CN113172626B - Intelligent robot group control method based on three-dimensional gene regulation and control network - Google Patents

Intelligent robot group control method based on three-dimensional gene regulation and control network Download PDF

Info

Publication number
CN113172626B
CN113172626B CN202110481878.1A CN202110481878A CN113172626B CN 113172626 B CN113172626 B CN 113172626B CN 202110481878 A CN202110481878 A CN 202110481878A CN 113172626 B CN113172626 B CN 113172626B
Authority
CN
China
Prior art keywords
intelligent robot
intelligent
component
target
robot
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110481878.1A
Other languages
Chinese (zh)
Other versions
CN113172626A (en
Inventor
范衠
马培立
谢飞
陈添善
谢敏冲
朱贵杰
石泽
王诏君
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shantou University
Original Assignee
Shantou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shantou University filed Critical Shantou University
Priority to CN202110481878.1A priority Critical patent/CN113172626B/en
Publication of CN113172626A publication Critical patent/CN113172626A/en
Application granted granted Critical
Publication of CN113172626B publication Critical patent/CN113172626B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B25J9/1666—Avoiding collision or forbidden zones
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B25J9/1682—Dual arm manipulator; Coordination of several manipulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Landscapes

  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Manipulator (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses an intelligent robot group control method based on a three-dimensional gene regulation and control network, wherein an intelligent robot group comprises a plurality of intelligent robots, communication connection is established among the intelligent robots, and the method comprises the following steps: the intelligent robot acquires a target position; the intelligent robot detects the environmental information, acquires the positions of the obstacles and other intelligent robots, sends the position of the intelligent robot and the positions of the obstacles to other intelligent robots, and receives the positions of the obstacles and other intelligent robots sent by other intelligent robots; the intelligent robot inputs the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots into a gene regulation network model, and the gene regulation network model outputs a motion component; the intelligent robot controls the motion of the intelligent robot according to the motion component; and the target is captured by the intelligent robot group. The self-adaptive capture of the target is completed while the group intelligent robots avoid the obstacle in the three-dimensional scene.

