CN112000070B - Heterogeneous cluster cooperative motion planning method - Google Patents
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Abstract
The invention provides a heterogeneous cluster cooperative motion planning method, which adopts intra-cluster and inter-cluster cooperative motion planning aiming at a task environment from a starting point to a target area of a cluster A and a cluster B with speed difference, so that in the operation process, the cooperation of motion and information communication can be always kept between the two clusters and internal individuals. In the moving process, in order to realize mutual communication and effective information transmission between two clusters, an inter-cluster individual information interaction mechanism is designed, namely, the motion state of an individual is updated by simultaneously utilizing the neighbor position and speed information of the cluster where the individual is located and the position information of the individual of other clusters, and the interconnection and motion coordination inside the clusters and among the clusters of a heterogeneous cluster system are ensured.
Description
Technical Field
The invention relates to the technical field of cluster intelligence, in particular to a collaborative motion planning and interconnection method of a cross-domain heterogeneous cluster.
Background
The cluster movement phenomenon is widely existed in nature, and the flying of bird groups, the traveling of fish groups, the migration of locust groups and the activities of flora and crowd are typical representatives of the cluster movement phenomenon. Despite the relatively limited ability, and even the lack of intelligence, of the individuals that make up these populations, the populations exhibit complex, ordered, and even highly intelligent patterns of collective behavior. This behavior pattern is called ad hoc group emergence behavior, i.e. the cluster motion is an "unconscious" behavior generated by the individuals in the system at the group level according to the respective behavior rules and actions. Based on behavior emerging phenomenon generated by local interaction behavior of individuals, Reynolds, Husi and the like propose a classic 'collision avoidance-formation-aggregation' rule (SAC rule), and according to the rule, cluster motion forms quite similar to a bird swarm and a fish swarm can be simulated.
At present, a self-organization mechanism for generating natural clusters is referred by engineering application and is used for design and construction of various artificial clustering systems, so that the artificial clusters have various qualities such as distribution, redundancy, robustness, flexibility, easy expandability, emerging intelligence and the like. The unmanned cluster system can replace the manned system to be applied to various task scenes, such as forest fire protection, natural resource detection, personnel search and rescue and the like. However, the cluster robots, unmanned aerial vehicle clusters and other systems used at present all adopt similar unmanned devices, and related cluster motion control algorithms mostly study the cooperation among individuals in the same cluster. Such as visker models, kunzan models, social force models, and velocity-averaging models that are based on classical control models modifications, etc.
With the increase of task complexity, under more conditions, unmanned cluster systems with different characteristics are required to act together to ensure the completion of tasks. For example, in the process of search and rescue tasks of marine personnel, an unmanned aerial vehicle cluster formed by a plurality of same unmanned aerial vehicles is matched with an unmanned ship cluster to realize large-range search and accurate rescue; in an air combat scene, an all-around sensing and target striking of a task environment can be realized simultaneously by matching two clusters of a heterogeneous unmanned aerial vehicle cluster consisting of an unmanned aerial vehicle cluster with weak sensing capability and strong striking capability and an unmanned aerial vehicle cluster without striking capability; in the indoor environment map construction task, a small unmanned aerial vehicle cluster with the same characteristics is matched with a monitoring robot cluster consisting of a plurality of ground robots, so that large-range observation and accurate detection of the environment can be realized, the task completion time is greatly shortened, and the modeling precision is improved.
However, since different kinds of unmanned systems have different motion characteristics, different communication ranges and different task levels, how to realize cooperation with an external cluster on the basis of realizing coordinated motion inside the cluster is a problem worthy of intensive research.
