CN116578092A - Gene regulation network-based intelligent agent cluster motion control method and system - Google Patents
Gene regulation network-based intelligent agent cluster motion control method and system Download PDFInfo
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Abstract
The application discloses an agent cluster motion control method, system, equipment and medium based on a gene regulation network, wherein the gene regulation network comprises an upper network and a lower network, and the method comprises the following steps: acquiring the position information of an intelligent body and the position information of all barriers falling in a given detection range of the intelligent body in real time; processing the position information of the intelligent body and all the obstacles through an upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and mapping the three-dimensional concentration map onto a two-dimensional plane; processing related information on the two-dimensional plane through a lower network to obtain obstacle avoidance speed, aggregation speed and homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment; and controlling the movement of the intelligent body according to the movement speed. According to the application, the obstacle avoidance behavior, the aggregation behavior and the homodromous motion behavior of the intelligent agent are considered when the current motion state of the intelligent agent is adjusted, so that the motion reliability of the intelligent agent cluster can be improved.
Description
Technical Field
The application relates to the technical field of intelligent control, in particular to an intelligent agent cluster motion control method, system, equipment and medium based on a gene regulation network.
Background
In recent years, the intelligent agents have wide application in aspects of power inspection, emergency rescue, city planning and the like, and single intelligent agents have the defects of poor fault tolerance, limited effective load, poor complex task processing and the like, so that the research on the cooperative motion control technology of intelligent agent clusters is particularly important for coping with increasingly complex tasks and environments. When the intelligent agent clusters move in any area, three basic action criteria, namely collision avoidance action, aggregation action and equidirectional movement action, are usually considered, but in the prior art, only one basic action criterion is often considered when the current movement state of a single intelligent agent is adjusted in real time, so that the movement effect of the intelligent agent clusters is poor.
Disclosure of Invention
The application provides an intelligent agent cluster motion control method, system, equipment and medium based on a gene regulation network, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In a first aspect, there is provided a method for controlling movement of an agent cluster based on a gene regulation network comprising an upper network and a lower network, the method comprising:
for each intelligent agent in an intelligent agent cluster, acquiring the position information of the intelligent agent and the position information of all barriers falling within a given detection range of the intelligent agent in real time;
processing the position information of the intelligent body and the position information of all the obstacles through the upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and mapping the three-dimensional concentration map onto a two-dimensional plane;
processing related information on the two-dimensional plane through the lower network to obtain obstacle avoidance speed, aggregation speed and homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment;
and controlling the movement of the intelligent body according to the movement speed.
Further, the relevant information on the two-dimensional plane includes an influence area where the agent is located, position information of an outer boundary line of the influence area on the two-dimensional plane, position information of the agent on the two-dimensional plane, and position information of all obstacles on the two-dimensional plane.
Further, the processing the related information on the two-dimensional plane through the lower network to obtain the obstacle avoidance speed, the aggregation speed and the homodromous speed of the intelligent body at the next time includes:
determining the obstacle avoidance speed of the intelligent body at the next moment according to the relative position relation between the intelligent body and the outer boundary line of the influence area;
determining the aggregation speed of the intelligent agent at the next moment according to the aggregation conditions between the intelligent agent and all the obstacles;
and a virtual guide is arranged in the intelligent agent cluster, and the same-direction speed of the intelligent agent at the next moment is determined according to the detection condition of the intelligent agent on the virtual guide.
Further, determining the obstacle avoidance speed of the intelligent body at the next moment according to the relative position relationship between the intelligent body and the outer boundary line of the influence area comprises:
according to a set sampling step length, a plurality of sampling points are obtained from an outer boundary line of the influence area;
and acquiring the position information of the plurality of sampling points on the two-dimensional plane, and determining the obstacle avoidance speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the given minimum collision avoidance distance of the intelligent body.
Further, the determining the aggregation speed of the agent at the next moment according to the aggregation situation between the agent and all the obstacles includes:
setting an allowable aggregation range of the intelligent agent according to a given detection range and a given maximum safety distance of the intelligent agent;
and extracting all barriers falling in the allowable aggregation range from all the barriers, acquiring the position information of all the barriers on the two-dimensional plane, and determining the aggregation speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the given maximum safe distance.
