A collision avoidance capability test method and system of an unmanned cluster system

Abstract

The application discloses a collision avoidance capability test method and system of an unmanned cluster system, and belongs to the field of automatic control. The application takes the internal collision avoidance rate and the external obstacle avoidance degree as the evaluation scale of the collision avoidance capability of the unmanned cluster, and evaluates the collision avoidance capability through the distance observation between the cluster individuals and the obstacles. When testing the obstacle avoidance control capability of the unmanned aerial vehicle cluster formation, the cluster size, the individual position, the individual speed and the obstacle position are acquired in real time, the internal collision avoidance rate and the external obstacle avoidance degree of the unmanned cluster formation system are calculated, the evaluation index is obtained by combining the internal collision avoidance rate and the external obstacle avoidance degree, and thus the collision avoidance control capability of the unmanned cluster system is tested. The application provides a feasible method for the intelligent evaluation of the unmanned cluster system, can quickly and effectively evaluate the collision avoidance control capability of the unmanned cluster formation, and is helpful to improving the effectiveness and stability of the obstacle avoidance control of the unmanned cluster system.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of unmanned cluster's collision avoidance ability test method and system, belong to automatic control field. BACKGROUND

[0002] In recent years, the development of unmanned system is rapid, unmanned cluster has caused extensive attention, has broad application prospect in military, agriculture, manufacturing, transportation, education, medical treatment, finance and many other fields.In the actual operation process of unmanned cluster, complex and changeable real environment is the problem that cluster navigation must consider.Currently, different types of obstacle avoidance algorithms are proposed by most scholars for the obstacle avoidance control problem of unmanned cluster, including optimization algorithm, artificial potential field method, speed obstacle algorithm, etc.In order to realize the safety and stability of unmanned cluster movement, the control ability index of obstacle avoidance algorithm has also been more researched.For example, the evaluation target function is constructed in combination with vehicle motion state [Fu X, Jiang Y, Huang D, et al.Intelligent computing budget allocation for on-road trajectory planning based on candidate curves[J].Frontiers of Information Technology & Electronic Engineering, 2016, 17 (6) :553-565.], Specific indicators include total time, acceleration, acceleration increment, illegal speed and collision safety index;For example, the unmanned vehicle obstacle avoidance evaluation target function is constructed based on the angle of path [Li X, Sun Z, Liu D, et al.Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path[C].17th International IEEE Conference on Intelligent Transportation Systems (ITSC).IEEE, 2014:725-731.], Specific indicators include the distance between path and road boundary, the curvature and length of path.But most of the index function is only used to select the optimal obstacle avoidance path, and does not involve the evaluation problem of collision avoidance ability. SUMMARY

[0003] Invention purposes: To solve the above problems, the application discloses an anti-collision ability testing method and system of an unmanned cluster system, which is used to improve the effectiveness and stability of the obstacle avoidance control of the unmanned cluster system.

[0004] Technical solutions: In order to achieve the above purposes, the application provides an anti-collision ability testing method of an unmanned cluster, which comprises the following steps:

[0005] Real-time obtaining of unmanned cluster formation data, including cluster size, individual position, speed and obstacle position;

[0006] Calculating the internal anti-collision rate and external obstacle avoidance degree of the cluster individuals, combining the internal anti-collision rate and the external obstacle avoidance degree to obtain an evaluation index, so as to test the anti-collision control ability of the unmanned cluster system; the internal anti-collision rate refers to the ability of the individuals in the cluster to avoid collision with each other during the movement of the cluster; the external obstacle avoidance degree refers to the extreme value ratio of the distance between all individuals in the cluster and the obstacle during the process of avoiding the obstacle.

[0007] As a preferred, the expression form of the internal anti-collision rate of the cluster individuals is:

[0008]

[0009] As can be seen from the above formula, the internal anti-collision rate has only two states, and the internal anti-collision rate is 100% if no collision occurs in the formation during the entire obstacle avoidance process; otherwise, it is 0.

