Multi-robot distributed control method and system for construction site

Abstract

The invention provides a multi-robot distributed control method and system for a construction site, and relates to the technical field of robot control, and the method comprises the steps: obtaining a plurality of pieces of static geometric morphology information of a plurality of robots distributed in a construction area, carrying out the analysis to determine a plurality of geometric centers, carrying out the electronic fence modeling to determine a plurality of first electronic fences, and carrying out the control of the first electronic fences; pre-storing to a control terminal; updating the first electronic fence according to a plurality of pieces of dynamic geometrical morphology information corresponding to the plurality of real-time motion states to obtain a plurality of second electronic fences; and performing electronic fence contact detection on the plurality of robots according to the plurality of second electronic fences, if a contact detection return result is not null, determining an identified robot group with a collision risk, and performing obstacle avoidance control on the identified robot group. The technical effects of improving the obstacle avoidance response speed of the robot, reducing misjudgment and missed judgment and guaranteeing safe and efficient operation of multi-robot collaborative operation on the construction site can be achieved.

Description

Technical Field

[0001] The present application relates to the field of robot control technology, and in particular to a multi-robot distributed control method and system for construction sites. Background Art

[0002] In modern construction sites, with the continuous improvement of intelligence and automation levels, mobile robots are widely used in scenarios such as material transportation, environmental monitoring, and collaborative work. However, the construction environment is complex, the space is small, and the people are densely populated. The safety hazards brought by the simultaneous operation of multiple robots are becoming increasingly prominent, especially the high risk of collision between robots during movement. To reduce such risks, existing technologies usually adopt a static electronic fence setting method, that is, a fixed safety boundary is preset for each robot, and the obstacle avoidance response is triggered by judging whether the actual distance between the robots is less than the sum of the two static boundary radii. Although this method realizes basic collision detection and obstacle avoidance control, it essentially relies on static models and fails to fully consider the dynamic changes of the robot's geometry, speed changes, and actual motion trajectory during movement, and has certain limitations.

[0003] Currently, existing static electronic fences cannot accurately reflect the dynamic morphological changes of robots during high-speed movement or turning. For example, when a robot accelerates, decelerates, or makes a sharp turn, its motion path and geometric envelope change significantly, and the static radius cannot effectively cover the actual space occupied by these changes, which can easily lead to misjudgments or missed judgments. Secondly, most current obstacle avoidance mechanisms only judge proximity based on a preset distance, lacking a comprehensive assessment of the robot's current task urgency, size, and past interaction experience. As a result, when multiple robots are running simultaneously, the obstacle avoidance strategy has no priority division, which can easily lead to execution confusion or repeated avoidance problems. In addition, control decisions generally rely on centralized servers for scheduling, which has problems with response delays and strong communication dependence, making it difficult to meet high-frequency obstacle avoidance needs.

[0004] To sum up, the existing technology has problems such as the static fixation of the electronic fence model, the single obstacle avoidance logic and the lack of priority judgment, and the centralized control method. As a result, it is unable to respond promptly and accurately in the face of high-speed robot movement, complex trajectory changes, and multi-robot collaborative obstacle avoidance scenarios, further affecting the safety, coordination, and overall real-time and stability of the robot operation on the construction site. Summary of the Invention

[0005] The purpose of this application is to provide a multi-robot distributed control method and system for construction sites, so as to solve the problems existing in the prior art, such as the static fixation of the electronic fence model, the single obstacle avoidance logic and the lack of priority judgment, and the centralized control method, which lead to the inability to respond promptly and accurately in the face of high-speed robot movement, complex trajectory changes and multi-robot collaborative obstacle avoidance scenarios, further affecting the safety, coordination of the robot operation on the construction site and the real-time and stability of the system as a whole. Technical problems.

[0006] In view of the above problems, the present application provides a multi-robot distributed control method and system for construction sites.

[0007] In the first aspect, the present application provides a multi-robot distributed control method for construction sites, which is implemented through a multi-robot distributed control system for construction sites, including: obtaining multiple static geometric morphology information of multiple robots distributed in the construction area; analyzing the multiple static geometric morphology information according to a geometric modeling algorithm to determine multiple geometric centers, performing electronic fence modeling based on the multiple geometric centers to determine multiple first electronic fences, and pre-storing the first electronic fences to control terminals corresponding to the multiple robots respectively; the control terminal identifies multiple real-time motion states of the multiple robots, and updates the first electronic fences according to multiple dynamic geometric morphology information corresponding to the multiple real-time motion states to obtain multiple second electronic fences; the control terminals corresponding to the multiple robots perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences, and if the contact detection return result is not empty, determine the identified robot group with collision risk, and perform obstacle avoidance control on the identified robot group.