This is also interesting in many studies, and Hiroki Sayama studies the motion morphology of multiple clusters with different motion parameters, explores the motion behavior of clusters consisting of individuals of different characteristics, and finds that clustered individuals with the same parameters have clustering characteristics and behavior consistency. However, because the cluster individuals do not distinguish and distinguish the neighbor individuals in the interaction process, the same cluster individuals and different cluster individuals have the same influence on the cluster individuals, and the interaction is more similar to the interaction between single cluster individuals on the interaction mechanism level, and the maximum effect of the isomorphic sub-clusters and the task allocation of different clusters are not easily exerted under the cross-domain situation or when facing more complex tasks. Jorge gomes provides a resource detection system consisting of a single quad-rotor unmanned aerial vehicle and a single unmanned vehicle, and the search efficiency and precision of the unmanned vehicle are improved through large-area observation of the unmanned aerial vehicle. But the number is small, so far not reaching the cluster interaction degree. The eagle eye system provided by Martin Saska and composed of a single quad-rotor unmanned aerial vehicle and a plurality of unmanned vehicles observes the environment by the unmanned aerial vehicle to guide the movement and obstacle avoidance behaviors of the unmanned vehicle cluster. Because four rotor unmanned aerial vehicle do not have speed constraint, consequently at the communication aspect, do not have the loss of connection problem for the time. However, in the face of larger motion scenarios and more complex problems, quad-rotor drones cannot meet the task requirements, and generally require faster, more powerful, and more functional fixed-wing drones to accomplish the corresponding tasks.
Disclosure of Invention
Aiming at solving the problems in the prior art, the invention provides a collaborative motion planning method for heterogeneous clusters, which have different speed constraints and communication constraints, and aims at solving the problem of collaborative motion and communication interconnection of heterogeneous clusters in different motion spaces. The motion planning method does not mean that a desired path is planned for each individual moving, but the motion speed of each individual is planned in real time according to the current position and the environment of each individual.
The invention adopts intra-cluster and inter-cluster cooperative motion planning aiming at the task environment from a starting point to a target area of the cluster A and the cluster B with speed difference, so that in the running process, the cooperation of motion and information communication can be always kept between the two clusters and the internal individual. In the moving process, in order to realize mutual communication and effective information transmission between two clusters, an inter-cluster individual information interaction mechanism is designed, namely, the motion state of an individual is updated by simultaneously utilizing the neighbor position and speed information of the cluster where the individual is located and the position information of the individual of other clusters, and the interconnection and motion coordination inside the clusters and among the clusters of a heterogeneous cluster system are ensured.
The technical scheme of the invention is as follows:
the heterogeneous cluster cooperative motion planning method comprises the following steps:
step 1: and initializing the position coordinates, the speed and the acceleration of each individual in the cluster A and the cluster B, and initializing the position coordinates of a target point of cluster motion.
Step 2: and the cluster individual A calculates the acting force of the individual in the cluster according to the position coordinates and the speed information of the same cluster neighbor sensed in the communication range. And calculating the self-driving force and the target attraction force of the individual according to the coordinate position and the movement speed of the individual and the position coordinate of the target point.
And step 3: and the A cluster individual calculates the inter-cluster acting force of the A cluster individual according to the position information of the B cluster individual sensed in the communication range.
And 4, step 4: and (4) calculating the internal acting force, the inter-group acting force, the self-driving force and the target attraction force of the A-cluster individual according to the step (2-3), and considering the external resistance and the noise of the individual to obtain the total acting force of the A-cluster individual at the moment.
And 5: and B, calculating the internal acting force of the cluster on the individual according to the position coordinate and the speed information of the same cluster neighbor sensed in the communication range. And calculating the self-driving force and the target attraction force of the individual according to the position coordinate and the movement speed of the individual and the position coordinate of the target point.
Step 6: the B cluster calculates the inter-cluster attraction center point x of the A cluster to the B cluster according to the sensed position coordinates of the individual A clusterAcAnd calculating the inter-group acting force of the B cluster individual according to the position coordinate of the central point and updating the inter-group coordination coefficient.
And 7, calculating the internal acting force, the inter-group acting force, the self-driving force and the target attraction force of the B-cluster individual according to the steps 5-6, and considering the external resistance and the noise of the individual to obtain the total acting force of the B-cluster individual at the moment.
And 8, calculating the expected acceleration of the individual according to the total acting force of the individual, and considering the actual maximum acceleration value and the maximum speed which can be reached by the individual. And calculating to obtain the speed of the individual at the next moment, and obtaining the position coordinate of the individual at the next moment according to the speed.
And 9. the cluster A and the cluster B reach the target area or switch the task, otherwise, the step 2 is continued.
