CN116520852A - Method, device and equipment for capturing multiple targets by group robots under local information - Google Patents
Method, device and equipment for capturing multiple targets by group robots under local information Download PDFInfo
- Publication number
- CN116520852A CN116520852A CN202310651389.5A CN202310651389A CN116520852A CN 116520852 A CN116520852 A CN 116520852A CN 202310651389 A CN202310651389 A CN 202310651389A CN 116520852 A CN116520852 A CN 116520852A
- Authority
- CN
- China
- Prior art keywords
- robot
- target
- robots
- coordinate system
- local coordinate
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 40
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 31
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 26
- 230000000694 effects Effects 0.000 claims abstract description 15
- 230000033001 locomotion Effects 0.000 claims description 34
- 230000002776 aggregation Effects 0.000 claims description 11
- 238000004220 aggregation Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 239000004973 liquid crystal related substance Substances 0.000 claims description 6
- 230000001066 destructive effect Effects 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 4
- 238000005755 formation reaction Methods 0.000 description 22
- 238000003860 storage Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000004048 modification Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 239000003795 chemical substances by application Substances 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 229910003460 diamond Inorganic materials 0.000 description 2
- 239000010432 diamond Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000004034 genetic regulation Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0289—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling with means for avoiding collisions between vehicles
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- 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)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Aviation & Aerospace Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a method, a device and equipment for capturing multiple targets by a group robot under local information, wherein the method comprises the following steps: discovering and determining targets to be captured; taking a first robot for finding a target as a center point, and simultaneously creating a local coordinate system by combining the positions of neighbor robots of the first robot; determining the position of the target in a local coordinate system; determining a second robot participating in capturing the target at least based on a local coordinate system, the position of a neighbor robot in the local coordinate system and the position of the target in the local coordinate system, wherein the first robot and the second robot form a capturing group; based on a local coordinate system, and through simulating activities between genes and proteins, a trapping formation mode corresponding to the trapping group is cooperatively created, wherein the trapping formation mode is used for guiding the first robot and the second robot to move so as to trap the target. The method based on the embodiment can enable the group robots to automatically realize grouping and target trapping according to different targets.
Description
Technical Field
The embodiment of the invention relates to the technical field of robots, in particular to a method, a device and equipment for capturing multiple targets by a group robot under local information.
Background
For group robots moving in harsh or unknown outdoor environments without GPS guidance and global communication, algorithms that rely on global information are not feasible. In general, conventional gene regulatory networks (gene regulatory networks, GRNs) perform excellently in forming a trapping pattern for targets, requiring accurate global positional information to guide group robots. Therefore, the group robots cannot find the targets by themselves, carry out target trapping according to the targets by themselves in groups, and can not realize target trapping according to the local information of the targets and the neighbor robots.
Disclosure of Invention
The invention provides a multi-target capturing method for group robots under local information, which comprises the following steps:
discovering and determining targets to be captured;
taking a first robot which finds the target as a center point, and simultaneously creating a local coordinate system by combining the positions of neighbor robots of the first robot;
determining a position of the target in the local coordinate system;
determining a second robot participating in trapping the target based on at least the local coordinate system, the position of the neighbor robot in the local coordinate system, and the position of the target in the local coordinate system, wherein the first robot and the second robot form a trapping group;
and on the basis of the local coordinate system, establishing a trapping formation mode corresponding to the trapping group in a matching way by simulating activities between genes and proteins, wherein the trapping formation mode is used for guiding the first robot and the second robot to move so as to trap the target.
As an alternative embodiment, the creating a local coordinate system with the first robot that finds the target as a center point and combining the positions of the neighboring robots of the first robot includes:
determining an X axis according to the position of a first neighbor robot closest to the first robot;
determining a Y axis according to the positions of the second neighbor robots which are respectively equal to the first neighbor robots and the distances between the first robots;
and creating the local coordinate system according to the central point, the X axis and the Y axis.