Further, the determining the homodromous speed of the agent at the next moment according to the detection condition of the agent on the virtual guide comprises:
when all the obstacles contain the virtual guide, acquiring the position information of the virtual guide on the two-dimensional plane, and determining the same-direction speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the current movement speed of the intelligent body.
Further, the determining the homodromous speed of the agent at the next moment according to the detection condition of the agent on the virtual guide further includes:
extracting a single obstacle nearest to the agent from all the obstacles when the virtual guide is not included in all the obstacles;
and acquiring the position information of the single obstacle on the two-dimensional plane, and determining the homodromous speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the current movement speed of the intelligent body.
In a second aspect, there is provided an agent cluster motion control system based on a gene regulation network comprising an upper network and a lower network, the system comprising:
the acquisition module is used for acquiring the position information of each intelligent agent and the position information of all the obstacles falling within the given detection range of the intelligent agent in real time for each intelligent agent in the intelligent agent cluster;
the first processing module is used for processing the position information of the intelligent body and the position information of all the obstacles through the upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and then mapping the three-dimensional concentration map onto a two-dimensional plane;
the second processing module is used for processing the related information on the two-dimensional plane through the lower network to obtain the obstacle avoidance speed, the aggregation speed and the homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment;
and the control module is used for controlling the intelligent body to move according to the movement speed.
In a third aspect, a computer device is provided, comprising a memory storing a computer program and a processor executing the computer program to implement the gene regulation network-based agent cluster motion control method according to the first aspect.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored, which when being executed by a processor implements the method for controlling the movement of an agent cluster based on a gene regulation network according to the first aspect.
The application has at least the following beneficial effects: the upper network of the gene regulation network is utilized to analyze the local information of a single intelligent agent, so that a two-dimensional plane representing the current motion information of the intelligent agent and all barriers around the intelligent agent can be obtained, and the requirement on computing resources is low; and analyzing the related motion information represented on the two-dimensional plane by utilizing a lower network of the gene regulation network, and comprehensively considering obstacle avoidance behaviors, aggregation behaviors and homodromous motion behaviors among the intelligent agents in the analysis process to obtain the motion speed of the intelligent agents at the next moment, thereby improving the motion reliability of the intelligent agent clusters.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
FIG. 1 is a schematic flow chart of an agent cluster motion control method based on a gene regulation network in an embodiment of the application;
FIG. 2 is a schematic illustration of a three-dimensional concentration map in an embodiment of the application;
FIG. 3 is a two-dimensional plan view of a three-dimensional concentration map mapped according to an embodiment of the present application;
FIG. 4 is a schematic diagram of the composition of an agent cluster motion control system based on a gene regulation network in an embodiment of the application;
fig. 5 is a schematic diagram of a hardware structure of a computer device in an embodiment of the disclosure.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that although functional block diagrams are depicted as block diagrams, and logical sequences are shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the block diagrams in the system. The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, and it should be understood that the data so used may be interchanged, as appropriate, in order that the embodiments of the application described herein may be practiced in other than those illustrated or described. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a flow chart of an agent cluster motion control method based on a gene regulation network, where the method can be applied to a scenario that the agent cluster performs a detection task in any area, and the gene regulation network is composed of an upper network and a lower network, and the method includes the following steps:
step S110, for each agent in the agent cluster, acquiring the position information of the agent and the position information of all obstacles falling within a given detection range of the agent in real time;
step S120, processing the position information of the intelligent body and the position information of all the obstacles through the upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and mapping the three-dimensional concentration map onto a two-dimensional plane;
step S130, processing related information on the two-dimensional plane through the lower network to obtain the obstacle avoidance speed, the aggregation speed and the homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment;
and step 140, controlling the movement of the intelligent body according to the movement speed.
In the embodiment of the present application, the specific implementation process of the step S110 includes the following steps:
step S111, acquiring position information of the intelligent body at the current moment through a position sensor carried by the intelligent body;
step S112, respectively measuring the relative position information of all barriers around the intelligent body at the current moment by a distance sensor carried by the intelligent body according to the limited allowable detection range, wherein the relative position information of any one barrier at the current moment comprises the distance and the included angle between the intelligent body and the barrier;
step S113, combining the position information of the intelligent body at the current time and the relative position information of all the obstacles at the current time, and determining the position information of all the obstacles at the current time.