Advantageous effects
The invention can realize the cooperative motion planning of the heterogeneous cluster formed by unmanned systems with different motion characteristics, communication capacity and spatial scale, and ensure that the heterogeneous cluster can complete effective communication and cooperative motion in the motion process.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is an overview of a simulation interface, where the upper left square area represents a cluster launch area and the lower right circle represents a target area for heterogeneous cluster motion. The front light gray individuals represent faster clustered individuals and the rear dark gray individuals represent slower clustered individuals.
Fig. 2 to fig. 10 show communication and movement coordination between clusters when a simulation runs to a certain step in the movement process of a target point of a starting area of heterogeneous cluster individuals. Wherein the individuals marked as light gray are the ones of the two clusters that implement inter-cluster communication, and the light gray circles represent communication radii, i.e., inter-cluster communication ranges.
Fig. 11 and 12 show the target area reached by the heterogeneous cluster and the communication state thereof.
Fig. 13 is a diagram of a heterogeneous cluster cooperative motion trajectory.
Fig. 1 step 5, fig. 2 step 45, fig. 3 step 100, fig. 4 step 120, fig. 5 step 130, fig. 6 step 150, fig. 7 step 180, fig. 8 step 200, fig. 9 step 230, fig. 10 step 231, fig. 11 step 250, fig. 12 step 300.
Detailed Description
The following detailed description of embodiments of the invention is intended to be illustrative, and not to be construed as limiting the invention.
The embodiment aims to provide a collaborative motion planning method for a heterogeneous cluster formed by unmanned systems with different motion characteristics, communication capabilities and spatial scales, and to ensure that the heterogeneous cluster can complete effective communication and collaborative motion in a motion process. The heterogeneous cluster system has the following characteristics:
1. the individuals within the cluster have isomorphism, that is, the attributes of the individuals forming the cluster are completely the same, which is shown in three aspects: the inherent external traits of all individuals are identical; all individuals follow the exact same internal cooperative law; individuals are equally located within the group, and there is no relationship between assignment and dominance.
2. Individuals within different clusters are heterogeneous. And the two have larger speed difference and motion characteristics, the cluster individual motion with higher speed is more flexible, and the cluster individual motion has larger steering angle and acceleration. The specific parameter settings are shown in table-1.
TABLE-1 Cluster parameters
3. All clustered individuals have anonymity, i.e., the identity of an individual cannot be identified between individuals based on some explicit extrinsic flag or signaling mechanism.
4. The cluster individual behaviors are completely independent and independent. They actively adjust their own motion state according to the real-time motion state of other individuals, but are not subject to, directed by or controlled by the other individuals.
Considering the heterogeneous cluster system meeting the above conditions, for example, an unmanned plane cluster (B cluster) is matched with an unmanned ship cluster (a cluster), and in a task scene from a starting area to a target area, information complementation between the two clusters is completed, so as to prepare for subsequent task allocation and execution. The method comprises the following specific steps:
step 1: and initializing the position coordinates, the speed and the acceleration of each individual in the cluster A and the cluster B, and initializing the position coordinates of a target point of cluster motion.
Initializing individual location coordinates x in cluster A, Bi,xjWherein i (i ═ 1.., N)A),j(j＝1,...,NB) Are the individuals in cluster A, B, respectively, and NA＝NBN10. In a cartesian coordinate system, the task area is initialized to a rectangular area 1920 × 1080, the departure area is a rectangular area surrounded by coordinates (0,0), (210,0), (210,185) and (0,185), and the target point x isaThe position coordinates are (1536,864. velocity of the initial individual is vi,vjThe velocity is 0.5m/s and 3m/s respectively, and the angle range is
The mass of an individual is m-1.
Step 2: and the cluster individual A calculates the acting force of the individual in the cluster according to the position coordinates and the speed information of the same cluster neighbor sensed in the communication range. And calculating the self-driving force and the target attraction force of the individual according to the coordinate position and the movement speed of the individual and the position coordinate of the target point.
Calculate a cluster individual i (i ═ 1.., N)A) Self-driving force of
Cluster internal forces
Target attraction force
The specific process is as follows:
calculating the self-driving force through the self speed of the individual:
wherein the content of the first and second substances,
for the set a cluster self-acceleration coefficient, t is the time step.