[0010] As a preferred, the expression form of the external obstacle avoidance degree between the cluster and the obstacle is as follows:

[0011]

[0012] Wherein, represents the minimum distance between the cluster formation and the obstacle during the entire control time, represents the maximum distance between the cluster formation and the obstacle during the entire control time; represents the minimum value of the distance between each individual and the obstacle during the entire control time, and n represents the number of intelligent agents; the greater the external obstacle avoidance rate, the better the obstacle avoidance effect, and W [0, 1].

[0013] As a preferred, the evaluation index calculation form of the anti-collision ability is as follows:

[0014] p=sign(N)*[ω1*N+ω2*W]

[0015] Wherein, ω1 and ω2 are the weighted weights of the internal obstacle avoidance rate and the external obstacle avoidance degree. Due to the numerical characteristics of N and W, p [0, 1].

[0016] As a preferred, the testing is performed through a simulation testing system, which specifically comprises:

[0017] Step 1, initialize the unmanned cluster formation test data set, including the initial position of the agent initial speed static obstacle position and dynamic obstacles and the like, where i = 1, 2, …, I, j = 1, 2, …, J, q = 1, 2, …, Q; I represents the number of individuals, J and P represent the number of static and dynamic obstacles, respectively;

[0018] Step 2, obtain real-time cluster individual position in the simulation test system based on ROS speed and obstacle position and the like, to determine whether obstacle avoidance is needed;

[0019] Step 3, change the formation speed to perform obstacle avoidance control using the relative obstacle velocity method (RVO) based cluster formation collision avoidance control technology;

[0020] Step 4, output the distance observation between the formation and the obstacles, obtain the distance between each individual inside the formation and the minimum and maximum distance between the cluster formation and the obstacles, calculate the internal collision avoidance rate and the external obstacle avoidance degree, and determine the evaluation level of the collision avoidance control ability of the unmanned cluster system.

[0021] As a preferred, the specific representation form of the velocity obstacle method (VO) and RVO is as follows:

[0022]

[0023] where v A and v B represent the speeds of agents A and B, respectively, is the set of speeds of A when A and B collide, λ(P A , v A -v B ) = {P A +t(v A -v B )|t≥0} represents a ray with P A as the vertex and v A -v B as the direction, represents the Minkowski sum, A = {-a | a ∈ A}, a and b represent points on agents A and B, respectively;

[0024]

[0025] where v' Ais the non-selective speed for avoiding collision, current speed of A with the average speed of any speed in the group, next time speed as long as not belonging to It can be ensured that no collision occurs.

[0026] As preferred, the evaluation ability level is divided as follows:

[0027] Collision Avoidance Level I (Weak) II (Medium) III (Strong) Threshold Range 0～0.4 0.4～0.7 0.7～1

[0028] According to the evaluation index threshold range given in the above table, the level of the collision avoidance ability of the unmanned cluster system is determined.

[0029] Based on the same inventive concept, the present application provides a collision avoidance ability test system of an unmanned cluster system, comprising: a data acquisition module, configured to obtain unmanned cluster formation data in real time, including cluster size, individual position, speed and obstacle position; a collision avoidance ability evaluation module, configured to calculate the internal collision avoidance rate of the cluster individuals and the external obstacle avoidance degree, and obtain evaluation indexes by combining the internal collision avoidance rate and the external obstacle avoidance degree, so as to test the collision avoidance control ability of the unmanned cluster system; the internal collision avoidance rate refers to the ability of the cluster individuals to avoid collision with each other in the movement process of the unmanned cluster; the external obstacle avoidance degree refers to the extreme value ratio of the distance between all individuals of the cluster and the obstacle in the process of avoiding the obstacle.

[0030] Based on the same inventive concept, the present application provides a computer system, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is loaded into the processor to realize the steps of the collision avoidance ability test method of the unmanned cluster system.