It should be noted that, all the obstacles mentioned in the step S112 include other agents except the agent in the agent cluster and other blocking obstacles not belonging to the agent in the area, where the distance between each obstacle and the agent is within the allowable detection range.
In the embodiment of the application, the specific position information of other surrounding obstacles can be obtained only by relying on the position sensor and the distance sensor carried by the intelligent agent, global information is not needed, the network communication environment where the intelligent agent is located is not required, and the intelligent agent can still stably operate under the condition of communication rejection.
In the embodiment of the present application, the specific implementation process of the step S120 includes the following steps:
step S121, generating an individual concentration field corresponding to the agent by using the position information of the agent at the current time, where the generation process depends on the formula:
wherein T is the concentration generated by the intelligent agent, T is time, dT/dT is the concentration change rate generated by the intelligent agent at the current time, gamma is the position information of the intelligent agent at the current time,for Laplace operator>Defined as the second derivative of T, x and y are two components in two dimensions, +.>The effect of (2) is to simulate the diffusion process of concentration in space.
Step S122, generating a comprehensive concentration field corresponding to all the obstacles by using the position information of all the obstacles at the current time, which is specifically expressed as follows:
firstly, generating an individual concentration field corresponding to each obstacle by using the position information of each obstacle at the current time, wherein the generation process depends on the formula:
secondly, all individual concentration fields corresponding to all the obstacles are overlapped to obtain a comprehensive concentration field corresponding to all the obstacles, and the formula relied on by the overlapping process is as follows:
wherein P is i The concentration produced for the ith obstacle of the total obstacles, t is time, dP i Dt represents the concentration change rate, gamma, generated by the ith obstacle in all the obstacles at the current time i For the position information of the ith obstacle in all the obstacles at the current time,for Laplace operator>Defined as P i Is the second derivative of (x) and y is two components in two dimensions,/i>The function of (1) is to simulate the diffusion process of the concentration in space, P is the comprehensive concentration field corresponding to all the obstacles, N t Is the total number of all obstacles.
Step S123, performing a fusion operation on the individual concentration fields corresponding to the intelligent agent and the comprehensive concentration fields corresponding to all the obstacles to obtain a three-dimensional concentration map formed by all the obstacles and the intelligent agent, where the fusion process depends on the formula:
wherein G is a gene set in the gene regulation network, and is responsible for processing the comprehensive concentration field P corresponding to all the obstacles to obtain a comprehensive concentration map, t is time, sig refers to an S-shaped function, also called an S-shaped growth curve, used for mapping variables into a (0, 1) interval, and θ 1 And (3) setting regulation parameters in advance for technicians, wherein k is the regulation parameters set in advance for the technicians, M is another gene set in the gene regulation network, and is responsible for processing an individual concentration field T corresponding to the intelligent agent and a comprehensive concentration map associated with the comprehensive concentration field P so as to obtain the three-dimensional concentration map formed by all the obstacles and the intelligent agent together.
And S124, constructing and running the gene regulation network through a Matlab software platform, and mapping the three-dimensional concentration map onto a two-dimensional plane by using a contour function in the Matlab software platform.
In the embodiment of the present application, after the mapping operation in the step S124 is performed, a plurality of closed contour lines may be displayed on the two-dimensional plane, where the plurality of closed contour lines may be actually divided into two groups of closed contour lines, the first group of closed contour lines uses the agent as a surrounding center, and the second group of closed contour lines uses all the obstacles as a surrounding center, where relevant information needs to be extracted from the two-dimensional plane, which is specifically shown as follows:
firstly, acquiring a maximum closed contour line falling on the outermost periphery from the first group of closed contour lines, wherein the value of the maximum closed contour line in the first group of closed contour lines is minimum, and defining a closed area formed by the maximum closed contour line as an influence area of the intelligent body at the current moment;
and secondly, acquiring the position information of the outer boundary line (namely the maximum closed contour line) of the influence area on the two-dimensional plane, the position information of all the obstacles on the two-dimensional plane and the position information of the intelligent body on the two-dimensional plane according to a two-dimensional space coordinate system formed on the two-dimensional plane.
