A method and related equipment for controlling passage through narrow passages

Abstract

This application provides a method and related equipment for controlling narrow passage passage, relating to the field of robot control technology. This application addresses the movement of each individual robot in a swarm robot system towards a narrow passage using its initial guiding speed. Through distributed motion environment perception information analysis, it ensures obstacle avoidance and swarm characteristics among the individual robots. Furthermore, through distributed virtual potential field constraint analysis, it ensures that the individual robots spontaneously converge within the narrow passage during the passage. This allows the individual robots in the swarm robot system to spontaneously maintain swarm characteristics, achieving collision-free safe sequencing and smoothly traversing the narrow passage. This realizes the distributed motion environment perception and distributed motion control functions of the swarm robot system, without relying on wireless communication technology and a central node, facilitating the development and application of large-scale swarm robot technology.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and more specifically, to a method and related equipment for controlling passage through narrow passages. Background Technology

[0002] With the continuous development of science and technology, robotics is being applied more and more widely across various industries. Among these applications, swarm robotics is an important research direction in robotics today. This technology typically requires multiple mobile robots to safely and orderly navigate narrow passages (e.g., windows, door frames, pipes) in chaotic environments to collaboratively perform desired tasks at a designated destination (e.g., formation rescue missions, swarm search missions). During the process of swarm robots navigating narrow passages, it is crucial to ensure that each mobile robot avoids collisions with obstacles in the environment, and also to prevent collisions between multiple mobile robots.

[0003] Currently, robot control schemes for swarm robots navigating narrow passages primarily rely on wireless communication technology and the scheduling and coordination of a central node. Typically, the central node needs to pre-plan collision-free trajectories for all mobile robots and, based on the actual motion data shared by each robot via wireless communication, dynamically adjust the planned trajectories for each robot, taking into account robot sequencing issues, to ensure the safe and orderly passage of multiple mobile robots through the narrow passage. However, it is worth noting that this robot control scheme places a significant burden on the central node's computing resources and requires the entire swarm robot system to have sufficient and stable communication resources. It is highly susceptible to system crashes due to excessive computational resource consumption at the central node or excessive wireless communication pressure, making it impossible to guarantee that all mobile robots in the swarm robot system can successfully pass through the narrow passage. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a narrow passage crossing control method, a mobile robot, and a readable storage medium, which can realize distributed motion environment perception and distributed motion control functions for swarm robot systems. This enables individual robots in the swarm robot system to spontaneously maintain swarm characteristics, achieve collision-free safe sorting, and smoothly cross narrow passages without relying on wireless communication technology and a central node, so as to further promote the development and application of large-scale swarm robot technology.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, this application provides a narrow passage crossing control method, applied to each individual robot in a swarm robot system, the method comprising:

[0007] The system obtains the actual position information of the single robot under the virtual channel potential field of the narrow channel to be traversed, as well as the current surrounding obstacle detection data of the single robot.

[0008] Based on the surrounding obstacle detection data, robot cluster interaction analysis is performed to obtain the body interaction drive speed of the individual robot that currently meets the robot obstacle avoidance requirements and robot cluster requirements.

[0009] Based on the actual body position information, the potential field constraint effect analysis is performed to obtain the current convergence and traversal driving speed of the single robot under the action of the potential field of the virtual channel.

[0010] The original guided crossing speed, the body interaction driving speed, and the convergence crossing driving speed of the single robot are superimposed to obtain the expected crossing speed of the single robot in the narrow passage to be crossed.

[0011] The single robot is driven to move at the desired traversal speed.

[0012] In an optional implementation, the surrounding obstacle detection data includes the actual distances between the individual robot and surrounding obstacles in multiple discrete detection directions. The step of performing robot swarm interaction analysis based on the surrounding obstacle detection data to obtain the body interaction drive speed of the individual robot that currently meets the robot obstacle avoidance and robot swarm requirements includes:

[0013] Based on the preset attraction-repulsion balance parameters and attraction-repulsion separation distance that match the robot obstacle avoidance requirements and the robot cluster requirements, the body interaction rate is predicted based on the actual distance of the individual robot in multiple discrete detection directions, and the corresponding target body interaction movement rate is obtained.