Description

Intelligent robot group control method based on three-dimensional gene regulation and control network
Technical Field
The invention relates to the technical field of intelligent robot group control, in particular to an intelligent robot group control method based on a three-dimensional gene regulation and control network.
Background
In recent years, Swarm Robotics (SR) research has begun to be widely applied in the fields of bionics, artificial intelligence, military striking, and the like. The swarm intelligent robot as a special robot system is more suitable for complex task scenes than a single robot. The group control model based on the biological heuristic becomes one of the research hotspots in the group robot control field in recent years, wherein a lot of researchers do a lot of research on group pattern generation based on the gene control network. As shown in fig. 1, a distributed swarm robot cooperative clustering algorithm based on an improved gene regulation network is proposed in the patent (publication number: CN108415425B), which overcomes the disadvantages that the prior art cannot simultaneously consider the high-accuracy control of the robot's swarm formation, make the system calculation simple, and effectively reduce the communication burden. As shown in FIG. 2, a method for generating and transforming a multi-level variable gene regulation network population robot model is proposed in the patent (publication number: CN111476337A), which can make an optimal morphological decision in a variable environment and effectively complete the morphological generation and transformation. Although the method can well complete the group capture task in the two-dimensional space, the method does not relate to the group motion control problem in the three-dimensional space. Aiming at the research of group motion control in a three-dimensional space, some prior arts control an underwater robot to perform path planning and capturing in a three-dimensional environment by using a method of combining an FMM and an improved GBNN model. The above prior art cannot adaptively form a colony-surrounding structure.
Disclosure of Invention
The invention provides an intelligent robot group control method based on a three-dimensional gene regulation network, which aims to solve one or more technical problems in the prior art and at least provides a beneficial selection or creation condition.
In a first aspect, an embodiment of the present invention provides an intelligent robot group control method based on a three-dimensional gene regulation and control network, where the intelligent robot group includes a plurality of intelligent robots, and communication connections are established among the plurality of intelligent robots, the method including:
s101, the intelligent robot acquires a target position;
s102, the intelligent robot detects the environmental information, obtains the positions of the obstacles and other intelligent robots, sends the positions of the obstacles and the positions of the obstacles to other intelligent robots, and receives the positions of the obstacles and the positions of other intelligent robots sent by other intelligent robots;
s103, inputting the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots into a gene regulation network model by the intelligent robot, and outputting a motion component by the gene regulation network model;
s104, the intelligent robot controls the motion of the intelligent robot according to the motion component;
and S105, repeating the steps S101-S104 to realize the target capture of the intelligent robot group.
Further, the gene regulation and control network model comprises an upper network and a lower network, the upper network generates a concentration gradient space according to the target position and the position of the obstacle, the lower network generates a group aggregation form according to the concentration gradient space, and generates a motion component for driving the intelligent robot to move according to the group aggregation form, the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots in the concentration gradient space, so that the intelligent robot group forms the group aggregation form.
Further, the generation of the concentration gradient space by the upper network according to the target position and the position of the obstacle specifically includes:
Figure BDA0003048792300000021
Figure BDA0003048792300000022
Figure BDA0003048792300000023
Figure BDA0003048792300000024
wherein M is a morphological gradient space formed under the condition of considering the obstacle and the target, T represents a morphological gradient generated by the target, O represents a morphological gradient generated by the obstacle, and T i And O j Respectively representing the morphological gradients generated by the ith target and the jth obstacle, N t To representNumber of targets, N o Representing the number of obstacles;
Figure BDA0003048792300000025
for Laplacian operator, in equation (1)
Figure BDA0003048792300000026
Represents T i Second derivative of the space in which the concentration gradient is present, formula (2)
Figure BDA0003048792300000027
Represents O j The second derivative of the concentration gradient space; gamma ray i Is target position information; delta j Is obstacle position information; theta, k and x are regulation parameters, sig () represents the sigmod function, and t represents time.
Further, the motion component includes 5 components, respectively: the method comprises the following steps of repelling a force component of a target to an intelligent robot, repelling a force component of the intelligent robot to other intelligent robots, acting a force component of the intelligent robot in a concentration gradient space, acting a force component of a density of a capture robot to the intelligent robot, and repelling a force component of the robot to an obstacle.