In addition, the maximum closed contour line falling on the outermost periphery is obtained from the second set of closed contour lines and is recorded as a first closed contour line to make a distinguishing description, the value of the first closed contour line in the second set of closed contour lines is the smallest, and then the closed area formed by the first closed contour line is defined as a common movement area where all the obstacles are located at the current moment, and the influence area is possibly deformed due to the change of the common movement area.
In the embodiment of the application, the implementation effect of the step S120 is visually described by taking the local area dynamically built by four intelligent agents as an example; in this application example, taking the body B, the body C, and the body D as all the obstacles around the body a, a three-dimensional density map as shown in fig. 2 is obtained after the processing from the above step S121 to the above step S123, in which the individual density fields corresponding to the body a are independent and concave downward, the individual density field corresponding to the body B is convex upward, the individual density field corresponding to the body C is convex upward, the individual density field corresponding to the body D is convex upward, and the integrated density fields formed by the body B, the body C, and the body D are connected; after the mapping operation in the step S124 and the relevant contour extraction and preservation on the two-dimensional plane, a two-dimensional plane schematic diagram as shown in fig. 3 may be obtained, where only the affected area where the agent a is located at the current time and the common movement area (not labeled in fig. 3) where the agent B, the agent C, and the agent D are located at the current time are displayed on the two-dimensional plane, it is seen that the common movement area causes a certain deformation in the affected area, and the corresponding deformation direction is the direction indicated by the arrow labeled in fig. 3.
In the embodiment of the present application, the specific implementation process of the step S130 includes the following steps:
step S131, based on the position relation of the intelligent body relative to the outer boundary line of the influence area, acquiring the obstacle avoidance speed of the intelligent body at the next moment and recording the obstacle avoidance speed as
Step S132, based on the aggregation situation of all the obstacles towards the intelligent agent, acquiring the aggregation speed of the intelligent agent at the next moment and recording as
Step S133, selecting an agent from the agent cluster as a virtual director, wherein the virtual director is positioned in front of the agent cluster, so that the agent cluster can move in the same direction towards the virtual director, and acquiring the same-direction speed of the agent at the next moment based on the detection condition of the agent on the virtual director and recording the same-direction speed as the following moment
Step S134, combining the aggregation speedThe obstacle avoidance speed->And said homodromous speed->Calculating the motion speed of the intelligent body at the next moment, wherein the corresponding calculation formula is as follows:
more specifically, the implementation process of the step S131 includes the following steps:
step S131.1, uniformly sampling on an outer boundary line (namely the maximum closed contour line) of the influence area according to a sampling step length preset by a technician to obtain a plurality of sampling points; wherein, the application preferably sets the sampling step length to be 0.1 meter, but is not limited to the method;
step S131.2, acquiring the position information of the plurality of sampling points on the two-dimensional plane in a matching searching mode according to the position information of the outer boundary line (namely the maximum closed contour line) of the influence area on the two-dimensional plane;
step S131.3, calculating the obstacle avoidance speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane, the position information of the plurality of sampling points on the two-dimensional plane and the minimum obstacle avoidance distance of the intelligent body set by a technician in advance, wherein the corresponding calculation formula is as follows:
wherein N is j D is the total number of a plurality of sampling points min For the minimum collision avoidance distance of the agent, sig refers to an S-shaped function, also known as an S-shaped growth curve, for mapping variables into (0, 1) intervals, k 1 The technical personnel are provided with good regulation parameters in advance,for the distance between the agent and the jth sampling point of the number of sampling points, (G) x ,G y ) For the location information of the agent on the two-dimensional plane, (G) jx ,G jy ) For the position information of the j-th sampling point in the plurality of sampling points on the two-dimensional plane, theta j For the deflection angle of the j-th sampling point in the plurality of sampling points relative to the intelligent agent,/for the intelligent agent>For the direction of obstacle avoidance speed of the agent at the next moment, +.>For the direction of the obstacle avoidance speed +.>Is included in the direction vector.