[0014] Based on the attraction-repulsion balance parameters and the attraction-repulsion separation distance, the interaction direction of the robot body is predicted based on the actual distance of the single robot in multiple discrete detection directions, and the corresponding target robot body interaction movement direction is obtained.

[0015] The target robot's interactive movement rate and direction are comprehensively characterized to obtain the current interactive driving speed of the single robot.

[0016] In an optional implementation, the target body interaction movement rate of the i-th individual robot in the swarm robot system at time t is predicted using the following formula:

[0017]

[0018] Among them, v Dete,i,t N is used to represent the target body interaction movement speed of the i-th individual robot at time t. i The number of discrete detection directions for the i-th individual robot is used to represent the total number of directions, α is used to represent the rate control coefficient, and φ is used to represent the rate control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i D(φ) is used to represent the detection angle interval between two adjacent discrete detection directions of the i-th individual robot. i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

[0019] In an optional implementation, the target body interaction movement direction of the i-th individual robot in the swarm robot system at time t is predicted using the following formula:

[0020]

[0021] in, N is used to represent the target body interaction movement direction of the i-th individual robot at time t. i β represents the total number of discrete detection directions for the i-th individual robot, β represents the direction control coefficient, and φ represents the direction control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i D(φ) is used to represent the detection angle interval between two adjacent discrete detection directions of the i-th individual robot. i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

[0022] In an optional implementation, the body interaction driving speed of the i-th individual robot in the swarm robot system at time t is calculated using the following formula:

[0023]

[0024] Among them, V Dete,i,t v is used to represent the body interaction driving speed of the i-th individual robot at time t. Dete,i,t This is used to represent the target body interaction movement speed of the i-th individual robot at time t. This is used to represent the direction of the target body interaction movement of the i-th individual robot at time t.

[0025] In an optional implementation, the step of performing potential field constraint analysis based on the actual body position information to obtain the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field includes:

[0026] Based on the actual body position information, calculate the actual shortest distance between the single robot and the edge of the potential field of the virtual channel potential field;

[0027] The target potential field function of the virtual channel potential field is invoked, and the potential field gradient is solved based on the actual shortest distance corresponding to the current single robot to obtain the position gradient of the single robot under the action of the virtual channel potential field.

[0028] Based on the preset speed control ratio coefficient and the position gradient, the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field is calculated.

[0029] In an optional implementation, the target potential field function is represented by the following equation:

[0030]

[0031] Where, d i,t U(d) represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. i,t The potential field strength is represented by σ at time t, where C represents the potential field strength coefficient, σ represents the potential field distribution attenuation parameter, and d represents the potential field strength coefficient. s d represents the safe distance that any single robot needs to maintain between itself and the inner wall of the narrow passage to be traversed. a This is used to represent the effective repulsive distance of the virtual channel potential field.

[0032] In an optional implementation, the convergence and traversal driving speed of the i-th individual robot in the swarm robot system at time t is calculated using the following formula:

[0033]

[0034] Among them, V Tube,i,t The velocity d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t, k is used to represent the speed control proportional coefficient, and d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t. i,t This represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. U(d) represents the position gradient of the i-th individual robot under the potential field of the virtual channel at time t. i,t ) is used to represent the magnitude of the potential field strength experienced by the i-th individual robot under the potential field of the virtual channel at time t.

[0035] Secondly, this application provides a mobile robot, including a processor, a memory, and a mobile component. The memory stores a computer program that can be executed by the processor. The processor can execute the computer program to invoke the mobile component to implement the narrow passage crossing control method described in any of the foregoing embodiments, wherein multiple mobile robots are combined to form a cluster robot system.

[0036] Thirdly, this application provides a readable storage medium storing a computer program thereon, which, when executed by each individual robot included in the swarm robot system, implements the narrow passage crossing control method described in any of the foregoing embodiments.