Further, the controlling, by the intelligent robot, the motion of the intelligent robot according to the motion component includes: the intelligent robot determines the movement speed of the intelligent robot according to the 5 components, and controls the movement of the intelligent robot at the movement speed; speed of movement V ti The following were used:
Figure BDA0003048792300000028
wherein ti is 1, 2, 3; v ti The ti component of the motion speed is represented, the x axis, ti-1 represents the component on the x axis, ti-2 represents the component on the y axis, ti-3 represents the component on the z axis, v ti The ti-th component, component d, representing a predetermined speed threshold ti The ti component, component n, representing the repulsive force component of the target to the intelligent robot ti Ti component, component z, representing the repulsive force component of the intelligent robot with other trapping intelligent robots ti The ti component, component u, of the acting component force of the intelligent robot according to the concentration field in the concentration gradient space ti A ti component representing the acting component force of the density of the encirclement intelligent robot on the intelligent robot; component b ti A ti-th component representing a repulsive force component of the intelligent robot and the obstacle; d TO Representing the distance between the intelligent robot and the obstacle, threshold representing the threshold distance, norm representing normalization, P ti Representing the distance of movement in time t.
Further, the intelligent robot according to the motion component control intelligent robot's motion still includes: control intelligent robot and remove along the direction that concentration descends in the concentration gradient space, the direction of motion is:
assuming that the position of the target is a coordinate origin, and the coordinate of the intelligent robot in the three-dimensional space is P (x) P ,y P ,z P ) The projection of point P onto the xoy plane is P' (x) p ,y p 0), the included angle between the direction from the intelligent robot to the target and the xoy plane is beta, and the included angle between the direction from the projection point p' to the target and the y axis is alpha;
Figure BDA0003048792300000031
Figure BDA0003048792300000032
further, the step S101 of acquiring the target position by the intelligent robot includes: the intelligent robot receives the target positions sent by other intelligent robots, or the intelligent robot searches for the target, obtains the target position and sends the target position to other intelligent robots.
In a second aspect, an embodiment of the present invention further provides an intelligent robot, where the intelligent robot is one of a group of intelligent robots, and the intelligent robot establishes a communication connection with other intelligent robots, and the intelligent robot includes: a processor, a memory, and a computer readable program stored in the memory, the computer readable program when executed by the processor implementing the method of:
acquiring a target position;
detecting environmental information, acquiring positions of obstacles and other robots, sending the positions of the obstacles and the positions of the obstacles to other intelligent robots, and receiving the positions of the obstacles and the positions of the other intelligent robots sent by the other intelligent robots;
inputting the position of the robot, the target position, the position of the barrier and the positions of other intelligent robots into a gene regulation network model, and outputting a motion component by the gene regulation network model;
and controlling the motion of the intelligent robot according to the motion component.
The intelligent robot group control method based on the three-dimensional gene regulation and control network, provided by the embodiment of the invention, at least has the following beneficial effects: inputting the position of the robot, the target position, the position of the obstacle and the positions of other intelligent robots into the gene regulation network model by constructing the gene regulation network model, and outputting a motion component by the gene regulation network model; when the position of the target changes, the output motion component is automatically updated, and the group intelligent robots can realize self-adaptive capture of the target while avoiding obstacles in the three-dimensional scene by using the gene regulation network model in the three-dimensional scene containing the obstacles.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flow chart of an intelligent robot group control method based on a three-dimensional gene regulation and control network according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a gene regulatory network model according to an embodiment of the present invention;
FIG. 3 is an exploded view of an intelligent robot in three-dimensional space in the direction to a target according to an embodiment of the present invention;
fig. 4 is a schematic diagram of five motion components of the velocity of an intelligent robot according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Fig. 1 is a three-dimensional gene regulation and control network-based intelligent robot population control method, where an intelligent robot population includes multiple intelligent robots, and communication connections are established between the multiple intelligent robots, the method includes the following steps:
s101, the intelligent robot acquires a target position;