As an alternative embodiment, further comprising:
the first robots and the neighbor robots can communicate with each other to determine the positions of the robots and the targets;
the determining a second robot participating in trapping the object based at least on the local coordinate system, a position of a neighbor robot in the local coordinate system, a position of the object in the local coordinate system, comprising:
determining the positions of the neighbor robots and the targets in the local coordinate system based on the position calculation of each robot and each target;
and determining that the neighbor robot meeting the position condition is the second robot at least based on the positions of the neighbor robot and the target in the local coordinate system.
As an alternative embodiment, the creating a capture queuing mode corresponding to the capture group by simulating activity between genes and proteins includes:
generating a desired protein concentration to create a capture formation pattern corresponding to the capture population based on the following power equation:
wherein, gamma j Is a scalar value, representing the environmental input of the equation,for Laplacian, p j Representing the integrated protein concentration, g, directly observed by the first robot and indirectly received from a neighboring robot with respect to the location of the target 1 ，g 2 And g 3 Expressed as protein concentration, g 3 From g 1 And g 2 Co-regulation of ρ, θ 1 ，θ 2 ，θ 3 Is a positive constant.
As an alternative embodiment, further comprising:
and setting a controller in each robot to correspondingly control the movement of the first robot and the second robot in the capture formation mode based on the controller.
As an optional embodiment, the controller may comprehensively determine motion parameters corresponding to the first robot and the second robot when capturing the target according to the number of other targets in the preset radius of the target, the safety distance between the robots in the capturing group, the robot density of the positions of the robots in the capturing group, and the obstacle in the moving range of the robots, where the motion parameters satisfy that no abnormal state occurs in the motion process of the first robot and the second robot, where the abnormal state at least includes collision, excessive aggregation, and coordinate drift.
As an optional embodiment, the motion parameters include calculating, based on the controller correspondence, motion speeds of the first robot and the second robot in different scenarios, where the different scenarios include avoidance of other targets, robots, obstacles, and reduction of destructive aggregation of robots.
As an alternative embodiment, the controller controls the robot to move based on vector sum of the movement speeds of the same robot in different scenes so as to achieve trapping of the target.
Another embodiment of the present invention also provides a multi-target trapping device for a group robot, including:
the first determining module is used for finding and determining a target to be captured;
a first creating module, configured to create a local coordinate system with a first robot that finds the target as a center point, and simultaneously with positions of neighboring robots of the first robot;
a second determining module for determining a position of the target in the local coordinate system;
the third determining module is used for determining a second robot participating in capturing the target at least according to the local coordinate system, the position of the neighbor robot in the local coordinate system and the position of the target in the local coordinate system;
the second creation module is used for cooperatively creating a trapping formation mode corresponding to the second robots according to the local coordinate system by simulating activities between genes and proteins, and the trapping formation mode is used for guiding each second robot to move so as to trap the target.
Another embodiment of the present invention also provides an electronic device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to implement a group robot-trapped multi-target method under local information as described in any of the embodiments above.
Based on the disclosure of the embodiment, the method has the advantages that the method can realize automatic grouping of the robots when the targets are found, and can realize effective capturing of the targets based on local information only, thereby enhancing multi-target capturing performance of large-scale groups in a limited environment, eliminating the rigidity requirement of the prior capturing process on global information of the robots and the targets, expanding the application range, and improving capturing precision.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
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 the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a method for capturing multiple targets by a group robot under local information in an embodiment of the invention.
Fig. 2 is an application process diagram of a cluster robot multi-target capturing method under local information in an embodiment of the invention.
Fig. 3 is a block diagram of a group robot capturing multiple targets under local information in an embodiment of the invention.
Detailed Description
Hereinafter, specific embodiments of the present invention will be described in detail with reference to the accompanying drawings, but not limiting the invention.