[0031] Advantages: Based on the distance observation output between the individuals in the formation and the individuals and the obstacles, the present application proposes a new unmanned cluster collision avoidance ability evaluation scale, i.e., the internal collision avoidance rate and the external obstacle avoidance degree. The collision avoidance control ability is evaluated through the distance observation between the individuals in the cluster and the individuals and the obstacles, and then the collision avoidance effect of multiple cluster formation obstacle avoidance control tests is tested based on the given collision avoidance ability evaluation scale. By using the method proposed in the present application, the formation obstacle avoidance control ability can be evaluated quickly and effectively, and the stability of the unmanned cluster formation obstacle avoidance is estimated; the test results show that the present application can effectively evaluate the collision avoidance control ability of the unmanned cluster system. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is the flowchart of the collision avoidance ability test of the unmanned cluster system of the present application.

[0033] Figure 2is a test step schematic diagram of the collision avoidance capability test of the unmanned cluster system of the present application.

[0034] Figure 3 is a three-dimensional motion trajectory diagram under the collision avoidance control of the cluster formation provided by the example of the present application.

[0035] Figure 4 is a position-time change diagram under the collision avoidance control of the cluster formation provided by the example of the present application.

[0036] Figure 5 is a speed-time change diagram under the collision avoidance control of the cluster formation provided by the example of the present application.

[0037] Figure 6 is a minimum distance-time change diagram between the cluster formation and the obstacle provided by the example of the present application.

[0038] Figure 7 is a minimum distance-time change diagram between the intelligent agents in the cluster formation provided by the example of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0040] The present application defines a new unmanned cluster collision avoidance capability evaluation scale, i.e., internal collision avoidance rate and external obstacle avoidance degree. The collision avoidance capability is evaluated through the distance observation between the cluster individuals and the distance between the individuals and the obstacles. The internal collision avoidance rate refers to the ability of the cluster individuals to avoid collision with each other in the process of moving towards a specific target or direction. The external obstacle avoidance degree refers to the extreme value ratio of the distance between all individuals in the cluster and the obstacles, i.e., the ratio of the minimum value and the maximum value, in the process of moving towards a specific target or direction and avoiding obstacles. The present application provides a collision avoidance capability test method for an unmanned cluster. Based on the given collision avoidance capability evaluation scale, the collision avoidance effect of multiple cluster formation obstacle avoidance control tests is tested. The specific process and steps are as follows Figure 1 、 Figure 2As shown, by obtaining the real-time unmanned cluster formation data, the internal collision avoidance rate and the external obstacle avoidance degree of the cluster individuals are calculated, the evaluation index is obtained by combining the internal collision avoidance rate and the external obstacle avoidance degree, so as to test the collision avoidance control ability of the unmanned cluster system. Specifically, in the simulation test system, the unmanned cluster formation test data set is initialized, the real-time cluster individual position, speed and obstacle position information are obtained based on ROS, the obstacle avoidance is realized by using the cluster formation collision avoidance control technology, the distance observation between the formation and the obstacle and the formation inside is output, and the evaluation ability of the unmanned cluster system collision avoidance control ability is determined.

[0041] The following takes the cluster formation obstacle avoidance control ability test of 6 agents as an example to illustrate the detailed implementation process of the embodiment of the application.

[0042] First, the collision avoidance capability evaluation scale is defined.

[0043] The internal collision avoidance rate between the 6 agents is represented as:

[0044]

[0045] As can be seen from the above formula, the internal collision avoidance rate has only two states, and the internal collision avoidance rate is 100% if no collision occurs in the formation during the entire obstacle avoidance process; otherwise, it is 0.

[0046] The external obstacle avoidance degree between the intelligent cluster and the obstacle is represented as:

[0047]

[0048] Wherein, represents the minimum distance between the cluster formation and the obstacle during the entire control time, represents the maximum distance between the cluster formation and the obstacle during the entire control time; respectively represent the minimum value of the distance between the 6 individuals and the obstacle during the entire control time. The greater the external obstacle avoidance rate, the better the obstacle avoidance effect, and W ∈ [0, 1].