More specifically, the implementation process of the step S132 includes the following steps:
step S132.1, setting the allowable detection range of the intelligent agent to be [0, d ] max ]，d max For the maximum detection distance of the intelligent agent, setting the maximum safety distance of the intelligent agent as d safe Thereby determining an allowable aggregation range based on the agent as a center as [ d ] safe ,d max ]The method comprises the steps of carrying out a first treatment on the surface of the The maximum detection distance d max Said maximum safe distance d safe And the minimum collision prevention distance d min The following relationship should be satisfied: 0<d min <d safe <d max ；
Step S132.2, based on the position information of all the obstacles at the current time and the position information of the intelligent agent at the current time, which are obtained in the step S110, all the obstacles in the allowable aggregation range are screened out from all the obstacles;
step S132.3, according to the position information of all the obstacles on the two-dimensional plane, acquiring the position information of all the obstacles on the two-dimensional plane in a matching searching mode;
step S132.4, combining the position information of the intelligent body on the two-dimensional plane, the position information of all the barriers on the two-dimensional plane and the maximum safe distance d safe Calculating the aggregation speed of the agent at the next moment, wherein the corresponding calculation formula is as follows:
wherein N is k For the total number of said total obstacles,for the distance between the agent and the kth obstacle of the total obstacles, sig refers to an S-shaped function, also called S-shaped growth curve, for mapping variables into (0, 1) intervals, k 2 Setting the regulation parameters (G) for technicians in advance x ,G y ) For the location information of the agent on the two-dimensional plane, (G) k,x ,G k,y ) For the position information of the kth obstacle in the whole obstacles on the two-dimensional plane, theta k For the deflection angle of the kth obstacle of said total obstacles with respect to said agent,/for all obstacles>For the direction of obstacle avoidance speed of the agent at the next moment, +.>For the direction of the obstacle avoidance speed +.>Is included in the direction vector.
It should be noted that, for the sig functions mentioned in the above step S123, the above step S131.3 and the above step S132.4, the corresponding expressions are: sig (a, b, c) =1/[ 1+e ] -c(a-b) ]A, b and c are all parameters required for the sig function.
More specifically, the implementation process of the step S133 includes the following steps:
step S133.1, judging whether the virtual guide is in a given detection range of the intelligent agent at the current moment, namely judging whether all the obstacles contain the virtual guide; if yes, go on to step S133.2 and step S133.3; if not, jumping to execute the steps S133.4 to S133.6;
s133.2, acquiring the position information of the virtual guide on the two-dimensional plane in a matching searching mode according to the position information of all the obstacles on the two-dimensional plane;
step S133.3, combining the position information of the intelligent body on the two-dimensional plane, the position information of the virtual guide on the two-dimensional plane and the movement speed of the intelligent body at the current moment, and calculating the same-direction speed of the intelligent body at the next moment, wherein the corresponding calculation formula is as follows:
wherein s is the movement speed of the intelligent body at the current moment, (G) x ,G y ) For the location information of the agent on the two-dimensional plane, (G) L,x ,G L,y ) For the position information of the virtual guide on the two-dimensional plane, theta L For the deflection angle of the virtual leader relative to the agent,for the direction of the same directional velocity of the agent at the next moment,/for the next moment>For the direction of the same directional velocity +.>Is included in the direction vector.
Step S133.4, screening single obstacles closest to the intelligent agent from all the obstacles based on the position information of all the obstacles at the current time and the position information of the intelligent agent at the current time acquired in the step S110;
s133.5, acquiring the position information of the single obstacle on the two-dimensional plane in a matching searching mode according to the position information of all the obstacles on the two-dimensional plane;
step S133.6, combining the position information of the intelligent body on the two-dimensional plane, the position information of the single obstacle on the two-dimensional plane and the movement speed of the intelligent body at the current moment, and calculating the homodromous speed of the intelligent body at the next moment, wherein the corresponding calculation formula is as follows:
wherein s is the movement speed of the intelligent body at the current moment, (G) x ,G y ) For the location information of the agent on the two-dimensional plane, (G) R,x ,G R,y ) For the position information of the single obstacle on the two-dimensional plane, theta R For the deflection angle of the individual obstacle relative to the agent,for the direction of the same directional velocity of the agent at the next moment,/for the next moment>For the direction of the same directional velocity +.>Is included in the direction vector.
It should be noted that the intelligent agent in the embodiment of the present application may be, but is not limited to, an unmanned plane or other intelligent robots.