It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the following description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this disclosure will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the invention has been described with reference to some specific examples, a person skilled in the art will certainly be able to achieve many other equivalent forms of the invention, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the disclosure in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a method for capturing multiple targets by a group robot under local information, including:
s101: discovering and determining targets to be captured;
s102: taking a first robot for finding a target as a center point, and simultaneously creating a local coordinate system by combining the positions of neighbor robots of the first robot;
s103: determining the position of the target in a local coordinate system;
s104: determining a second robot participating in capturing the target at least based on the local coordinate system, the position of the neighbor robot in the local coordinate system and the position of the target in the local coordinate system, wherein the first robot and the second robot form a capturing group;
s105: based on a local coordinate system, and by simulating activities between genes and proteins, a trapping formation mode corresponding to a trapping group is cooperatively created, wherein the trapping formation mode is used for guiding the first robot and the second robot to move so as to trap the target.
The method in the embodiment can be applied to a complex environment, and any one of the group robots can activate a new robot subgroup, namely a trapping subgroup, when a new target is found, so as to be used for trapping different targets. Therefore, the method of the embodiment can realize automatic grouping of the robots when the targets are found, realize effective capturing of the targets based on local information only, enhance the multi-target capturing performance of large-scale groups in a limited environment, eliminate the rigidity requirement of the prior capturing process on global information of the robots and the targets, enlarge the application range and improve the capturing precision.
In order for the agents within the robot to function in a more practical situation, to achieve timely and efficient creation of a trapping group, while being able to determine the target location for efficient trapping, the present embodiment creates a genetic regulation network with local information about the robot and the target, specifically, first requires the establishment of a local coordinate system:
creating a local coordinate system with a first robot that finds a target as a center point while combining positions of neighbor robots of the first robot, comprising:
s106: determining an X axis according to the position of a first neighbor robot closest to the first robot;
s107: determining a Y axis according to the positions of the second neighbor robots which are respectively equal to the distance between the first neighbor robots;
s108: a local coordinate system is created from the center point, the X-axis, and the Y-axis.
For example, a first order organizer, i.e. the first robot that finds and determines the target, may be determined as the center of the coordinate system, i.e. a center-bot, based on the target triggering hierarchical self-organizing grouping algorithm, since it is closest to the target. Next, the first robot propagates the IDs of its nearest neighbors. The X-axis of the local coordinate system is determined by the nearest neighbor, called X-bot, and corresponds to the X-axis of the coordinate system. Once the x-bot receives the message from the center-bot and identifies itself as an x-bot, it sets its own location to (d) cx 0), where d cx Is the distance between the x-bot and the center-bot. Thereafter, the x-bot changes its positioning state to determine and propagate its position. Once this is done, the first robot may select one robot from the neighbor robots that can see the x-bot as the h-bot, corresponding to the Y-axis of the coordinate system. The h-bot should satisfy the following conditions: the h-bot, center-bot and x-bot are kept as equidistant as possible. Namely:
wherein d is cx And d ci The distances d from the central robot to the x-bot and robot i, respectively ix Is the distance of robot i to x-bot. In this section, the robot motion may resolve and correct the possible rollover ambiguity of the coordinate system to right-handedness. Thus, it can be assumed that the h-bot is at the top of the x-axis, and the relative position can be calculatedCalculated as
Wherein d is hx Is the distance between h-bot and x-bot, d hc Is the distance between the h-bot and the center-bot. Both were measured and calculated by h-bot.
Further, the method in this embodiment further includes:
s109: the first robots and the neighbor robots can communicate with each other to determine the positions of the robots and the targets;
determining a second robot participating in capturing the object based at least on the local coordinate system, the position of the neighbor robot in the local coordinate system, the position of the object in the local coordinate system, comprising:
s110: determining the positions of the neighbor robots and the targets in a local coordinate system based on the position calculation of each robot and each target;
s111: and determining that the neighbor robot meeting the position condition is at least a second robot based on the positions of the neighbor robot and the target in the local coordinate system.