[0037] In this case, the beneficial effects of the embodiments of this application may include the following:

[0038] This application can acquire, for each individual robot in a swarm robot system, the actual position information of the individual robot under the virtual channel potential field of the narrow passage to be traversed, and the current surrounding obstacle detection data of the individual robot. Based on the surrounding obstacle detection data, it performs robot swarm interaction analysis to obtain the current body interaction driving speed of the individual robot that meets the robot obstacle avoidance and robot swarm requirements. Simultaneously, based on the actual body position information, it performs potential field constraint analysis to obtain the current convergence traversal driving speed of the individual robot under the influence of the virtual channel potential field. Then, it superimposes the original guided traversal speed, body interaction driving speed, and convergence traversal driving speed of the individual robot to drive the narrow passage. The robot moves by using its original guiding speed to propel individual robots through narrow passages. Distributed motion environment perception information analysis (i.e., robot cluster interaction analysis of individual robots) ensures obstacle avoidance and cluster characteristics among the individual robots. Distributed virtual potential field constraint analysis (i.e., potential field constraint analysis of individual robots) ensures that the individual robots spontaneously gather inside the narrow passage during the crossing. This allows the individual robots in the cluster robot system to spontaneously maintain cluster characteristics, achieve collision-free safe sequencing, and smoothly cross narrow passages without relying on wireless communication technology and a central node, facilitating the further development and application of large-scale cluster robot technology.

[0039] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A schematic diagram illustrating the composition of the mobile robot provided in an embodiment of this application;

[0042] Figure 2 A flowchart illustrating the narrow passage crossing control method provided in this application embodiment;

[0043] Figure 3 A schematic diagram illustrating the distribution of surrounding obstacle detection data for a single robot provided in an embodiment of this application;

[0044] Figure 4 A three-dimensional spatial schematic diagram of the potential field of a virtual channel to be traversed in an embodiment of this application;

[0045] Figure 5 for Figure 2 A flowchart illustrating the sub-steps included in step S220;

[0046] Figure 6 for Figure 4 A schematic diagram of the axial cross-section of the virtual channel potential field;

[0047] Figure 7 for Figure 2 The flowchart of the sub-steps included in step S230 is shown below.

[0048] Icons: 10-Mobile robot; 11-Memory; 12-Processor; 13-Communication unit; 14-Environmental detection unit; 15-Mobile component. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0051] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0052] In the description of this application, it should be understood that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are used only for the convenience of describing this application and simplifying the description, and are not intended to indicate or imply that the equipment or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0053] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0054] Furthermore, it is understood in the description of this application that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Those skilled in the art will understand the specific meaning of the above terms in this application based on the specific circumstances.

[0055] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0056] Please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating the composition of a mobile robot 10 provided in an embodiment of this application. In this embodiment, the mobile robot 10 can be used to construct a swarm robot system and act as a single robot within that system. The mobile robot 10 may include a memory 11, a processor 12, a communication unit 13, an environment detection unit 14, and a movement component 15. The memory 11, processor 12, communication unit 13, environment detection unit 14, and movement component 15 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected via one or more communication buses or signal lines.

[0057] In this embodiment, the memory 11 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 11 is used to store computer programs, and the processor 12 can execute the computer programs accordingly after receiving execution instructions.

[0058] In this embodiment, the processor 12 can be an integrated circuit chip with signal processing capabilities. The processor 12 can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in this embodiment.

[0059] In this embodiment, the communication unit 13 is used to establish a communication connection between the mobile robot 10 and other electronic devices via a network, and to send and receive data via the network, wherein the network includes wired communication networks and wireless communication networks. For example, the mobile robot 10 can obtain, through the communication unit 13, channel parameter information of the narrow passage to be traversed from the robot remote control terminal, as instructed by the user for the cluster robot system to which the mobile robot 10 belongs. The channel parameter information may include the channel location information, channel length information, channel cross-sectional dimension information, etc., of the narrow passage to be traversed.

[0060] In this embodiment, the environment detection unit 14 can detect and perceive the motion environment around the mobile robot 10 during its movement to obtain the environmental conditions around the mobile robot 10, and effectively determine the actual position information of the mobile robot 10 based on conventional positioning technologies (e.g., triangulation, image recognition, etc.). The environment detection unit 14 may include, but is not limited to, lidar, ultrasonic detectors, infrared detectors, RGB-D cameras, etc. In one implementation of this embodiment, to effectively reduce the implementation cost of the environment detection unit 14 and minimize environmental interference, an RGB-D camera can be directly used to ensure that the mobile robot 10 has independent motion environment perception capabilities.

[0061] In this embodiment, the moving component 15 can be used to realize the movement function of the mobile robot 10. The moving component 15 may include, but is not limited to, wheels, motors, tracks, etc.