[0008] In a second aspect, the present application also provides a multi-robot distributed control system for construction sites, which is used to execute the multi-robot distributed control method for construction sites as described in the first aspect, including: an information acquisition module, used to obtain multiple static geometric morphology information of multiple robots distributed in the construction area; a fence determination module, used to analyze the multiple static geometric morphology information according to a geometric modeling algorithm, determine multiple geometric centers, perform electronic fence modeling based on the multiple geometric centers to determine multiple first electronic fences, and pre-store the first electronic fences to the control terminals corresponding to the multiple robots respectively; a fence update module, used to identify the multiple real-time motion states of the multiple robots by the control terminal, update the first electronic fence according to the multiple dynamic geometric morphology information corresponding to the multiple real-time motion states, and obtain multiple second electronic fences; an obstacle avoidance control module, used for the control terminals corresponding to the multiple robots to perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not empty, the identified robot group with collision risk is determined, and obstacle avoidance control is performed on the identified robot group.

[0009] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of building an electronic fence based on the dynamic geometric shape and real-time motion state of the robot and performing distributed obstacle avoidance control, the technical effect of improving the robot's obstacle avoidance response speed, reducing misjudgments and missed judgments, and ensuring the safe and efficient collaborative operation of multiple robots at the construction site is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 This is a flow chart of the multi-robot distributed control method for construction sites in this application;

[0013] Figure 2This is a schematic diagram of the structure of the multi-robot distributed control system for construction sites in this application.

[0014] Description of reference numerals: information acquisition module 11 , fence determination module 12 , fence update module 13 , obstacle avoidance control module 14 . DETAILED DESCRIPTION

[0015] This application provides a multi-robot distributed control method and system for construction sites, resolving existing technical issues such as the static fixed electronic fence model, the single obstacle avoidance logic and lack of priority judgment, and the centralized control method. These issues result in the inability to respond promptly and accurately to high-speed robot motion, complex trajectory changes, and multi-robot collaborative obstacle avoidance scenarios, further impacting the safety and coordination of robots operating on construction sites, as well as the real-time and stability of the system as a whole. This method achieves the technical goal of constructing an electronic fence based on the dynamic geometry and real-time motion state of the robots and performing distributed obstacle avoidance control, thereby improving the robots' obstacle avoidance response speed, reducing misjudgments and missed judgments, and ensuring the safe and efficient collaborative operation of multiple robots on construction sites.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example 1, please refer to the attached Figure 1 This application provides a multi-robot distributed control method for construction sites, which is applied to a multi-robot distributed control system for construction sites, and specifically includes the following steps:

[0018] S1: Obtain multiple static geometric information of multiple robots distributed in the construction area.

[0019] Specifically, multiple static geometric morphological information of multiple robots distributed in the construction area is collected through sensors, cameras or background control systems. The construction area refers to the workplace where construction, paving or installation operations are in progress, which may include floors, foundation pits, roads and other sites. In the construction area, multiple robots are scattered in different corners of the construction area at certain intervals or rules, and may perform different tasks such as cleaning, transportation, welding, and inspection. Static geometric morphological information refers to the dimensional structure of each robot in a non-moving state, such as length, width, height, shape outline or footprint boundary. Static geometric features do not change frequently with the movement of the robot and are stable.

[0020] S2: Analyze the multiple static geometric morphology information according to the geometric modeling algorithm to determine multiple geometric centers, perform electronic fence modeling based on the multiple geometric centers to determine multiple first electronic fences, and pre-store the first electronic fences in the control terminals corresponding to the multiple robots.

[0021] Specifically, geometric modeling algorithms refer to computational methods that mathematically describe the external structure of an object. These include convex hull algorithms, bounding box algorithms, and polygon fitting algorithms, and are used to represent and process the shape and spatial characteristics of an object in a computer. Using geometric modeling algorithms, multiple static geometric morphological data are analyzed. Specifically, a point is identified within each robot that represents its spatial position, facilitating the determination of the robot's actual position in space, orientation, and relative distance to other objects. The geometric center refers to the center of mass, center of gravity, or center of geometric symmetry of a shape.

[0022] Electronic fence modeling is performed based on multiple geometric centers to determine multiple first electronic fences. Electronic fence modeling refers to the use of geometric centers and related radius information to construct a boundary model for obstacle avoidance control. It can be in the form of a circle, polygon, etc. The purpose is to delineate the safe area when the robot is running, and finally generate a set of boundary areas corresponding to each geometric center as the electronic safety control range for subsequent obstacle avoidance and path judgment.