That is, when three robots (center-bot, x-bot, h-bot) constitute a local coordinate system, other robots capable of communicating with the three robots can calculate their relative positions. Each robot propagates its estimated position to its neighbors when calculating its relative position, thereby achieving a comprehensive propagation of local position information through subgroups. Since the subgroups in this embodiment also have the characteristic of local interaction, that is, there is always at least one peer in each of the neighbor subgroups of the robots, all members in each subgroup can receive the relative position information of their neighbors. After knowing the position information of the robots in the local coordinate system, it can be determined whether to participate in capturing of the target by combining the positions of the target, for example, whether the robot is far away from the target, has too many obstacles in the process of moving to the target, whether the robot for capturing the target is enough, and the like, so as to determine a plurality of second robots for capturing.
Further, according to the relative position of the target, the swarm robot can form a specific geometric shape by adjusting the protein concentration, and by simulating the activity between the genes and the proteins, the relation between the target and the robot is established, namely the generation and the formation of the trapping formation mode are realized, in particular:
cooperating by modeling activity between genes and proteins to create a set of capture patterns corresponding to a set of capture clusters, comprising:
s112: generating the desired protein concentration to create a capture formation pattern for the corresponding capture population based on the following power equation:
in the embodiment, the grouped organic robots are all provided with airborne sensors, and the relative position of the targets can be determined in the detection range or can be determined from the following pointsLearning from nearby tissue robots of the same group. Wherein, gamma j Is a scalar value representing the environmental inputs of these dynamic equations, maintaining a positive constant value at the location of the target.As a laplace operator, can be regarded as a diffusion process in biological systems, described in the spatial domain as a second derivative, representing an environmental input p from a target j Protein concentration, p j Representing the integrated protein concentration of the target directly observed by the organized robots and indirectly received from neighboring robots within the same subgroup. g 1 ，g 2 And g 3 Expressed as protein concentration, g 3 From g 1 And g 2 Co-modulation is the definition of the morphological gradient of the final target pattern. The sigmoid function, also called logistic function, is used for the output of neurons of the hidden layer, ranging in value from (0, 1), which maps a real number to the interval (0, 1), ρ, θ 1 ，θ 2 ，θ 3 Is a positive constant.
Further, the local coordinate system is constantly updated as each subgroup moves, and thus, potential coordinate drift may occur, compromising the accuracy of the trap formation. Drift can cause jumps in concentration near the target. At the same time, the formulation is sensitive to the concentration gradient around itself. Thus, some robotic agents may be in an abnormal state, such as pacing the robot back and forth in areas where concentration changes jump. Some robots may be clustered together somewhere instead of forming a trap pattern. Thus, to improve this situation, the robot is enabled to move with high precision to capture the object, and the method of the present embodiment further includes:
a controller is arranged in each robot to correspondingly control the movement of the first robot and the second robot in the trapping formation mode based on the controller.
The controller can comprehensively determine the motion parameters of the corresponding first robot and second robot when the targets are captured according to the number of other targets in the preset radius of the positions of the targets, the safety distance between the robots in the capturing group, the robot density of the positions of the robots in the capturing group and the obstacles in the moving range of the robots, wherein the motion parameters meet the condition that no abnormal state of the first robot and the second robot occurs in the motion process, and the abnormal state at least comprises collision, excessive aggregation and coordinate drift. The motion parameters comprise motion speeds of the first robot and the second robot under different scenes based on the corresponding calculation of the controller, wherein the different scenes comprise avoidance of other targets, robots and obstacles and reduction of destructive aggregation of the robots. After each motion parameter is determined, the controller can control the motion of the robot based on vector sum of motion speeds of the same robot in different scenes to achieve the object trapping, a specific trapping effect can be shown by referring to fig. 2, black dots in the drawing represent the robot, hollow diamond boxes represent the object to be trapped, and a plurality of solid diamond boxes arranged and combined can represent the moving direction of the robot.
Specifically, the controller in the present embodiment includes five elements: target (T), neighbor (N), pattern (P), density (D) and obstacle (O), i.e. the robot motion is controlled comprehensively according to the five elements.