[0062] In this embodiment, the mobile robot 10 may pre-store a specific computer program related to the narrow passage crossing control function in the memory 11. By driving the processor 12 to execute the specific computer program, it is ensured that when the mobile robot 10 is a single robot in a swarm robot system that needs to cross a narrow passage, it can spontaneously cooperate with other single robots to maintain swarm characteristics, achieve collision-free safe sorting, and smoothly cross the narrow passage. This eliminates the need to rely on wireless communication technology and a central node to achieve the effect of swarm crossing the narrow passage. This enables the corresponding swarm robot system to achieve distributed motion environment perception and distributed motion control functions during narrow passage crossing, thereby further promoting the development and application of large-scale swarm robot technology.

[0063] Understandable Figure 1 The block diagram shown is only a schematic diagram of one possible composition of the mobile robot 10. The mobile robot 10 may also include components such as... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0064] In this application, to ensure that the mobile robot 10, as a single robot in a swarm robot system, can spontaneously cooperate with other single robots (i.e., other mobile robots 10) to maintain swarm characteristics, achieve collision-free safe sequencing, and smoothly traverse a narrow passage, enabling the corresponding swarm robot system to achieve distributed motion environment perception and distributed motion control functions during narrow passage traversal without relying on wireless communication technology and a central node to achieve the swarm traversal effect, this application provides a narrow passage traversal control method to achieve the aforementioned objective. The narrow passage traversal control method provided in this application will be described in detail below.

[0065] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the narrow passage crossing control method provided in an embodiment of this application. In this embodiment, the narrow passage crossing control method is applied to each individual robot in a swarm robot system, and the individual robot can employ... Figure 1 The mobile robot 10 shown is implemented, wherein the narrow passage crossing control method may include steps S210 to S250.

[0066] Step S210: Obtain the actual position information of the single robot under the virtual channel potential field of the narrow channel to be traversed, as well as the surrounding obstacle detection data of the single robot.

[0067] In this embodiment, for each individual robot in the swarm robot system, the individual robot can use the aforementioned environmental detection unit 14 to detect its surrounding environment in real time, and locate its actual position information in the virtual channel potential field of the narrow passage to be traversed based on the detected surrounding environment. The surrounding obstacle detection data can include the actual distances between the corresponding individual robot and surrounding obstacles (including static obstacles, dynamic obstacles, and other individual robots within the robot's motion environment) in multiple discrete detection directions. These multiple discrete detection directions are combined to achieve omnidirectional environmental detection for the corresponding individual robot.

[0068] In this process, Figure 3 For example, the individual robots in the same robot swarm system can be simplified as follows: Figure 3 Small, round individuals within a single robot, and when a single robot uses an RGB-D camera to construct an environment detection unit 14, that single robot (i.e., in a position...) Figure 3 The multiple discrete detection directions involved in the obstruction detection data around the small pink circular individual at the center correspond to a discrete ray of visual observation (i.e., Figure 3 (Any black straight line segment in the diagram), at this time the detection angle range of the surrounding environment of the single robot is [-π, π]. The actual distance between the single robot and surrounding obstacles in any discrete detection direction can be expressed as... Figure 3 The length of the line segment corresponding to the black straight line segment is represented in the text; wherein, in the robot swarm robot system, the other individual robots besides the individual robot are the other small circular individuals surrounding the pink small circular individual; if there are no obstacles in a certain discrete detection direction within the environmental detection range of any individual robot, the actual distance of the individual robot in that discrete detection direction can be represented by the maximum detection distance of the individual robot.

[0069] by Figure 4For example, the narrow passage to be traversed can be simplified to a narrow square frame that does not support the simultaneous passage of a cluster of robots. In this case, a virtual channel potential field adapted to the narrow passage can be constructed based on the channel parameter information. This virtual channel potential field can be divided into a straight-tube potential field and a trumpet-shaped potential field. One end of the straight-tube potential field is located behind the simplified square frame of the narrow passage, and the other end is located in front of the simplified square frame. The straight-tube potential field penetrates the simplified square frame of the narrow passage. The small-diameter potential field end of the trumpet-shaped potential field is connected to the potential field end of the straight-tube potential field away from the simplified square frame. The large-diameter potential field end of the trumpet-shaped potential field is open to the cluster of robots, allowing each individual robot in the cluster to sequentially pass through the large-diameter potential field end, the small-diameter potential field end, and the straight-tube potential field to traverse the narrow passage. Figure 4 The solid red dots in the diagram can be used to characterize the intersection of the central axis of the virtual channel potential field and the end of the large-diameter potential field.