the intelligent robot searches for the target, and the intelligent robot acquires the target position and comprises: the intelligent robot receives the target positions sent by other intelligent robots, or the intelligent robot searches for the target, obtains the target position and sends the target position to other intelligent robots.
S102, the intelligent robot detects the environmental information, obtains the positions of the obstacles and other robots, sends the positions of the obstacles and the positions of the obstacles to other intelligent robots, and receives the positions of the obstacles and the positions of other intelligent robots sent by other intelligent robots;
the intelligent robot comprises a position sensor, environment information is detected through the position sensor, the positions of obstacles and other intelligent robots are obtained, and the position of the intelligent robot, the positions of the obstacles and the positions of the obstacles are shared with other intelligent robots.
S103, inputting the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots into a gene regulation network model by the intelligent robot, and outputting a motion component by the gene regulation network model;
the schematic structure of the gene regulation network model is shown in FIG. 2, and comprises an upper network and a lower network, wherein the upper network comprises an XNOR gate model, T i And O i Respectively representing the protein concentration generated by a target and an obstacle in the environment, M represents a concentration gradient space, an upper network is used for generating the concentration gradient space and transmitting the concentration gradient space to a lower network, and M is used for inputting T i And O i And carrying out the same or operation. In the lower network, the population aggregate morphology, P, is generated according to the concentration gradient space i And G i Respectively represent the current position and the internal state of the ith intelligent robot, wherein G i Changes occur with the position information of the neighboring intelligent robot. With T i 、O i 、G i The information sharing of the target and the barrier can be guaranteed between the intelligent robot enclosure. As in fig. 2, the smart robot 2 can transmit the target position and the position of the obstacle to the smart robot 1, and the change in the distance between the smart robot 2 and the smart robot 1 affects G i 。
The upper network of the gene regulation network model generates a concentration gradient space according to the target position and the position of the obstacle, the lower network generates a group aggregation form according to the concentration gradient space, and a motion component for driving the intelligent robot to move is generated according to the group aggregation form, the position of the lower network, the target position, the position of the obstacle and the positions of other intelligent robots in the concentration gradient space, so that the intelligent robot group forms the group aggregation form.
The positions of the obstacles and the positions of the targets are used as input to generate a concentration gradient space (namely, a global concentration map), and the conversion of the concentration gradient space into a population aggregation form is mainly divided into two steps: 1) generating a concentration gradient space containing the obstacle according to the gene regulatory function; 2) different morphological gradients are extracted according to the equipotential lines of the concentration gradient spatial information, and a proper threshold value is selected to adaptively generate a group aggregation morphology. Each intelligent robot follows the following kinetic equation and generates a suitable concentration gradient space containing barrier information, specifically:
Figure BDA0003048792300000051
Figure BDA0003048792300000052
Figure BDA0003048792300000053
Figure BDA0003048792300000054
wherein M is a morphological gradient space formed under the condition of considering the obstacle and the target, T represents a morphological gradient generated by the target, O represents a morphological gradient generated by the obstacle, and T i And O j Respectively representing the morphological gradients generated by the ith target and the jth obstacle, N t Representing the number of targets, N o Representing the number of obstacles;
Figure BDA0003048792300000055
for Laplacian operator, in equation (1)
Figure BDA0003048792300000056
Represents T i Second derivative of the space in which the concentration gradient is present, formula (2)
Figure BDA0003048792300000057
Represents O j The second derivative of the concentration gradient space in which it is located; gamma ray i Is target position information; delta j Is position information of the obstacle; theta, k and x are control parameters, t represents time, sig () tableA sigmod function. The expression (3) represents a concentration gradient space containing an obstacle obtained by processing the sample with an XNOR model under an environmental input. The formula (4) further normalizes the concentration gradient space to ensure that the concentration gradient space is within a proper range.
In the lower network, generating motion components for driving the intelligent robot to move according to the group aggregation state, the self position, the target position, the position of the obstacle and the positions of other intelligent robots in a concentration gradient space, wherein the motion components comprise 5 components which are respectively: the method comprises the following steps of repelling a force component of a target on the intelligent robot, repelling a force component of the intelligent robot and other intelligent robots, acting a force component of the intelligent robot in a concentration gradient space, acting a force component of a density of the intelligent robot on the intelligent robot, and repelling a force component of the intelligent robot and an obstacle.