(1) The object is: for following or rejecting objects, a linear distance R with maximum safe distance target Depending on the speed term under which the robot starts to follow a specified target or to reverse other targets that match its grouping state. G (g) th The targets represent targets for subgroup tags. Here, the group cannot acquire and perceive global information about the target, so g may be numbered with the unique ID of the first organizer (i.e., the first robot) in the group. Robot i th To target j th Can be calculated by calculating the direction of (2)Obtained by->And->Respectively represent robots i th Is the position of (1) and target j th N, N t Represents radius R target The number of other targets in the system.
(2) Neighbor: a suitable distance is required between the robot and the neighboring robot, and thus, one contains a safe distance R neighbor The vectors of the neighboring robots of (a) provide an efficient collision avoidance of the robots with each other. N (N) c Representing a safe distance R neighbor Number of inner neighbors.
(3) Formation mode: a specific pointer guides the maximum concentration gradient of the trapping agent around the robot in the trapping formation mode, namely the concentration of the robot positionG' is a set limiting the concentration around the robot i to a radius R pattern Within the range. Calculating +.>Is used to direct the robot to advance the maximum gradient toward the desired trapping mode in equation (a).
(4) Density: an effective strategy to reduce destructive aggregation is to construct adaptive density guidance that can lead to a wide population shape when the population wishes to deploy collective formations, particularly large scale formations. Too much aggregation can cause unavoidable rejection between robots, thereby causing collisions or loss to varying degrees. Density is N which is used for guiding the robot to move towards the direction with less neighbors d Represents the observation radius d obs Number of neighbor robots within. In the present embodiment, the permanent density effect between neighboring robots is designed and a relatively small weight p is utilized density1 Control is performed with relatively large weight p density2 For assisting the robot in escaping from the disturbance caused by the relative coordinate system when approaching the desired target. Since the relative coordinates are being updated and the source of the coordinate system is moving, there is a deviation of the relative coordinates of the targets at adjacent times. The deviation is so small that the colony can still bypass it to approach the target. However, such deviations may cause the perceived position of the target to repeatedly bounce on its left and right sides, resulting in a disturbance of the group behavior, e.g., the group behavior is stuck rather than being trapped to the desired target k g Is provided. Relatively large weight p density2 The population may be enabled to avoid dilemma, disperse, and form a pattern of traps around the target. Thus, the trapping group is triggered in the vicinity of the target.
(5) An obstacle: the boundaries of the obstacle expand slightly and the expanded object is shaped like the original object. The robots may cooperate to generate corresponding virtual agents after identifying the obstacle and fill them in the expansion area providing the group buffer distance. These virtual agents interact with the robot only when the robot actually observes them. Robot i to nearest virtual agent r near Is of the direction of (2)Is the initial expected collision-free vector between the robot and the obstacle at the current moment, wherein the baseline vector v of the virtual agent representation base Inward perpendicular to the polygonal edges of the barrier. The two vectors v can be compared when in use obstacle And v base The angular relationship between the two is chosen to direct the robot to the possible collision-free region phi with positive and negative offsets. The calculation of the avoidance process continues until the robot leaves any virtual agent radius R avoid The designated area within the interior is then terminated.
After the controller calculates the above-mentioned each interaction item, i.e. each different speed, a vector sum can be calculated for it to obtain a final control of the movement speed of the robot.
Through the arrangement of the controller and the application of the method, group robots can be quickly grouped and form a trapping formation according to different targets and local information, and the first robot and the second robot used for trapping can smoothly move without collision, excessive aggregation and other conditions affecting trapping movement, so that the target trapping precision is improved. In addition, the whole movement process of each robot is controlled by itself, no human participation is needed, the flexibility is strong, the application range is wide, the full communication environment is not excessively relied on, and the target trapping action can be well implemented even under the condition of limited environment.