[0070] Step S220: Based on the surrounding obstacle detection data, perform robot cluster interaction analysis to obtain the current body interaction drive speed of a single robot that meets the robot obstacle avoidance requirements and robot cluster requirements.

[0071] In this embodiment, the robot cluster interaction analysis operation of each individual robot in the cluster robot system can be used to characterize the distributed motion environment perception information analysis function of the cluster robot system, ensuring that individual robots belonging to the same cluster robot system can achieve mutual obstacle avoidance and cluster structure stability during movement. Therefore, the body interaction drive speed of a single individual robot can effectively guarantee the obstacle avoidance effect and cluster characteristics between the corresponding individual robot and other individual robots.

[0072] Alternatively, please refer to Figure 5 , Figure 5 yes Figure 2 The flowchart of step S220 is shown below. In this embodiment, step S220 may include sub-steps S221 to S223 to realize the distributed motion environment perception information analysis function of the swarm robot system, ensure that the analyzed body interaction drive speed can avoid collisions between the corresponding individual robots and other individual robots during narrow passage crossing, effectively maintain the robot swarm characteristics of the swarm robot system, and prevent the swarm robot system from collapsing.

[0073] Sub-step S221: Based on the preset attraction-repulsion balance parameters and attraction-repulsion separation distance that match the robot obstacle avoidance requirements and robot cluster requirements, predict the body interaction rate based on the actual distance of the individual robot in multiple discrete detection directions, and obtain the corresponding target body interaction movement rate.

[0074] In this embodiment, the attraction-repulsion separation distance is used to characterize the equilibrium distance between the robot's obstacle avoidance requirements and the robot swarm requirements. The attraction-repulsion separation distance can be adopted as follows: Figure 3 The radius of the large pink circular region is used to represent the area. Any obstacle (including a single robot) within this large pink circular region will affect the robot's position. Figure 3 The single robot at the center generates a repulsive force to ensure that the robot's obstacle avoidance requirements are met, while any surrounding obstacles (including the single robot) outside the large pink circular area will affect the robot's ability to avoid obstacles. Figure 3 The individual robot at the center generates an attractive force to ensure that the needs of the robot swarm are met. The attraction-repulsion balance parameter is used to adjust the attraction-repulsion balance between the corresponding individual robot and surrounding obstacles.

[0075] For the i-th individual robot in the aforementioned swarm robot system, the target body interaction movement rate of this individual robot at time t can be predicted using the following formula:

[0076]

[0077] Among them, v Dete,i,t N is used to represent the target body interaction movement speed of the i-th individual robot at time t. i The number of discrete detection directions for the i-th individual robot is used to represent the total number of directions, α is used to represent the rate control coefficient, and φ is used to represent the rate control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i D(φ) is used to represent the detection angle interval between two adjacent discrete detection directions of the i-th individual robot. i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

[0078] Sub-step S222: Based on the attraction-repulsion balance parameters and the attraction-repulsion separation distance, predict the interaction direction of the robot body based on the actual distance of the single robot in multiple discrete detection directions, and obtain the corresponding target robot body interaction movement direction.

[0079] In this embodiment, for the i-th individual robot in the swarm robot system, the target body interaction movement direction of the individual robot at time t is predicted using the following formula:

[0080]

[0081] in, N is used to represent the target body interaction movement direction of the i-th individual robot at time t. i β represents the total number of discrete detection directions for the i-th individual robot, β represents the direction control coefficient, and φ represents the direction control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i This is used to represent the detection angle interval (e.g., 2π / N) between two adjacent discrete detection directions of the i-th individual robot. i ), D(φ i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

[0082] Sub-step S223: Perform a comprehensive velocity characterization of the target robot's interactive movement rate and direction to obtain the current interactive driving speed of the single robot.

[0083] In this embodiment, for the i-th individual robot in the swarm robot system, the body interaction driving speed of the individual robot at time t can be calculated using the following formula:

[0084]

[0085] Among them, V Dete,i,t v is used to represent the body interaction driving speed of the i-th individual robot at time t. Dete,i,t This is used to represent the target body interaction movement speed of the i-th individual robot at time t. This is used to represent the direction of the target body interaction movement of the i-th individual robot at time t.