S104, the intelligent robot controls the motion of the intelligent robot according to the motion component;
in one embodiment, as shown in fig. 4, the intelligent robot determines the movement speed of the intelligent robot according to 5 components, and controls the movement of the intelligent robot at the movement speed; in fig. 4, Obstacle represents an Obstacle, a star icon represents a target position, and 5 components are: the method comprises the following steps of repelling a force component of a target to an intelligent robot, repelling a force component of the intelligent robot to other intelligent robots, acting a force component of the intelligent robot in a concentration gradient space, acting a force component of a density of a capture robot to the intelligent robot, and repelling a force component of the robot to an obstacle. The movement speed is as follows:
Figure BDA0003048792300000061
wherein ti is 1, 2, 3; v ti Ti-th component representing velocity, ti-1 representing the component on the x-axis, ti-2 representing the component on the y-axis, ti-3 representing the component on the z-axis, v ti The ti-th component, component d, representing a predetermined speed threshold ti The ti-th component, component n, representing the repulsive force component of the target to the robot ti Indicating that the robot is in close proximity to other robotsThe ti-th component of the repulsive force component, component z ti The ti component, z, representing the component force of the intelligent robot according to the concentration field in the concentration gradient space ti Mainly prompting the intelligent robot to follow the target and move to the vicinity of the enclosure; component u ti The ti component represents the acting force component of the density of the encirclement intelligent robot on the robot, and the robot group can be uniformly diffused to the surrounding ring to form a group aggregation shape; component b ti A ti-th component representing a repulsive force component of the intelligent robot and the obstacle; d TO Representing the distance between the intelligent robot and the obstacle, threshold representing the threshold distance, norm representing normalization, P ti Representing the distance of movement in time t.
When the intelligent robot is too close to the target distance, the intelligent robot needs to be controlled to be far away from the target, for example, when the Euclidean distance is less than 4m, a repulsive component force d of the target to the robot is triggered i 。
When the density between the intelligent robot and other intelligent robots is too large, the intelligent robot is controlled to be far away from other intelligent robots, for example, when the Euclidean distance is less than 2m, the repulsive component force n of the robot and other intelligent robots for enclosure is triggered i 。
When the distance between the intelligent robot and the obstacle is too close, for example, less than a threshold distance, the intelligent robot and the obstacle repulsive force b is triggered independently i 。
In one embodiment, the controlling of the movement of the smart robot according to the movement component by the smart robot includes: control intelligent robot and remove along the direction that concentration descends in the concentration gradient space, the direction of motion is:
assuming that the position of the target is a coordinate origin o, and the coordinate of the intelligent robot in the three-dimensional space is P (x) P ,y P ,z P ) The projection of point P onto the xoy plane is P' (x) P ,y P 0), the included angle between the direction from the intelligent robot to the target and the xoy plane is beta, and the intersection point from the projection point p 'to the vertical line of the y axis is p' (0, y) P 0), the included angle between the direction from the projection point p' to the target and the y axis is alpha; as shown in fig. 3.
Figure BDA0003048792300000071
Figure BDA0003048792300000072
Wherein, alpha belongs to (-pi, pi),
Figure BDA0003048792300000073
and S105, repeating the steps S101-S104 to realize the target capture of the intelligent robot group.
The target escapes, and therefore the target position changes, and therefore the steps S101 to S104 are repeated until the intelligent robot group forms the group aggregation morphology, so as to realize the target enclosure of the intelligent robot group.
The embodiment of the present invention further provides an intelligent robot, wherein the intelligent robot is one of the groups of intelligent robots, the intelligent robot establishes communication connection with other intelligent robots, and the intelligent robot includes: a processor, a memory, and a computer readable program stored in the memory, the computer readable program when executed by the processor implementing the method of:
acquiring a target position;
detecting environmental information, acquiring the position of an obstacle, sending the position of the obstacle and the position of the obstacle to other intelligent robots, and receiving the position of the obstacle and the positions of other intelligent robots sent by other intelligent robots;
inputting the position of the robot, the target position, the position of the barrier and the positions of other intelligent robots into a gene regulation network model, and outputting a motion component by the gene regulation network model;
and controlling the motion of the intelligent robot according to the motion component.
In addition, when the computer readable program is executed by the processor, the above-mentioned method for controlling the human body of the intelligent robot based on the three-dimensional gene regulation and control network is implemented, which is not described herein again.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.