The cluster of co-cluster neighbors that an individual can perceive within a certain communication range is:
Νi＝{k|dik＜rA,sen,k∈{1,...,NA},k≠i} (2)
wherein d isikRepresents the individual spacing, r, within the A clusterA,senThe perceived radius within the a cluster.
The cluster internal acting force refers to the motion cooperative force between an individual and a cluster internal neighbor, and is divided into a position cooperative force and a speed cooperative force:
the position cooperation force calculation method comprises the following steps:
wherein the content of the first and second substances,
respectively set A cluster position coordination coefficient and A cluster speed coordination coefficient. x is the number ofi、xkRespectively representing the position coordinates of the individual and the neighboring individuals of the A cluster, dikDenotes the individual spacing,/c> 0 is the correlation length,/aAnd more than 0 is a gravitational force-repulsive force equilibrium position adjusting parameter. lcThe method is used for adjusting the speed of the gravity attenuation along with the distance: when l isaAt a certain time, with lcIncrease, increase of individual gravitational strengthStrong and the action range is enlarged. Parameter laPosition for adjusting the attraction-repulsion equilibrium (i.e. g (d) ═ 0): when d isikWhen < d, repulsion is generated between individuals; when d isik> d, attractive forces are generated between each other.
The speed cooperation force calculation method comprises the following steps:
the target attraction force calculation method comprises the following steps:
wherein xaIs the target point location.
And step 3: and the A cluster individual calculates the inter-cluster acting force of the A cluster individual according to the position information of the B cluster individual sensed in the communication range.
Calculating the inter-group forces from the individuals of the group B to which the individuals of the group A received according to the following formula
N′i＝{j|dij＜Rsen,j∈{1,...,NB}} (7)
Wherein d isijRepresents the distance between the A cluster individual i and the B cluster communication individual j, RsenIs the inter-group communication radius.
And 4, step 4: calculating the internal acting force, the inter-group acting force, the self-driving force and the target attraction force of the A-cluster individual according to the steps 2-3, considering the external resistance and the noise of the individual and according to the related acting force influence coefficient
The total force that the individual in the A cluster is subjected to at this moment is obtained. The calculation method is as follows:
wherein the content of the first and second substances,
for a set a-cluster target attraction coefficient,
is the inter-cluster cooperation coefficient of the set A cluster.
Representing the frictional force generated by an individual in contact with an environmental medium,
is the damping coefficient. Eta xiiRepresenting random noise with intensity η > 0, let ξiIs in the range of [ -0.5,0.5 [)]2And n is equal to the random vector of uniform distribution, and n represents the noise intensity.
And 5: group B individual j (j ═ 1.., N)B) According to the position coordinates and the speed information of the same group of neighbors sensed in the communication range, the internal acting force of the individual in the cluster is calculated
Calculating the self-driving force of the individual according to the position coordinate, the movement speed and the position coordinate of the target point of the individual
And target attraction
The calculation process is the same as step 2 here.
Step 6: b cluster individual according to sensed A clusterCalculating the inter-cluster attraction center point x of the A cluster to the B clusterAcAnd calculating the inter-group acting force of the B cluster individual according to the position coordinate of the central point and updating the inter-group coordination coefficient.
The practical significance is that historical position information of the communication individuals of the cluster A collected by the cluster B is utilized to apply cluster external acting force to the communication individuals of the cluster B, and when the number of the communication individuals in the cluster A is less than a certain threshold value, the communication individuals of the cluster B can turn to timely and keep communication and information interaction with the cluster A.
The calculation method is as follows:
N′j＝{i|dij＜Rsen,i∈{1,...,NA}}
wherein, N'jRepresenting the individual set of the A cluster for realizing communication with the B cluster, n representing the number of the individual in the A cluster for realizing communication among the clusters, xAcThe position coordinate center point of the A cluster communication individual is represented, cluster external acting force is exerted on the B cluster individual through the point, when the communication number is small, the inter-cluster coordination coefficient is large, and when the communication number is large, the inter-cluster coordination coefficient of the B cluster individual is influenced by the distance between the individual and the point;
the inter-cluster cooperative coefficient is set as a B cluster;
step 7, calculating the cluster internal acting force of the B cluster individual according to the steps 5-6
Acting force between groups
Self-driving force
And target attraction force
And considering the external resistance and noise to which the individual is subjected, according to the relative force influence coefficients
The total force that the individual of the group B is subjected to at this moment is obtained. The calculation method is as follows:
wherein the content of the first and second substances,
for a set B-cluster self-acceleration factor,
for a set B cluster target attraction coefficient,
and B is the inter-group cooperative coefficient of the individual of the group B.