[0086] Therefore, by executing the above sub-steps S221 to S223, this application can realize the distributed motion environment perception information analysis function of the cluster robot system, ensure that the analyzed body interaction drive speed can avoid collisions between the corresponding individual robots and other individual robots during the passage through narrow channels, effectively maintain the robot cluster characteristics of the cluster robot system, and prevent the cluster robot system from collapsing.

[0087] Step S230: Based on the actual body position information, perform potential field constraint analysis to obtain the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field.

[0088] In this embodiment, the potential field constraint analysis operation of each individual robot in the swarm robot system under the virtual channel potential field of the narrow channel to be traversed can be used to characterize the distributed virtual potential field constraint analysis function of the swarm robot system. This ensures that individual robots belonging to the same swarm robot system spontaneously converge towards the inside of the narrow channel during movement. This, in conjunction with the aforementioned robot swarm interaction analysis operation, allows the individual robots in the swarm robot system to spontaneously maintain swarm characteristics and achieve collision-free safe sequencing during narrow channel traversal, ensuring that all individual robots in the swarm robot system can traverse the narrow channel normally. Therefore, the convergence driving speed of a single individual robot can effectively ensure that the corresponding individual robot spontaneously converges towards the inside of the narrow channel during movement.

[0089] Optionally, please refer to the reference adjustment. Figure 6 and Figure 7 ,in Figure 6 yes Figure 4 A schematic diagram of the axial cross-section of the virtual channel potential field. Figure 7 yes Figure 2 A flowchart illustrating the sub-steps included in step S230 is provided. In this embodiment, the total potential field length of the virtual channel potential field to be traversed in the narrow channel can be adopted as... Figure 6 In the representation of l2, the potential field length of the straight tubular potential field in the virtual channel potential field can be expressed as l2. Figure 6 In the figure, l1 is used to represent the potential field radius of the straight tubular potential field, which can be expressed as l1. Figure 6 In the text, r1 is used to represent the potential field radius of the funnel-shaped potential field in the virtual channel potential field, which can be expressed as r1. Figure 6 The value is represented by r1+r2, where the greater the length of the narrow channel to be traversed, the greater the total length of the potential field of the virtual channel. Figure 6 The blue solid line in the diagram represents the central axis of the virtual channel potential field, d. a d represents the effective repulsive distance of the virtual channel potential field. s This represents the safe distance that any single robot needs to maintain between itself and the inner wall of the narrow passage to be traversed. In this case, step S230 may include sub-steps S231 to S233 to implement the distributed virtual potential field constraint analysis function of the swarm robot system, ensuring that the analyzed convergence traversal driving speed can drive the corresponding single robot to spontaneously converge towards the inside of the narrow passage during the traversal process.

[0090] Sub-step S231: Calculate the actual shortest distance between the current position of the single robot and the edge of the potential field of the virtual channel, based on the actual position information of the robot.

[0091] Sub-step S232 calls the target potential field function of the virtual channel potential field, and solves the potential field gradient based on the actual shortest distance corresponding to the current single robot, so as to obtain the position gradient of the single robot under the action of the virtual channel potential field.

[0092] In this embodiment, the target potential field function of the virtual channel potential field can be represented by the following equation:

[0093]

[0094] Where, d i,t U(d) represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. i,t The potential field strength is represented by σ at time t, where C represents the potential field strength coefficient, σ represents the potential field distribution attenuation parameter, and d represents the potential field strength coefficient. s d represents the safe distance that any single robot needs to maintain between itself and the inner wall of the narrow passage to be traversed. a This is used to represent the effective repulsive distance of the virtual channel potential field. Specifically, when the shortest distance from a single robot to the edge of the virtual channel potential field is less than the safety distance or the effective repulsive distance, the potential field strength exerted by the virtual channel potential field on that single robot will repel it into the narrow channel to be traversed, thus causing the single robot to spontaneously enter the narrow channel.

[0095] Sub-step S233: Calculate the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field based on the preset speed control ratio coefficient and position gradient.