In the embodiment of the application, the upper network of the gene regulation network is utilized to analyze the local information of a single intelligent agent, so that a two-dimensional plane representing the current motion information of the intelligent agent and all the obstacles around the intelligent agent can be obtained, and the requirement on computational resources is low; and analyzing the related motion information represented on the two-dimensional plane by utilizing a lower network of the gene regulation network, and comprehensively considering obstacle avoidance behaviors, aggregation behaviors and homodromous motion behaviors among the intelligent agents in the analysis process to obtain the motion speed of the intelligent agents at the next moment, thereby improving the motion reliability of the intelligent agent clusters.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating a system for controlling movement of an agent cluster based on a gene regulation network according to an embodiment of the present application, where the system may be applied to a scenario in which the agent cluster performs a detection task in any area, where the gene regulation network is composed of an upper network and a lower network; the system includes an acquisition module 210, a first processing module 220, a second processing module 230, and a control module 240, which are connected in sequence.
In the implementation process of the present application, for each agent contained in the agent cluster, the acquiring module 210 acquires, in real time, the position information of the agent at the current time, and acquires, in real time, the position information of all the obstacles in the given detection range of the agent at the current time; enabling the upper network in the gene regulation network through the first processing module 220, comprehensively analyzing the position information of the intelligent body at the current time and the position information of all the obstacles at the current time to obtain a three-dimensional concentration map formed by all the obstacles and the intelligent body together, and then mapping the three-dimensional concentration map onto a two-dimensional plane; the lower network in the gene regulation network is started through the second processing module 230, the relevant information represented on the two-dimensional plane is comprehensively analyzed, the homodromous speed, the aggregation speed and the obstacle avoidance speed of the intelligent body at the next moment are obtained, and the movement speed of the intelligent body at the next moment is obtained by combining the homodromous speed, the aggregation speed and the obstacle avoidance speed; the current movement state of the agent is adjusted by the control module 240 according to the movement speed.
The content in the above method embodiment is applicable to the system embodiment, and functions implemented by the system embodiment are the same as those of the method embodiment, and beneficial effects achieved by the system embodiment are the same as those of the method embodiment, and are not repeated herein.
In addition, the embodiment of the application also provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the method for controlling the movement of the intelligent agent cluster based on the gene regulation network in the embodiment is realized. The computer readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS Memory, random access memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable ProgrammableRead-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer, a cell phone, etc.), which can be a read-only memory, a magnetic or optical disk, etc.
In addition, fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application, where the computer device includes a processor 320, a memory 330, an input unit 340, and a display unit 350. Those skilled in the art will appreciate that the device architecture shown in fig. 5 does not constitute a limitation of all devices, and may include more or fewer components than shown, or may combine certain components. The memory 330 may be used to store the computer program 310 and the functional modules, and the processor 320 runs the computer program 310 stored in the memory 330 to perform various functional applications and data processing of the device. The memory may be or include an internal memory or an external memory. The internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, floppy disk, ZIP disk, U-disk, tape, etc. The memory 330 disclosed in embodiments of the present application includes, but is not limited to, those types of memory described above. The memory 330 disclosed in the embodiments of the present application is by way of example only and not by way of limitation.
The input unit 340 is used for receiving input of a signal and receiving keywords input by a user. The input unit 340 may include a touch panel and other input devices. The touch panel can collect touch operations on or near the touch panel by a user (such as operations of the user on or near the touch panel by using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a preset program; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., play control keys, switch keys, etc.), a trackball, mouse, joystick, etc. The display unit 350 may be used to display information input by a user or information provided to the user and various menus of the terminal device. The display unit 350 may take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 320 is a control center of the terminal device, connects various parts of the entire device using various interfaces and lines, performs various functions and processes data by running or executing software programs and/or modules stored in the memory 320, and invoking data stored in the memory.
As an embodiment, the computer device comprises a processor 320, a memory 330 and a computer program 310, wherein the computer program 310 is stored in the memory 330 and configured to be executed by the processor 320, the computer program 310 being configured to perform the method of controlling the movement of the intelligent agent clusters based on the genetic control network in the above embodiment.