As shown in fig. 3, another embodiment of the present invention also provides a multi-target trapping device 100 for a group robot, including:
the first determining module is used for finding and determining a target to be captured;
a first creating module, configured to create a local coordinate system with a first robot that finds the target as a center point, and simultaneously with positions of neighboring robots of the first robot;
a second determining module for determining a position of the target in the local coordinate system;
the third determining module is used for determining a second robot participating in capturing the target at least according to the local coordinate system, the position of the neighbor robot in the local coordinate system and the position of the target in the local coordinate system;
the second creation module is used for cooperatively creating a trapping formation mode corresponding to the second robots according to the local coordinate system by simulating activities between genes and proteins, and the trapping formation mode is used for guiding each second robot to move so as to trap the target.
As an alternative embodiment, the creating a local coordinate system with the first robot that finds the target as a center point and combining the positions of the neighboring robots of the first robot includes:
determining an X axis according to the position of a first neighbor robot closest to the first robot;
determining a Y axis according to the positions of the second neighbor robots which are respectively equal to the first neighbor robots and the distances between the first robots;
and creating the local coordinate system according to the central point, the X axis and the Y axis.
As an alternative embodiment, further comprising:
the first robots and the neighbor robots can communicate with each other to determine the positions of the robots and the targets;
the determining a second robot participating in trapping the object based at least on the local coordinate system, a position of a neighbor robot in the local coordinate system, a position of the object in the local coordinate system, comprising:
determining the positions of the neighbor robots and the targets in the local coordinate system based on the position calculation of each robot and each target;
and determining that the neighbor robot meeting the position condition is the second robot at least based on the positions of the neighbor robot and the target in the local coordinate system.
As an alternative embodiment, the creating a capture queuing mode corresponding to the capture group by simulating activity between genes and proteins includes:
generating a desired protein concentration to create a capture formation pattern corresponding to the capture population based on the following power equation:
wherein, gamma j Is a scalar value, representing the environmental input of the equation,for Laplacian, p j Representing the direct observation by the first robot and the indirect receipt from the neighboring robotThe comprehensive protein concentration of the position of the target g 1 ，g 2 And g 3 Expressed as protein concentration, g 3 From g 1 And g 2 Co-regulation of ρ, θ 1 ，θ 2 ，θ 3 Is a positive constant.
As an alternative embodiment, further comprising:
the setting module is used for setting a controller in each robot so as to correspondingly control the movement of the first robot and the second robot in the capture formation mode based on the controller.
As an optional embodiment, the controller may comprehensively determine motion parameters corresponding to the first robot and the second robot when capturing the target according to the number of other targets in the preset radius of the target, the safety distance between the robots in the capturing group, the robot density of the positions of the robots in the capturing group, and the obstacle in the moving range of the robots, where the motion parameters satisfy that no abnormal state occurs in the motion process of the first robot and the second robot, where the abnormal state at least includes collision, excessive aggregation, and coordinate drift.
As an optional embodiment, the motion parameters include calculating, based on the controller correspondence, motion speeds of the first robot and the second robot in different scenarios, where the different scenarios include avoidance of other targets, robots, obstacles, and reduction of destructive aggregation of robots.
As an alternative embodiment, the controller controls the robot to move based on vector sum of the movement speeds of the same robot in different scenes so as to achieve trapping of the target.
Another embodiment of the present invention also provides an electronic device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to implement a group robot-trapped multi-target method under local information as described in any of the embodiments above.
Further, an embodiment of the present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a group robot trapping multi-target method under local information as described above. It should be understood that each solution in this embodiment has a corresponding technical effect in the foregoing method embodiment, which is not described herein.
Further, embodiments of the present invention also provide a computer program product tangibly stored on a computer-readable medium and comprising computer-readable instructions that, when executed, cause at least one processor to perform a group robot-trapping multi-target method under local information, such as in the embodiments described above.
The computer storage medium of the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage media element, a magnetic storage media element, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Additionally, it should be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this invention will occur to those skilled in the art, and are intended to be within the spirit and scope of the invention.