Representing the frictional force generated by an individual in contact with an environmental medium,
is the damping coefficient. Eta xijRepresenting random noise with intensity η > 0, let ξjIs in the range of [ -0.5,0.5 [)]2And n is equal to the random vector of uniform distribution, and n represents the noise intensity.
And 8, calculating the expected acceleration of the individual according to the total acting force of the individual, and considering the actual maximum acceleration value and the maximum speed which can be reached by the individual. And calculating to obtain the speed of the individual at the next moment, and obtaining the position coordinate of the individual at the next moment according to the speed.
The specific method comprises the following steps:
the acceleration and velocity of the individual m in the cluster A, B are corrected taking into account the maximum acceleration maxa and the maximum velocity maxv by the following method:
and updating the individual location:
and 9. the cluster A and the cluster B reach the target area or switch the task, otherwise, the step 2 is continued.
Verifying and analyzing a simulation experiment:
the parameter coefficient settings are shown in table-1. The simulation beat is about 0.03s once, and the total simulation step number is 300.
Simulation analysis
Fig. 1 to 13 are simulation screenshots of the cooperative motion process of the heterogeneous cluster from the starting point to the target point. FIG. 1 shows a heterogeneous cluster simulation environment, and cluster individual initial motion states. Fig. 2-10 show the movement state and communication condition of the heterogeneous cluster individuals in the movement process from the starting point to the target point. Fig. 11 and 12 show the motion state and communication situation of the heterogeneous cluster individuals when reaching the target point. FIG. 13 shows the individual operation traces of the heterogeneous clusters. Through dynamic observation of the movement state of the heterogeneous clusters, in the movement process of the heterogeneous clusters from a starting area to a target point, although the inherent movement speed of the B cluster is always greater than that of the A cluster, due to the existence of the attractive force among the clusters, a position constraint force is generated on the B cluster with higher speed, and the B cluster can stably turn around to continuously communicate with the A cluster when the B cluster is disconnected from the A cluster, so that interconnection among the clusters is realized, and preparation is made for task switching of the next step. The heterogeneous cluster cooperative motion planning method designed by the invention can realize motion cooperation and communication among clusters in the whole motion process for two heterogeneous clusters with different motion speeds.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (6)
1. A heterogeneous cluster cooperative motion planning method is characterized by comprising the following steps:
step 1: initializing position coordinates, speeds and accelerations of all individuals in the cluster A and the cluster B, and initializing position coordinates of a target point of cluster motion;
step 2: a, calculating the internal acting force of an individual in a cluster according to the position coordinates and the speed information of the neighbor in the same cluster sensed in a communication range by the individual in the cluster; calculating the self-driving force and the target attraction force of the individual according to the coordinate position and the movement speed of the individual and the position coordinate of the target point;
and step 3: the cluster A individuals calculate the inter-cluster acting force of the cluster A individuals according to the position information of the cluster B individuals sensed in the communication range;
and 4, step 4: calculating the internal acting force, the inter-group acting force, the self-driving force and the target attraction force of the A-cluster individual according to the step 2-3, and considering the external resistance and the noise of the individual to obtain the total acting force of the A-cluster individual at the moment;
and 5: b, the cluster individual calculates the internal acting force of the cluster on the individual according to the position coordinate and the speed information of the same cluster neighbor sensed in the communication range; calculating the self-driving force and the target attraction force of the individual according to the position coordinate and the movement speed of the individual and the position coordinate of the target point;
step 6: the B cluster calculates the inter-cluster attraction center point x of the A cluster to the B cluster according to the sensed position coordinates of the individual A clusterAcCalculating the inter-group acting force of the B cluster individual according to the position coordinate of the central point and updating the inter-group coordination coefficient;
and 7: calculating the internal acting force, the inter-group acting force, the self-driving force and the target attraction force of the B cluster individual according to the steps 5-6, and considering the external resistance and the noise of the individual to obtain the total acting force of the B cluster individual at the moment;
and 8: calculating the expected acceleration of the individual according to the total acting force on the individual, calculating the speed of the individual at the next moment by considering the actual maximum acceleration value and the maximum speed which can be reached by the individual, and obtaining the position coordinate of the individual at the next moment according to the speed;
and step 9: and (3) the cluster A and the cluster B reach a target area or switch tasks, otherwise, the step (2) is continued.