[0096] In this embodiment, for the i-th individual robot in the swarm robot system, the convergence and traversal driving speed of the individual robot at time t is calculated using the following formula:

[0097]

[0098] Among them, V Tube,i,t The velocity d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t, k is used to represent the speed control proportional coefficient, and d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t. i,t This represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. U(d) represents the position gradient of the i-th individual robot under the potential field of the virtual channel at time t. i,t ) is used to represent the magnitude of the potential field strength experienced by the i-th individual robot under the potential field of the virtual channel at time t.

[0099] Therefore, by executing the above sub-steps S231 to S233, this application can realize the distributed virtual potential field constraint analysis function of the cluster robot system, ensuring that the analyzed convergence and crossing driving speed can drive the corresponding individual robots to spontaneously converge into the narrow channel during the narrow channel crossing process.

[0100] Step S240: The original guiding speed, body interaction driving speed and convergence driving speed of the single robot are superimposed to obtain the expected crossing speed of the single robot in the narrow passage to be crossed.

[0101] In this embodiment, the initial guided crossing speed of a single robot for the narrow passage to be traversed is used to drive the single robot to move towards the narrow passage. The initial guided crossing speed can be configured by the robot's remote control terminal. By superimposing the initial guided crossing speed, body interaction driving speed, and convergence crossing driving speed of the same single robot at the same time, it can be ensured that the corresponding desired crossing movement speed can drive the single robot to spontaneously move towards the inside of the narrow passage to be traversed during the movement towards the narrow passage, and maintain a collision-free effect and stable cluster structure with other single robots. In this way, the individual robots in the cluster robot system can spontaneously maintain the cluster characteristics to achieve a collision-free safe order and smoothly cross the narrow passage, without relying on wireless communication technology and a central node to achieve the effect of group crossing of narrow passages. This facilitates the corresponding cluster robot system to realize distributed motion environment perception and distributed motion control functions during the narrow passage crossing process.

[0102] Step S250: Drive the single robot to move according to the desired crossing speed.

[0103] Therefore, by executing the above steps S210 to S250, this application, for each individual robot in the swarm robot system, during the process of moving and crossing the narrow passage using the original guiding crossing speed, ensures the obstacle avoidance effect and swarm characteristics between different individual robots through distributed motion environment perception information analysis (i.e., robot swarm interaction analysis operation of each individual robot), and ensures that each individual robot spontaneously gathers inside the narrow passage during the crossing through distributed virtual potential field constraint analysis (i.e., potential field constraint effect analysis operation of each individual robot), so that each individual robot in the swarm robot system can spontaneously maintain swarm characteristics to achieve collision-free safe sorting and smoothly cross the narrow passage, without relying on wireless communication technology and a central node, which facilitates the further development and application of large-scale swarm robot technology.

[0104] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0105] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the various functions provided in this application are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause any single robot in the swarm robot system (i.e., the aforementioned mobile robot 10) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned readable storage medium includes: USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0106] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling passage through narrow channels, characterized in that, The method, applied to each individual robot in a swarm robot system, includes: The system obtains the actual position information of the single robot under the virtual channel potential field of the narrow channel to be traversed, as well as the current surrounding obstacle detection data of the single robot. Based on the surrounding obstacle detection data, robot cluster interaction analysis is performed to obtain the body interaction drive speed of the individual robot that currently meets the robot obstacle avoidance requirements and robot cluster requirements. Based on the actual body position information, the potential field constraint effect analysis is performed to obtain the current convergence and traversal driving speed of the single robot under the action of the potential field of the virtual channel. The original guided crossing speed, the body interaction driving speed, and the convergence crossing driving speed of the single robot are superimposed to obtain the expected crossing speed of the single robot in the narrow passage to be crossed. The single robot is driven to move at the desired traversal speed.

2. The method according to claim 1, characterized in that, The surrounding obstacle detection data includes the actual distances between the individual robot and surrounding obstacles in multiple discrete detection directions. The step of performing robot swarm interaction analysis based on the surrounding obstacle detection data to obtain the body interaction drive speed of the individual robot that currently meets the robot obstacle avoidance and robot swarm requirements includes: Based on the preset attraction-repulsion balance parameters and attraction-repulsion separation distance that match the robot obstacle avoidance requirements and the robot cluster requirements, the body interaction rate is predicted based on the actual distance of the individual robot in multiple discrete detection directions, and the corresponding target body interaction movement rate is obtained. Based on the attraction-repulsion balance parameters and the attraction-repulsion separation distance, the interaction direction of the robot body is predicted based on the actual distance of the single robot in multiple discrete detection directions, and the corresponding target robot body interaction movement direction is obtained. The target robot's interactive movement rate and direction are comprehensively characterized to obtain the current interactive driving speed of the single robot.