Claims (10)
1. The method for capturing multiple targets by the group robot under the local information is characterized by comprising the following steps of:
discovering and determining targets to be captured;
taking a first robot which finds the target as a center point, and simultaneously creating a local coordinate system by combining the positions of neighbor robots of the first robot;
determining a position of the target in the local coordinate system;
determining a second robot participating in trapping the target based on at least the local coordinate system, the position of the neighbor robot in the local coordinate system, and the position of the target in the local coordinate system, wherein the first robot and the second robot form a trapping group;
and on the basis of the local coordinate system, establishing a trapping formation mode corresponding to the trapping group in a matching way by simulating activities between genes and proteins, wherein the trapping formation mode is used for guiding the first robot and the second robot to move so as to trap the target.
2. The method for capturing multiple targets by a group robot under local information according to claim 1, wherein the creating a local coordinate system with a first robot finding the targets as a center point while combining positions of neighbor robots of the first robot comprises:
determining an X axis according to the position of a first neighbor robot closest to the first robot;
determining a Y axis according to the positions of the second neighbor robots which are respectively equal to the first neighbor robots and the distances between the first robots;
and creating the local coordinate system according to the central point, the X axis and the Y axis.
3. The method for capturing multiple targets by a group robot under local information according to claim 1, further comprising:
the first robots and the neighbor robots can communicate with each other to determine the positions of the robots and the targets;
the determining a second robot participating in trapping the object based at least on the local coordinate system, a position of a neighbor robot in the local coordinate system, a position of the object in the local coordinate system, comprising:
determining the positions of the neighbor robots and the targets in the local coordinate system based on the position calculation of each robot and each target;
and determining that the neighbor robot meeting the position condition is the second robot at least based on the positions of the neighbor robot and the target in the local coordinate system.
4. The method for capturing multiple targets by group robots under local information according to claim 1, wherein said creating a capture formation pattern corresponding to said capture group by modeling activities between genes and proteins comprises:
generating a desired protein concentration to create a capture formation pattern corresponding to the capture population based on the following power equation:
wherein, gamma j Is a scalar value representing the environmental input of the equation, v 2 For Laplacian, p j Representing the integrated protein concentration, g, directly observed by the first robot and indirectly received from a neighboring robot with respect to the location of the target 1 ，g 2 And g 3 Expressed as protein concentration, g 3 From g 1 And g 2 Co-regulation of ρ, θ 1 ，θ 2 ，θ 3 Is a positive constant.
5. The method for capturing multiple targets by a group robot under local information according to claim 1, further comprising:
and setting a controller in each robot to correspondingly control the movement of the first robot and the second robot in the capture formation mode based on the controller.
6. The method for capturing multiple targets by group robots under local information according to claim 5, wherein the controller is capable of comprehensively determining motion parameters of the first robot and the second robot when capturing the targets according to the number of other targets in a preset radius of the target, the safety distance between the robots in the capturing group, the robot density of the positions of the robots in the capturing group and the obstacles in the moving range of the robots, wherein the motion parameters meet the condition that no abnormal state of the first robot and the second robot occurs in the motion process, and the abnormal state at least comprises collision, excessive aggregation and coordinate drift.
7. The method of multi-objective crowd robot capture under local information of claim 6, wherein the motion parameters include computing motion speeds of the first and second robots in different scenarios based on the controller correspondence, the different scenarios including avoidance of other objects, robots, obstacles, and reduction of destructive aggregation of robots.
8. The method for capturing multiple targets by a group robot under local information according to claim 7, wherein the controller controls the robot to move based on vector sum of moving speeds of the same robot under different scenes to achieve capturing of the targets.
9. A multi-target trapping device for a population robot, comprising:
the first determining module is used for finding and determining a target to be captured;
a first creating module, configured to create a local coordinate system with a first robot that finds the target as a center point, and simultaneously with positions of neighboring robots of the first robot;
a second determining module for determining a position of the target in the local coordinate system;
the third determining module is used for determining a second robot participating in capturing the target at least according to the local coordinate system, the position of the neighbor robot in the local coordinate system and the position of the target in the local coordinate system;
the second creation module is used for cooperatively creating a trapping formation mode corresponding to the second robots according to the local coordinate system by simulating activities between genes and proteins, and the trapping formation mode is used for guiding each second robot to move so as to trap the target.