[0023] The first electronic fence is pre-stored in the control terminals corresponding to each of the multiple robots. Pre-storage means that the boundary data is stored in the corresponding location before the robot executes the task. The control terminal is the control device used to manage the robot's operating logic. It can be an embedded system, a host computer, or a mobile terminal. It has functions such as processing boundary information, receiving motion data, and controlling the operation path.

[0024] S3: The control terminal identifies multiple real-time motion states of the multiple robots, and updates the first electronic fence according to multiple dynamic geometric morphological information corresponding to the multiple real-time motion states to obtain multiple second electronic fences.

[0025] Specifically, the control terminal identifies the real-time motion states of multiple robots. This involves logically identifying and processing the input data to obtain a collection of data such as the robots' current position, speed, direction, and posture at a given moment. Real-time motion states are dynamic and require real-time feedback to the control terminal via sensors or communication modules for decision-making.

[0026] The multiple dynamic geometric morphology information corresponding to multiple real-time motion states refers to the changes in the robot's physical form in the computational modeling as it moves, such as edge extension during turns and inertial trailing during acceleration. These changes are generated dynamically and in real time. The first electronic fence is updated based on the multiple dynamic geometric morphology information corresponding to the multiple real-time motion states, indicating that the shape, radius, or boundary range of the first electronic fence is recalculated to adapt to the changes in the current real-time dynamic information, resulting in multiple second electronic fences. The multiple second electronic fences are new electronic fences generated based on the current real-time motion state and dynamic geometric morphology. Compared to the first electronic fence, they better reflect the robot's current safety range in real-time motion, have higher accuracy and timeliness, and are used for more precise obstacle avoidance judgment.

[0027] S4: The control terminals corresponding to the multiple robots respectively perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not empty, the identified robot group with collision risk is determined, and obstacle avoidance control is performed on the identified robot group.

[0028] Specifically, each robot in the various machines and equipment distributed throughout the construction site has its own operating and control system. The control terminals corresponding to the multiple robots perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not null, a group of robots identified as posing a collision risk is determined. This means that the electronic fences of certain robots have been identified as being in contact, resulting in an overlap between their safety boundaries. A collision risk refers to the high probability of actual physical contact between two or more robots if they continue to operate along their current trajectory. An identified robot group refers to the collection of robots identified as having a risk relationship during this detection.

[0029] Obstacle avoidance control is performed on the group of robots. This involves employing strategies (such as changing paths, adjusting speed, or temporarily stopping) to prevent collisions. This process may involve the combined efforts of multiple algorithms, such as trajectory adjustment and priority determination, to ensure that multiple robots can operate safely and collaboratively in a confined space.

[0030] Furthermore, the present application also includes: performing priority factor analysis on each robot in the identified robot group, the priority factors including task urgency, robot volume and historical interaction data, wherein the number of identified robot groups is at least 2; calculating the obstacle avoidance priority of each robot in the identified robot group based on the task urgency, robot volume and historical interaction data; determining the obstacle avoidance execution robot of the identified robot group according to the size of the obstacle avoidance priority, and controlling the obstacle avoidance execution robot to execute the obstacle avoidance strategy.

[0031] Specifically, a priority factor analysis is performed on each robot in the identified robot group. Priority factor analysis refers to the identification and quantification of multiple key parameters that affect the order of robots' obstacle avoidance, so as to determine the robot that needs to avoid first when encountering potential conflicts and the robot that can keep the current path unchanged.

[0032] Priority factors include task urgency, robot size, and historical interaction data, clarifying the reference parameters used to calculate priority. Task urgency refers to the time limit or importance of the robot's current task. The more urgent the task, the higher its priority. Robot size refers to the size of the robot in the physical space. The larger the robot, the more difficult it is to move and adjust, so maintaining a stable path is the priority. Historical interaction data indicates the frequency of yielding or conflicting with other robots during obstacle avoidance over a period of time, which can be used to learn and optimize obstacle avoidance behavior. The identification of at least two robot groups means that the scenario is based on the premise that two or more robots have potential interference or intersection.

[0033] The obstacle avoidance priority of each robot in the identified robot group is calculated based on task urgency, robot size, and historical interaction data. Obstacle avoidance priority is a ranking criterion that determines whether each robot should actively yield in the event of an obstacle avoidance conflict. For example, the higher the robot's score, the more likely it is to maintain its path in the event of a conflict, allowing other robots with lower priorities to yield.