While the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiments or any particular embodiment, but is to be considered as providing a broad interpretation of such claims by reference to the appended claims in light of the prior art and thus effectively covering the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (10)
1. An agent cluster motion control method based on a gene regulation network, wherein the gene regulation network comprises an upper network and a lower network, the method comprising:
for each intelligent agent in an intelligent agent cluster, acquiring the position information of the intelligent agent and the position information of all barriers falling within a given detection range of the intelligent agent in real time;
processing the position information of the intelligent body and the position information of all the obstacles through the upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and mapping the three-dimensional concentration map onto a two-dimensional plane;
processing related information on the two-dimensional plane through the lower network to obtain obstacle avoidance speed, aggregation speed and homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment;
and controlling the movement of the intelligent body according to the movement speed.
2. The gene regulation network-based agent cluster motion control method according to claim 1, wherein the related information on the two-dimensional plane includes an influence region in which the agent is located, position information of an outer boundary line of the influence region on the two-dimensional plane, position information of the agent on the two-dimensional plane, and position information of all obstacles on the two-dimensional plane.
3. The method for controlling the cluster motion of the intelligent agent based on the gene regulation network according to claim 2, wherein the processing the related information on the two-dimensional plane through the lower network to obtain the obstacle avoidance speed, the aggregation speed and the homodromous speed of the intelligent agent at the next time comprises the following steps:
determining the obstacle avoidance speed of the intelligent body at the next moment according to the relative position relation between the intelligent body and the outer boundary line of the influence area;
determining the aggregation speed of the intelligent agent at the next moment according to the aggregation conditions between the intelligent agent and all the obstacles;
and a virtual guide is arranged in the intelligent agent cluster, and the same-direction speed of the intelligent agent at the next moment is determined according to the detection condition of the intelligent agent on the virtual guide.
4. The method of claim 3, wherein determining the obstacle avoidance speed of the agent at the next time according to the relative positional relationship between the agent and the outer boundary line of the affected area comprises:
according to a set sampling step length, a plurality of sampling points are obtained from an outer boundary line of the influence area;
and acquiring the position information of the plurality of sampling points on the two-dimensional plane, and determining the obstacle avoidance speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the given minimum collision avoidance distance of the intelligent body.
5. The method of claim 3, wherein determining the aggregation speed of the agent at the next time according to the aggregation situation between the agent and all the obstacles comprises:
setting an allowable aggregation range of the intelligent agent according to a given detection range and a given maximum safety distance of the intelligent agent;
and extracting all barriers falling in the allowable aggregation range from all the barriers, acquiring the position information of all the barriers on the two-dimensional plane, and determining the aggregation speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the given maximum safe distance.
6. The method for controlling cluster motion of agents based on gene regulation network according to claim 3, wherein determining the homodromous speed of the agents at the next moment according to the detection condition of the agents on the virtual leader comprises:
when all the obstacles contain the virtual guide, acquiring the position information of the virtual guide on the two-dimensional plane, and determining the same-direction speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the current movement speed of the intelligent body.
7. The method for controlling cluster motion of agents based on a gene regulation network according to claim 3, wherein determining the homodromous speed of the agents at the next moment according to the detection situation of the agents on the virtual leader further comprises:
extracting a single obstacle nearest to the agent from all the obstacles when the virtual guide is not included in all the obstacles;
and acquiring the position information of the single obstacle on the two-dimensional plane, and determining the homodromous speed of the intelligent body at the next moment by combining the position information of the intelligent body on the two-dimensional plane and the current movement speed of the intelligent body.
8. An agent cluster motion control system based on a gene regulation network, wherein the gene regulation network comprises an upper network and a lower network, the system comprising:
the acquisition module is used for acquiring the position information of each intelligent agent and the position information of all the obstacles falling within the given detection range of the intelligent agent in real time for each intelligent agent in the intelligent agent cluster;
the first processing module is used for processing the position information of the intelligent body and the position information of all the obstacles through the upper network to obtain a three-dimensional concentration map formed by the intelligent body and all the obstacles, and then mapping the three-dimensional concentration map onto a two-dimensional plane;
the second processing module is used for processing the related information on the two-dimensional plane through the lower network to obtain the obstacle avoidance speed, the aggregation speed and the homodromous speed of the intelligent body at the next moment, and further determining the movement speed of the intelligent body at the next moment;
and the control module is used for controlling the intelligent body to move according to the movement speed.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor executes the computer program to implement the gene regulation network-based agent cluster motion control method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the gene regulation network-based agent cluster motion control method according to any one of claims 1 to 7.
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