[0049] The evaluation index calculation form is as follows:

[0050] p = sign (N) * [ω1*N + ω2*W]

[0051] Wherein, ω1 and ω2 are the weighted weights of the internal obstacle avoidance rate and the external obstacle avoidance degree. Due to the numerical characteristics of N and W, p ∈ [0, 1].

[0052] The evaluation ability level is divided as follows:

[0053] Collision Avoidance Level I (Weak) II (Medium) III (Strong) Threshold Range 0～0.4 0.4～0.7 0.7～1

[0054] According to the evaluation index threshold range given in the above table, the ability level of the measured unmanned cluster system collision avoidance control technology is evaluated.

[0055] Then, based on the given collision avoidance capability evaluation scale, the collision avoidance effect of multiple cluster formation obstacle avoidance control tests is tested; including the following specific steps:

[0056] S1, initialize the unmanned cluster formation test data set;

[0057] Agent initial position Initial speed Static obstacle position And dynamic obstacles And other information, where i=1, 2, …, 6, j=1, 2, q=1, 2;

[0058] S2, get real-time cluster individual position Speed And obstacle position And other information in the simulation test system based on the ROS platform, judge whether it needs to avoid obstacles;

[0059] S3, use the relative obstacle velocity method (RVO) based on the cluster formation collision avoidance control technology to change the formation speed for obstacle avoidance control;

[0060] Relative velocity obstacle is an improved obstacle avoidance idea based on the velocity obstacle method (VO). The specific representation of VO and RVO is as follows:

[0061]

[0062] Where, v A , v B respectively represent the speed of agents A and B, is the set of A's speed when A and B collide, if then A and B will collide at some time in the future. λ(P A , v A -v B )={P A +t(v A -v B )|t≥0} represents a ray with P A as the vertex and v A -v B as the direction, represents the Minkowski sum, -A={-a|a∈A}, a, b represent points on agents A and B respectively;

[0063]

[0064] where v' = v + v A is the non-selected speed for avoiding collision, represents the current speed of A and the average speed of any speed within A, the next time speed as long as it does not belong to can guarantee that no collision occurs.

[0065] S4, output the distance observation of the formation inside and the individual and the obstacle, obtain the distance between each individual inside the formation, and the minimum distance and the maximum distance between the cluster formation and the obstacle, calculate the internal collision avoidance rate and the external obstacle avoidance degree, and determine the evaluation level of the collision avoidance ability of the unmanned cluster system.

[0066] In this embodiment, Python 3.6 is used as the simulation software to test the collision avoidance ability of the unmanned cluster formation obstacle avoidance control test.

[0067] The test data set 1 is used for the internal collision avoidance rate and the external obstacle avoidance degree test of the cluster formation collision avoidance ability. The test data contains 100 groups of related data about the cluster formation collision avoidance control, including 6 initial positions of the intelligent agents randint(0, 6), initial speed uniform(0, 8), obstacle position, etc. Among them, the obstacle contains one static obstacle with a position of [100, 100, 100] and a moving speed of 0; one dynamic obstacle with a moving range of [[395, 400, 400], [405, 400, 400]] and a moving speed of 2 in the x direction. Figure 3 represents the three-dimensional motion trajectory diagram of the cluster formation under data set 1, Figure 4 , Figure 5 is the position and speed change diagram of the cluster formation in each dimension.

[0068] Figure 6 and Figure 7 respectively give the minimum distance between the cluster formation and the obstacle during the obstacle avoidance process and the minimum distance within the cluster formation. The minimum distance within the cluster formation is > 0, indicating that the internal collision avoidance rate of the cluster is N = 1, and the external obstacle avoidance degree output W = 0.5. The weighted weights of the internal obstacle avoidance rate and the external obstacle avoidance degree are set to ω1 = 0.3, ω2 = 0.7, and the evaluation index p = 0.65. Therefore, according to the evaluation ability level division, the collision avoidance ability evaluation of this test is II (medium).