Claims (6)

1. An intelligent robot group control method based on a three-dimensional gene regulation network is characterized in that an intelligent robot group comprises a plurality of intelligent robots which are in communication connection with each other, and the method comprises the following steps:
s101, the intelligent robot acquires a target position;
s102, the intelligent robot detects the environmental information, obtains the positions of the obstacles and other intelligent robots, sends the positions of the obstacles and the positions of the obstacles to other intelligent robots, and receives the positions of the obstacles and the positions of other intelligent robots sent by other intelligent robots;
s103, inputting the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots into a gene regulation network model by the intelligent robot, and outputting a motion component by the gene regulation network model;
s104, the intelligent robot controls the motion of the intelligent robot according to the motion component;
s105, repeating the steps S101-S104 to realize the target capture of the intelligent robot group;
the gene regulation network model comprises an upper network and a lower network, wherein the upper network generates a concentration gradient space according to a target position and a position of an obstacle, the lower network generates a group aggregation form according to the concentration gradient space, and generates a motion component for driving the intelligent robot to move according to the group aggregation form, the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots in the concentration gradient space, so that the intelligent robot group forms the group aggregation form;
the generation of the concentration gradient space by the upper network according to the target position and the position of the obstacle specifically comprises the following steps:
Figure FDA0003694984310000011
Figure FDA0003694984310000012
Figure FDA0003694984310000013
Figure FDA0003694984310000014
wherein M is a morphological gradient space formed under the condition of considering the obstacle and the target, T represents a morphological gradient generated by the target, O represents a morphological gradient generated by the obstacle, and T i And O j Respectively representing the morphological gradients generated by the ith target and the jth obstacle, N t Representing the number of targets, N o Indicating the number of obstacles;
Figure FDA0003694984310000015
for Laplacian operator, in equation (1)
Figure FDA0003694984310000016
Represents T i Second derivative of the space in which the concentration gradient is present, formula (2)
Figure FDA0003694984310000017
Represents O j The second derivative of the concentration gradient space; gamma ray i Is target position information; delta j Is position information of the obstacle; theta, k and x are regulation parameters, sig () represents a sigmod function, and t represents time.
2. The intelligent robot population control method based on the three-dimensional gene regulation network, according to claim 1, wherein the motion components comprise 5 components, which are respectively: the method comprises the following steps of repelling a force component of a target to an intelligent robot, repelling a force component of the intelligent robot to other intelligent robots, acting a force component of the intelligent robot in a concentration gradient space, acting a force component of a density of a capture robot to the intelligent robot, and repelling a force component of the robot to an obstacle.
3. The intelligent robot group control method based on the three-dimensional gene regulation network of claim 2, wherein the intelligent machineThe robot controls the motion of the intelligent robot according to the motion component, and the robot comprises: the intelligent robot determines the movement speed of the intelligent robot according to the 5 components, and controls the movement of the intelligent robot at the movement speed; speed of movement V ti The following:
Figure FDA0003694984310000021
wherein ti is 1, 2, 3; v ti Ti component representing the speed of motion, ti-1 representing the component on the x-axis, ti-2 representing the component on the y-axis, ti-3 representing the component on the z-axis, v ti The ti-th component, component d, representing a predetermined speed threshold ti The ti-th component, component m, representing the repulsive force component of the target to the intelligent robot ti Ti component, component z, representing the repulsive force component of the intelligent robot with other trapping intelligent robots ti The ti component, component u, of the acting component force of the intelligent robot according to the concentration field in the concentration gradient space ti A ti component representing the acting component force of the density of the encirclement intelligent robot on the intelligent robot; component n ti A ti-th component representing a repulsive force component of the intelligent robot and the obstacle; d To Representing the distance between the intelligent robot and the obstacle, threshold representing the threshold distance, norm representing normalization, P ti Representing the distance of movement in time t.
4. The intelligent robot population control method based on the three-dimensional gene regulation and control network as claimed in claim 3, wherein the controlling of the intelligent robot movement according to the movement component by the intelligent robot comprises: control intelligent robot and remove along the direction that concentration descends in the concentration gradient space, the direction of motion is:
assuming that the position of the target is the origin of coordinates, the coordinate of the intelligent robot in the three-dimensional space is P (x) P ,y P ,z P ) The projection of point P onto the xoy plane is P' (x) P ,y P 0), the included angle between the direction from the intelligent robot to the target and the xoy plane is beta, and the included angle between the direction from the projection point p' to the target and the y axis is alpha；
Figure FDA0003694984310000022
Figure FDA0003694984310000023
5. The intelligent robot population control method based on the three-dimensional gene regulation and control network as claimed in claim 1, wherein the step S101 of acquiring the target position by the intelligent robot comprises: the intelligent robot receives the target positions sent by other intelligent robots, or the intelligent robot searches for the target, obtains the target position and sends the target position to other intelligent robots.
6. An intelligent robot, wherein the intelligent robot is one of a group of intelligent robots, the intelligent robot establishes a communication connection with other intelligent robots, and the intelligent robot comprises: a processor, a memory, and a computer readable program stored in the memory, the computer readable program when executed by the processor implementing the method of:
acquiring a target position;
detecting environmental information, acquiring positions of obstacles and other robots, sending the positions of the obstacles and the positions of the obstacles to other intelligent robots, and receiving the positions of the obstacles and the positions of the other intelligent robots sent by the other intelligent robots;
inputting the position of the robot, the target position, the position of the barrier and the positions of other intelligent robots into a gene regulation network model, and outputting a motion component by the gene regulation network model;
controlling the motion of the intelligent robot according to the motion component;
the gene regulation network model comprises an upper network and a lower network, wherein the upper network generates a concentration gradient space according to a target position and a position of an obstacle, the lower network generates a group aggregation form according to the concentration gradient space, and generates a motion component for driving the intelligent robot to move according to the group aggregation form, the position of the intelligent robot, the target position, the position of the obstacle and the positions of other intelligent robots in the concentration gradient space, so that the intelligent robot group forms the group aggregation form;
the generation of the concentration gradient space by the upper network according to the target position and the position of the obstacle specifically comprises the following steps:
Figure FDA0003694984310000031
Figure FDA0003694984310000032
Figure FDA0003694984310000033
Figure FDA0003694984310000034
wherein M is a morphological gradient space formed under the condition of considering the obstacle and the target, T represents a morphological gradient generated by the target, O represents a morphological gradient generated by the obstacle, and T i And O j Respectively representing the morphological gradients generated by the ith target and the jth obstacle, N t Representing the number of targets, N o Representing the number of obstacles;
Figure FDA0003694984310000035
for Laplacian operator, in equation (1)
Figure FDA0003694984310000036
Represents T i Second derivative of the space of concentration gradient, formula(2) In (1)
Figure FDA0003694984310000037
Represents O j The second derivative of the concentration gradient space; gamma ray i Is target position information; delta j Is position information of the obstacle; theta, k and x are regulation parameters, sig () represents a sigmod function, and t represents time.
CN202110481878.1A 2021-04-30 2021-04-30 Intelligent robot group control method based on three-dimensional gene regulation and control network Active CN113172626B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110481878.1A CN113172626B (en) 2021-04-30 2021-04-30 Intelligent robot group control method based on three-dimensional gene regulation and control network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110481878.1A CN113172626B (en) 2021-04-30 2021-04-30 Intelligent robot group control method based on three-dimensional gene regulation and control network