3. The method according to claim 2, characterized in that, The target body interaction movement speed of the i-th individual robot in the swarm robot system at time t is predicted using the following formula: Among them, v Dete,i,t N is used to represent the target body interaction movement speed of the i-th individual robot at time t. i The number of discrete detection directions for the i-th individual robot is used to represent the total number of directions, α is used to represent the rate control coefficient, and φ is used to represent the rate control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i D(φ) is used to represent the detection angle interval between two adjacent discrete detection directions of the i-th individual robot. i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

4. The method according to claim 2, characterized in that, The direction of the target body interaction movement of the i-th individual robot in the swarm robot system at time t is predicted using the following formula: in, N is used to represent the target body interaction movement direction of the i-th individual robot at time t. i β represents the total number of discrete detection directions for the i-th individual robot, β represents the direction control coefficient, and φ represents the direction control coefficient. i,j Δφ is used to represent the actual detection angle of the j-th discrete detection direction of the i-th individual robot. i D(φ) is used to represent the detection angle interval between two adjacent discrete detection directions of the i-th individual robot. i,j ,t) is used to represent the actual distance between the i-th individual robot and surrounding obstacles at time t in the j-th discrete detection direction, d o The γ is used to represent the attraction-repulsion separation distance, and γ is used to represent the attraction-repulsion balance parameter.

5. The method according to claim 2, characterized in that, The body interaction driving speed of the i-th individual robot in the swarm robot system at time t is calculated using the following formula: Among them, V Dete,i,t v is used to represent the body interaction driving speed of the i-th individual robot at time t. Dete,i,t This is used to represent the target body interaction movement speed of the i-th individual robot at time t. This is used to represent the direction of the target body interaction movement of the i-th individual robot at time t.

6. The method according to any one of claims 1-5, characterized in that, The step of performing potential field constraint analysis based on the actual body position information to obtain the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field includes: Based on the actual body position information, calculate the actual shortest distance between the single robot and the edge of the potential field of the virtual channel potential field; The target potential field function of the virtual channel potential field is invoked, and the potential field gradient is solved based on the actual shortest distance corresponding to the current single robot to obtain the position gradient of the single robot under the action of the virtual channel potential field. Based on the preset speed control ratio coefficient and the position gradient, the convergence and traversal driving speed of the single robot under the influence of the virtual channel potential field is calculated.

7. The method according to claim 6, characterized in that, The target potential field function is represented by the following equation: Where, d i,t U(d) represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. i,t The potential field strength is represented by σ at time t, where C represents the potential field strength coefficient, σ represents the potential field distribution attenuation parameter, and d represents the potential field strength coefficient. s d represents the safe distance that any single robot needs to maintain between itself and the inner wall of the narrow passage to be traversed. a This is used to represent the effective repulsive distance of the virtual channel potential field.

8. The method according to claim 6, characterized in that, The convergence and traversal driving speed of the i-th individual robot in the swarm robot system at time t is calculated using the following formula: Among them, V Tube,i,t The velocity d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t, k is used to represent the speed control proportional coefficient, and d is used to represent the convergence and traversal drive speed of the i-th individual robot at time t. i,t This represents the shortest distance between the i-th individual robot and the edge of the potential field of the virtual channel at time t. U(d) represents the position gradient of the i-th individual robot under the potential field of the virtual channel at time t. i,t ) is used to represent the magnitude of the potential field strength experienced by the i-th individual robot under the potential field of the virtual channel at time t.

9. A mobile robot, characterized in that, The system includes a processor, a memory, and a mobile component. The memory stores a computer program that can be executed by the processor, which can execute the computer program to invoke the mobile component to implement the narrow passage crossing control method according to any one of claims 1-8, wherein multiple mobile robots are combined to form a swarm robot system.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by each individual robot included in the swarm robot system, it implements the narrow passage crossing control method according to any one of claims 1-8.

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