10. An electronic device, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to implement the group robot-trapped multi-target method under local information as claimed in any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310651389.5A CN116520852A (en) | 2023-06-02 | 2023-06-02 | Method, device and equipment for capturing multiple targets by group robots under local information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310651389.5A CN116520852A (en) | 2023-06-02 | 2023-06-02 | Method, device and equipment for capturing multiple targets by group robots under local information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116520852A true CN116520852A (en) | 2023-08-01 |
Family
ID=87403068
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310651389.5A Pending CN116520852A (en) | 2023-06-02 | 2023-06-02 | Method, device and equipment for capturing multiple targets by group robots under local information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116520852A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117148730A (en) * | 2023-11-01 | 2023-12-01 | 北京航空航天大学 | Time-varying grouping formation tracking control method, system and electronic equipment |
-
2023
- 2023-06-02 CN CN202310651389.5A patent/CN116520852A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117148730A (en) * | 2023-11-01 | 2023-12-01 | 北京航空航天大学 | Time-varying grouping formation tracking control method, system and electronic equipment |
| CN117148730B (en) * | 2023-11-01 | 2024-01-16 | 北京航空航天大学 | Time-varying grouping formation tracking control method, system and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dang et al. | Visual saliency-aware receding horizon autonomous exploration with application to aerial robotics | |
| Lin et al. | A Novel Improved Bat Algorithm in UAV Path Planning. | |
| Saeed et al. | A boundary node method for path planning of mobile robots | |
| Jain et al. | MVO-based path planning scheme with coordination of UAVs in 3-D environment | |
| CN113172626B (en) | Intelligent robot group control method based on three-dimensional gene regulation and control network | |
| Petrlík et al. | Coverage optimization in the cooperative surveillance task using multiple micro aerial vehicles | |
| CN116520852A (en) | Method, device and equipment for capturing multiple targets by group robots under local information | |
| Geng et al. | UAV surveillance mission planning with gimbaled sensors | |
| CN113359859B (en) | Combined navigation obstacle avoidance method, system, terminal equipment and storage medium | |
| Shoushtary et al. | Team robot motion planning in dynamics environments using a new hybrid algorithm (honey bee mating optimization-tabu list) | |
| CN114154383A (en) | Multi-robot-source search scheme generation method and system based on cognitive search strategy | |
| Shukla et al. | Multi robot path planning parameter analysis based on particle swarm optimization (PSO) in an intricate unknown environments | |
| Shvets | Stochastic Multi-Agent Patrolling Using Social Potential Fields. | |
| Hanay et al. | Distributed sensor deployment using potential fields | |
| Tuba et al. | Water cycle algorithm for robot path planning | |
| Abiyev et al. | Improved path-finding algorithm for robot soccers | |
| Farid et al. | Search and tracking in 3D space using a species based particle swarm optimizer | |
| Rashid et al. | Polygon Shape Formation for Multi-Mobile Robots in a Global Knowledge Environment. | |
| Wanasinghe et al. | A Jacobian free approach for multi-robot relative localization | |
| Mishra et al. | A distributed epigenetic shape formation and regeneration algorithm for a swarm of robots | |
| Jung et al. | Visual cooperation based on LOS for self-organization of swarm robots | |
| CN117762150B (en) | Multi-agent distributed area coverage control method, agent and program product | |
| Rajan et al. | Optimizing multiagent area coverage using dynamic global potential fields | |
| Cayirpunar et al. | Experimental study on the effects of communication range on cooperative robotic search in complex environments | |
| Babovic et al. | Sliding Holt algorithm implementation in mobile robots collision detection with dynamic obstacles based on computer vision technologies |
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 |