[0069] Based on the same inventive concept, the embodiment of the present application provides a collision avoidance capability test system of an unmanned cluster system, comprising: a data acquisition module, configured to obtain real-time unmanned cluster formation data, including cluster size, individual position, speed and obstacle position; and a collision avoidance capability evaluation module, configured to calculate the internal collision avoidance rate and external obstacle avoidance degree of the cluster individuals, and obtain an evaluation index by combining the internal collision avoidance rate and the external obstacle avoidance degree, so as to test the collision avoidance control capability of the unmanned cluster system. For specific implementation details, refer to the method embodiments described above, which will not be repeated here.

[0070] Based on the same inventive concept, the embodiment of the present application provides a computer system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the steps of the collision avoidance capability test method of the unmanned cluster system.

[0071] It should be noted that the implementation modes not shown or described in the drawings or the specification are known to those skilled in the art, and will not be described in detail.

[0072] The technical means disclosed in the present application scheme is not limited to the technical means disclosed in the above embodiments, but also includes the technical solutions composed of any combination of the above technical features. It should be noted that for those skilled in the art, without departing from the principle of the present application, some improvements and refinements can also be made, which are also considered within the protection scope of the present application.

Claims

1. A method for testing the collision avoidance capability of an unmanned swarm system, characterized in that, Includes the following steps: Real-time data on unmanned swarm formations can be obtained, including swarm size, individual positions, speeds, and obstacle positions. The internal collision avoidance rate and external obstacle avoidance degree of each individual in the swarm are calculated. The evaluation index is obtained by combining the internal collision avoidance rate and the external obstacle avoidance degree, thereby testing the collision avoidance control capability of the unmanned swarm system. The internal collision avoidance rate refers to the ability of individuals within the unmanned swarm to avoid collisions with each other during the movement of the swarm. The external obstacle avoidance degree refers to the extreme ratio of the distances between all individuals in the swarm and the obstacles during the process of the unmanned swarm avoiding obstacles. The method is tested using a simulation testing system, specifically including: Step 1: Initialize the unmanned swarm formation test dataset, including the initial positions of individuals. initial velocity Static obstacle position and dynamic obstacles Information, where i = 1, 2, ..., I, j = 1, 2, ..., J, q = 1, 2, ..., Q; I represents the number of individuals, and J and Q represent the number of static and dynamic obstacles, respectively; Step 2: Obtain the real-time cluster individual positions in the simulation test system using the Robot Operating System (ROS). speed and the location of obstacles Information to determine whether obstacle avoidance is necessary; Step 3: Use relative obstacle velocity (RVO) based cluster formation collision avoidance control technology to change the formation speed for obstacle avoidance control; Step 4: Output the distance observations within the formation and between individual members of the formation and obstacles. Obtain the distances between individuals within the formation and between the swarm formation and obstacles. Calculate the internal collision avoidance rate and external obstacle avoidance degree to determine the evaluation level of the collision avoidance control capability of the unmanned swarm system.

2. The method for testing the collision avoidance capability of an unmanned swarm system according to claim 1, characterized in that, The collision avoidance rate N within an individual in the cluster is expressed as: As shown in the above formula, if no collision occurs within the formation during the entire obstacle avoidance process, the internal collision avoidance rate is 100%; otherwise, it is 0.

3. The method for testing the collision avoidance capability of an unmanned swarm system according to claim 1, characterized in that, The external obstacle avoidance degree W between the cluster and the obstacle is expressed as follows: in, This represents the minimum distance between the entire control time cluster formation and the obstacle. This represents the maximum distance between the entire control time cluster formation and the obstacle; This represents the minimum distance between each individual and the obstacle during the entire control period, where n represents the number of agents.