[0034] The obstacle avoidance execution robot in the group is identified based on its obstacle avoidance priority. This robot is then controlled to execute the obstacle avoidance strategy, determining which robot is actually required to perform the obstacle avoidance action. The obstacle avoidance execution robot is the robot that is determined to need to yield or replan its path in the current conflict situation. The obstacle avoidance strategy is a specific action, including deceleration, pause, and detour. Control refers to issuing specific execution commands through the control terminal, enabling the robots to complete obstacle avoidance actions according to the priority logic.

[0035] Furthermore, the present application also includes: determining the motion trajectory of the obstacle avoidance execution robot; taking the contact detection return result as empty as the optimization target, adjusting the motion trajectory according to the A-star pathfinding algorithm, and calculating the cost data of the adjusted motion trajectory through a heuristic cost function. If the cost data meets the convergence condition, a converged motion trajectory is obtained; and using the converged motion trajectory as the obstacle avoidance strategy to control the obstacle avoidance execution robot.

[0036] Specifically, the motion trajectory of the obstacle avoidance robot is determined. The motion trajectory refers to the expected movement path of the robot from the current position to the target position. It consists of a series of continuous spatial coordinate points and represents the robot's route during movement.

[0037] The trajectory of the motion trajectory is adjusted according to the A-star pathfinding algorithm, with the contact detection return result being null as the optimization goal. The contact detection return result being null means that the safety fences between all robots in the current motion trajectory do not come into contact, that is, there is no potential risk of collision. The optimization goal represents the final expected result of the algorithm or decision. The A-star pathfinding algorithm is a commonly used path planning algorithm that can efficiently find the optimal path from the starting point to the end point in a complex environment. Trajectory adjustment refers to optimizing the original straight line or existing path so that it can complete the target navigation without overlapping with the electronic fences of other robots.

[0038] The cost of adjusting the trajectory is calculated using a heuristic cost function. A heuristic cost function is a method for estimating the cost of the shortest path from the current position to the target position. It is used in the A-star algorithm to improve search efficiency. The cost of changing the path is mathematically derived. The cost refers to the cost of trajectory adjustment, including factors such as path length, number of avoidance turns, and speed loss. A smaller value represents a more ideal path, while a higher cost indicates a greater impact on the original task during obstacle avoidance.

[0039] If the cost data meets the convergence criteria, a converged trajectory is obtained, indicating that the current cost data has stabilized or reached a preset threshold and is no longer changing significantly. A converged trajectory indicates that a stable and reasonable obstacle avoidance path has been finally determined and no further adjustments are required, meeting the obstacle avoidance goal and efficiency requirements.

[0040] Using the converged trajectory as the obstacle avoidance strategy to control the obstacle avoidance robot means that the final safe and stable path is used as the execution basis. The obstacle avoidance strategy refers to the specific plan for controlling the robot to perform avoidance behavior.

[0041] Furthermore, the present application also includes: performing contour extraction based on the multiple static geometric morphology information to determine whether the contours of the multiple robots are regular; if the contours of the multiple robots are regular, using an external expansion radius to respectively expand the radius of the multiple geometric centers, and outputting multiple first electronic fences; if the multiple robots are of irregular geometric types, calculating the buffer radius of the multiple robots according to the convex hull algorithm, using the buffer radius to respectively expand the radius of the multiple geometric centers, and outputting multiple first electronic fences.

[0042] Specifically, contour extraction is performed based on multiple static geometric morphology information. Contour extraction refers to identifying the outer boundary or external structure of the robot from multiple static geometric morphology information through edge detection, point set fitting, polygon approximation, etc., and then obtaining clear outer contour lines in two-dimensional or three-dimensional space, thereby obtaining the true shape distribution of each robot.

[0043] After contour extraction, the robot's contours are determined to be regular. This involves analyzing the contour features and performing a structural analysis of the contour shapes themselves. Regular contours may include smooth boundaries, symmetry, consistent side lengths, and standard angles. Irregular contours may appear as polygons, irregular shapes, or contours with complex undulating edges.

[0044] If the contours of multiple robots are regular—that is, their outer boundaries are standard and symmetrical, such as smooth edges, rectangular, circular, or other standard geometric shapes, with no obvious protrusions or depressions—then the robot's geometric center is expanded using an expansion radius, generating multiple first geo-fences for visualization or obstacle avoidance. The expansion radius is a value derived from the robot's maximum edge distance and represents a buffer extending outward from the geometric center to ensure a safe distance between the robot and its surroundings during normal operation.