2. The method for planning coordinated movement of heterogeneous clusters according to claim 1, wherein the a cluster individuals i (i ═ 1.., N) are calculated in step 2A) Self-driving force of
Cluster internal forces
Target attraction force
The specific process is as follows:
calculating the self-driving force through the self speed of the individual:
wherein the content of the first and second substances,
setting the cluster A self-acceleration coefficient, wherein t is a time step;
the cluster of co-cluster neighbors that an individual can perceive within a certain communication range is:
Νi＝{k|dik＜rA,sen,k∈{1,...,NA},k≠i}
wherein d isikRepresents the individual spacing, r, within the A clusterA,senIs the sensed radius within cluster a;
the cluster internal acting force refers to the motion cooperative force between an individual and a cluster internal neighbor, and is divided into a position cooperative force and a speed cooperative force:
the position cooperation force calculation method comprises the following steps:
the speed cooperation force calculation method comprises the following steps:
the target attraction force calculation method comprises the following steps:
wherein xaIs the target point location.
3. The method of claim 2, wherein the inter-group forces of the individuals of cluster A from the individuals of cluster B are calculated in step 3 according to the following formula
N′i＝{j|dij＜Rsen,j∈{1,...,NB}}
Wherein d isijRepresents the distance between the A cluster individual i and the B cluster communication individual j, RsenIs the inter-group communication radius.
4. The method for planning cooperative motion of heterogeneous cluster according to claim 3, wherein the step 4 is performed according to a formula
Calculating the total force to which the individuals of the A cluster are subjected, wherein
For a set a-cluster target attraction coefficient,
the inter-cluster cooperative coefficient is set as the A cluster;
representing the frictional force generated by an individual in contact with an environmental medium,
is a damping coefficient; eta xiiRepresenting random noise with intensity η > 0.
5. The method for planning cooperative motion of heterogeneous cluster according to claim 4, wherein the calculation formula in step 6 is
N′j＝{i|dij＜Rsen,i∈{1,...,NA}}
Wherein N'jRepresenting the individual set of the A cluster for realizing communication with the B cluster, n representing the number of the individual in the A cluster for realizing communication among the clusters, xAcThe center point of the position coordinates of the A cluster communication individuals is represented,
is the inter-cluster cooperation coefficient of the set B cluster.
6. The method for planning cooperative motion of heterogeneous cluster according to claim 5, wherein the calculation formula in step 7 is
Wherein
For a set B-cluster self-acceleration factor,
for a set B cluster target attraction coefficient,
and B is the inter-group cooperative coefficient of the individual of the group B.
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| US8411567B2 (en) * | 2010-06-30 | 2013-04-02 | Intel Corporation | Swarm intelligence based methods to enable cooperative communication in a mesh network |
| CN103425840A (en) * | 2013-08-14 | 2013-12-04 | 西北工业大学 | Cooperative air combat firepower distribution method based on improved multi-target leapfrog algorithm |
| KR102299245B1 (en) * | 2013-08-16 | 2021-09-08 | 인튜어티브 서지컬 오퍼레이션즈 인코포레이티드 | System and method for coordinated motion among heterogeneous devices |
| CN109117571B (en) * | 2018-08-27 | 2020-11-03 | 安徽农业大学 | Automatic control method for interconnected cooperative motion of unmanned aerial vehicle cluster |
| CN110244757A (en) * | 2019-05-16 | 2019-09-17 | 湖州师范学院 | A kind of motion control method being easy to group's evolution |
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