4. The method for testing the collision avoidance capability of an unmanned swarm system according to claim 1, characterized in that, The evaluation index p is calculated in the following form: p = sign(N) * [ω1 * ​​N + ω2 * W] Wherein, ω1 and ω2 are the weighted weights of the set internal obstacle avoidance rate N and external obstacle avoidance degree W, respectively.

5. The method for testing the collision avoidance capability of an unmanned swarm system according to claim 1, characterized in that, The specific representations of the velocity barrier method (VO) and RVO are as follows: Among them, v A v B These represent the velocities of agents A and B, respectively. It refers to the set of velocities of A when A collides with B, λ(P A ,v A -v B )={P A +t(v A -v B )|t≥0} represents the state with respect to point P, the center of A. A v is the vertex. A -v B A ray with direction . Minkowski and, -A = {-a|a∈A}, where a and b represent points on agents A and B, respectively; Where, v′ A This is a non-selective velocity to avoid collisions. Indicates the current speed of A and The set of average velocities of any given velocity, and the velocity at the next moment. As long as it does not belong to This ensures that no collision will occur.

6. The method for testing the collision avoidance capability of an unmanned swarm system according to claim 1, characterized in that, The collision avoidance capability levels are divided according to the evaluation indicators as follows: Level I has a threshold range of 0 to 0.4, Level II has a threshold range of 0.4 to 0.7, and Level III has a threshold range of 0.7 to 1. The collision avoidance capability level of the unmanned swarm system is determined based on the threshold range of the evaluation indicators.

7. A collision avoidance capability testing system for an unmanned swarm system, characterized in that, include: The data acquisition module is used to obtain real-time data on unmanned swarm formations, including swarm size, individual position, speed, and obstacle position. When conducting tests using a simulation testing system, the process includes: initializing the unmanned swarm formation test dataset, including the initial positions of individuals. initial velocity Static obstacle position and dynamic obstacles Information, where i = 1, 2, ..., I, j = 1, 2, ..., J, q = 1, 2, ..., Q; I represents the number of individuals, and J and Q represent the number of static and dynamic obstacles, respectively; the real-time cluster individual positions in the simulation test system are obtained based on the Robot Operating System (ROS). speed and the location of obstacles Information to determine whether obstacle avoidance is necessary; The collision avoidance capability assessment module is used to calculate the internal collision avoidance rate and external obstacle avoidance degree of each individual in the swarm. Combining these two metrics yields an evaluation index, thereby testing the collision avoidance control capability of the unmanned swarm system. The internal collision avoidance rate refers to the ability of individuals within the swarm to avoid collisions during movement. The external obstacle avoidance degree refers to the extreme ratio of the distances between all individuals in the swarm and obstacles during obstacle avoidance. Testing via a simulation system includes: employing a swarm formation collision avoidance control technique based on Relative Obstacle Velocity (RVO) to change the formation speed for obstacle avoidance control; outputting distance observations within the formation and between individuals in the formation and obstacles to obtain the distances between individuals within the formation and between the swarm formation and obstacles; calculating the internal collision avoidance rate and external obstacle avoidance degree; and determining the evaluation level of the unmanned swarm system's collision avoidance control capability.

8. The collision avoidance capability testing system for an unmanned swarm system according to claim 7, characterized in that, The collision avoidance rate N within an individual in the cluster is expressed as: As can be seen from the above formula, if no collision occurs within the formation during the entire obstacle avoidance process, the internal collision avoidance rate is 100%; otherwise, it is 0. The external obstacle avoidance degree W between the cluster and the obstacle is expressed as follows: in, This represents the minimum distance between the entire control time cluster formation and the obstacle. This represents the maximum distance between the entire control time cluster formation and the obstacle; This represents the minimum distance between each individual and the obstacle during the entire control period, where n represents the number of agents.

9. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the steps of the collision avoidance capability testing method for the unmanned swarm system according to any one of claims 1-6.

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