Publications (2)

Publication Number Publication Date
CN113172626A CN113172626A (en) 2021-07-27
CN113172626B true CN113172626B (en) 2022-08-16

Family

ID=76925909

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110481878.1A Active CN113172626B (en) 2021-04-30 2021-04-30 Intelligent robot group control method based on three-dimensional gene regulation and control network

Country Status (1)

Country Link
CN (1) CN113172626B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114397887B (en) * 2021-12-21 2023-06-06 汕头大学 Group robot aggregation control method based on three-layer gene regulation network
CN114326747B (en) * 2022-01-06 2023-10-24 中国人民解放军国防科技大学 Multi-target capture control method and device for group robot and computer equipment
CN114955455B (en) * 2022-06-14 2024-06-11 乐聚(深圳)机器人技术有限公司 Robot control method, server, robot, and storage medium
CN115145289A (en) * 2022-09-02 2022-10-04 汕头大学 Multi-agent cooperative trapping method, system, equipment and storage medium
CN115390589B (en) * 2022-10-27 2023-02-28 汕头大学 Unmanned aerial vehicle cluster control method and device, electronic equipment and storage medium
CN116578092B (en) * 2023-05-29 2023-11-14 汕头大学 Gene regulation network-based intelligent agent cluster motion control method and system

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102008019345A1 (en) * 2008-04-17 2009-10-22 Kuka Roboter Gmbh X-ray device and medical workstation
CN103268729B (en) * 2013-05-22 2015-08-19 北京工业大学 Based on mobile robot's tandem type map creating method of composite character
CN105225604B (en) * 2015-10-30 2018-06-29 汕头大学 A kind of construction method of the mixing map of Mobile Robotics Navigation
CN109910010A (en) * 2019-03-23 2019-06-21 广东石油化工学院 A kind of system and method for efficient control robot
CN112462779B (en) * 2020-11-30 2023-07-25 汕头大学 Dynamic group robot trapping control method and system based on gene regulation network

Also Published As

Publication number Publication date
CN113172626A (en) 2021-07-27

Similar Documents

Publication Publication Date Title
CN113172626B (en) Intelligent robot group control method based on three-dimensional gene regulation and control network
Zhen et al. Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization
Cagnoni Genetic and evolutionary computation for image processing and analysis
Jiang et al. Path planning for robotic manipulator in complex multi-obstacle environment based on improved_RRT
CN111015656A (en) Control method and device for robot to actively avoid obstacle and storage medium
CN114859904B (en) Cluster trapping method, executing device and system based on E-GRN
Hou et al. Dynamic compound shape control of robot swarm
Issa et al. A survey of multi-mobile robot formation control
EP3853765A1 (en) Training a deep neural network model to generate rich object-centric embeddings of robotic vision data
CN112462779A (en) Group robot dynamic capture control method and system based on gene regulation network
CN112596549A (en) Multi-unmanned aerial vehicle formation control method, device and medium based on continuous convex rule
Liang et al. Bio-inspired self-organized cooperative control consensus for crowded UUV swarm based on adaptive dynamic interaction topology
Wu et al. Vision-based target detection and tracking system for a quadcopter
Tian et al. An improved RRT robot autonomous exploration and SLAM construction method
CN114397887B (en) Group robot aggregation control method based on three-layer gene regulation network
Liao et al. Model predictive control for cooperative hunting in obstacle rich and dynamic environments
Kim Self-organization of unicycle swarm robots based on a modified particle swarm framework
Guo et al. Convolutional neural network-based robot control for an eye-in-hand camera
Yan et al. Dynamic formation control for autonomous underwater vehicles
Shahriari et al. Lightweight collision avoidance for resource-constrained robots
CN116520852A (en) Method, device and equipment for capturing multiple targets by group robots under local information
Santana et al. Swarm cognition on off-road autonomous robots
Golluccio et al. Objects relocation in clutter with robot manipulators via tree-based q-learning algorithm: Analysis and experiments
Kondratenko et al. Safe Navigation of an Autonomous Robot in Dynamic and Unknown Environments
Nurmaini et al. Motion coordination for swarm robots

Legal Events

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