim 1 or 2, wherein in step two, the measuring period of the distance meter is 0.5 s.
4. The method as claimed in claim 3, wherein the communication period of the communication system in step three is 0.5 s.
5. The method for autonomous collision avoidance control of an USV according to claim 4, wherein the position information obtained by the GPS positioning system and the communication system in step three is: the geographic coordinate of the unmanned ship is O (x) measured by a GPS positioning systemO,yO) The communication system obtains the geographic coordinate of the obstacle as T (x)T,yT) Wherein the x-axis is north-south and the y-axis is east-west.
6. The method for autonomous collision avoidance control of a USV according to claim 5, wherein the moving speed measured by the speedometer in step three is: the motion velocity vector of the unmanned ship is v (v)Ox,vOy) The obstacle motion velocity vector is v (v)Tx,vTy)。
7. The method for autonomous collision avoidance control of an USV according to claim 6, wherein the specific process of the information processing in step four is as follows: and (3) solving the course of the unmanned ship and the barrier according to the information obtained in the previous three steps:
course of the unmanned ship:
wherein:
the relative distance between the unmanned boat and the obstacle is calculated according to the geographic coordinates of the unmanned boat and the obstacle as follows:
D T = ( x T - x O ) 2 + ( y T - y O ) 2
the true orientation of the obstacle relative to the unmanned surface is θT
&theta; T = arctan x T - x O y T - y O + &alpha; 2
The true orientation of the unmanned vehicle relative to the obstacle is theta0
&theta; 0 = arctan x O - x T y O - y T + &alpha; 2
Wherein,
the phase orientation of the obstacle is αT
The relative velocity components of the obstacle relative to the unmanned vehicle in the x, y axes are:
v R x = v T x - v O x v R y = v T y - v O y
relative heading of the obstacle with respect to the unmanned boat:
wherein:
calculating the nearest meeting distance DCPA between the unmanned ship and the target ship:
calculate the time to reach the meeting closest point TCPA:
wherein v isRThe relative speed of the two ships is large or small;
resolving the space collision risk udtComprises the following steps:
u d t = 1 | D C P A | < d 1 1 2 - 1 2 s i n &lsqb; &pi; d 2 - d 1 - D C P A ( d 2 + d 1 ) 2 &rsqb; d 1 < | D C P A | &le; d 2 0 d 2 < | D C P A |
in the formula d1=1.5ρ(θT),d2=2d1
ρ(θT) Obtained from the following formula
Solving the time collision risk utTComprises the following steps:
when TCPA > 0:
u t T = 1 T C P A &le; 1 ( t 2 - T C P A t 2 - t 1 ) 2 t 1 &le; T C P A &le; t 2 0 T C P A > t 2
when TCPA is less than or equal to 0
u t T = 1 T C P A &le; 1 ( t 2 + T C P A t 2 - t 1 ) 2 t 1 &le; T C P A &le; t 2 0 T C P A > t 2
Wherein
Obtaining the comprehensive collision risk degree u of the unmanned ship and the barrier according to the time collision risk degree and the space collision risk degreet
u t = u d t &CirclePlus; u t T
utThe value is divided into three cases,wherein, if udtWhen equal to 0, ut=0;
If udt≠0,utTWhen equal to 0, ut=0;
If udt≠0,utTWhen not equal to 0, ut=max(udt,utT)。
8. The method for autonomous collision avoidance control of an USV according to claim 7, wherein the process of the genetic algorithm controller to make a collision avoidance strategy according to the obstacle data in the fifth step is:
1. encoding the population:
each chromosome represents an initial path of the unmanned ship, in the population initialization process, a population with the number of individuals of n is automatically generated according to the initial position and the target position of the unmanned ship, all feasible paths from the starting point to the end point of the unmanned ship are coded, and each path is an individual in the population;
2. solving a fitness function:
the calculation of the fitness function considers the length of the path, the smoothness of the path and the safety of the path; the following two principles are followed when converting the objective function into the fitness function:
(1) the fitness value is non-negative;
(2) the change direction of the target function in the optimization process is consistent with the change direction of the fitness function in the population evolution process;
for the problem of selecting the collision avoidance path of the unmanned ship, a fitness function with a mapping relation with a target function is established through the following formula:
F(x)=C-f(x)
in the formula, F(x)Is a fitness function; c is an adjustable parameter whose value is such that the fitness function F(x)Constantly greater than or equal to 0; (x) is an objective function of the optimization problem;
to ensure F(x)Not less than 0, the fitness function is established as follows:
F ( x ) = C m a x - f ( x ) f ( x ) < C m a x 0 f ( x ) &GreaterEqual; C m a x
in the formula CmaxIs an adjustable parameter, CmaxTaking the maximum value theoretically possible for the objective function f (x); the length of the path, the smoothness of the path and the safety of the path are considered in the selection of the target function f (x);
3. basic genetic manipulation
The basic genetic operation comprises selection, crossing and mutation, and comprises the following specific operation steps:
(1) selection operation
Selecting individuals with high fitness from the population, and eliminating individuals with low fitness; probability P of individual i being selectediProportional to its fitness value; adopting a roulette model, comprising the following steps:
1) calculating fitness of each chromosome Fi,1≤i≤n;
2) Integrating fitness values of all chromosomes, and recording intermediate integrated values Si:SiS1=F1,Si=Si-1+Fi
3) Generating a random number x, 0 ≦ x ≦ Sn
4) Selecting chromosomes if Si-1<x≤SiIf i is more than or equal to 1 and less than or equal to n, the ith chromosome is selected to enter the next generation population;
5) repeating steps 3) and 4) until enough chromosomes are obtained;
(2) crossover operation
Crossover operation
Exchanging partial genes of two paired chromosomes according to a certain mode according to the cross probability so as to form two new individuals, wherein the cross rate is 0.5-0.95;
(3) mutation operation
Mutation operation
And replacing some gene values in the individual codes with other gene values according to the mutation probability so as to form a new individual, wherein the mutation rate is set to be between 0 and 0.5, and the gene mutation is carried out with small probability.
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