[0045] If multiple robots are of irregular geometric types, the buffer radius of the multiple robots is calculated according to the convex hull algorithm, and multiple first electronic fences are output. The irregular geometric type means that the outer boundaries of the multiple robots do not have symmetry or standard shapes, and may have features such as unequal sides, multiple edges and corners, and protruding accessories. The convex hull algorithm is a commonly used method in computational geometry, which is used to wrap a set of boundary points into a minimum convex polygon. It can simplify the processing of complex shapes and is used for approximate modeling of irregular outer contours. The buffer radius refers to the distance from the geometric center to the farthest point of the boundary based on the approximate contour formed by the convex hull, which represents the minimum safety protection radius required for the robot in space, ensuring that the device body can be covered in any direction.

[0046] Using a buffer radius, multiple geometric centers are individually extended, creating a circular protection zone with their own buffer radius. This creates a corresponding geo-fence for each irregular robot, meeting the safety coverage requirements of its asymmetric structure. Finally, multiple primary geo-fences are output for display, control system calls, collision detection, and other functions.

[0047] Furthermore, the present application also includes: obtaining regular outer contour data of a regular robot; determining a first radius of the regular robot based on the distance between the regular outer contour data and the geometric center, using the first radius as the outer expansion radius to perform radius expansion on the multiple geometric centers respectively, and outputting multiple first electronic fences; wherein, the first radius is the radius with the largest distance from the geometric center in the regular outer contour data.

[0048] Specifically, a regular robot refers to a robot with a regular outline. The regular outer contour data of the regular robot is acquired by sensor acquisition and other methods to obtain the shape boundary information of the regular robot.

[0049] Next, the geometric center is the central position of each regular robot in space when it is at rest. It is a point calculated by the modeling algorithm. Based on the distance between the regular outer contour data and the geometric center, the first radius of the regular robot is determined, and the straight-line distance between the geometric center and each point on the outer contour is obtained.

[0050] The expansion radius is the distance extended outward from the original boundary. With the geometric center as the center, the first radius is used as the expansion radius to expand the radius of each of the multiple geometric centers, outputting multiple first geo-fences for constructing a safety buffer zone.

[0051] There are multiple distance values ​​between a point in the regular outer contour data and the geometric center. The first radius is the radius with the largest distance from the geometric center in the regular outer contour data. It represents the distance between the robot's farthest boundary and the geometric center and is used to determine the machine's expansion range, thereby ensuring that the robot has sufficient safety buffer in any direction.

[0052] Furthermore, the present application also includes: obtaining irregular outer contour data of an irregular robot; calculating approximate outer contour data of the irregular outer contour data according to a convex hull algorithm; determining a first radius of the regular robot according to the distance between the approximate outer contour data and the geometric center, and using the first radius as the expansion radius to perform radius expansion on the multiple geometric centers respectively, and outputting multiple first electronic fences; wherein, the first radius is the radius with the largest distance from the geometric center in the approximate outer contour data.

[0053] Specifically, irregular robots are robots with asymmetrical shapes, complex edges, or multiple protruding structures, such as robotic arms, special-shaped inspection robots, or work robots with attachments. The irregular outer contour data of irregular robots is acquired through sensors, 3D scanning, or vision systems. This irregular outer contour data refers to the actual boundary point information of the irregular robot, describing the actual spatial area occupied by the robot.

[0054] The convex hull algorithm is a computational geometry method that encloses all boundary points and constructs a minimal convex polygon. This method approximates the overall outline of a complex object, simplifying the analysis of the original shape. The convex hull algorithm is used to calculate approximate contour data for irregular contour data. This simplified version, derived using the convex hull algorithm, while not identical to the true boundary, better reflects the overall contour shape and is used for further calculations and modeling.

[0055] The first radius of the regular robot is determined based on the distance between the approximate outer contour data and the geometric center. This first radius is the radius with the largest distance from the geometric center in the approximate outer contour data. This radius is the maximum distance from all points in the simplified contour to the center point. This ensures that the generated circular geo-fence is sufficient to cover the entire irregular robot, even if its original shape has protruding edges.

[0056] Subsequently, the first radius is used as the expansion radius to expand the radius of multiple geometric centers respectively, that is, with the geometric center as the center of the circle, a circular area is drawn with the first radius, and multiple first electronic fences are output to form a safety buffer zone for subsequent obstacle avoidance control or path planning.

[0057] Furthermore, the present application also includes: identifying the outer contour data corresponding to the first electronic fence; judging the multiple real-time motion states, the parameters of the real-time motion states include motion speed, motion freedom and motion load size, obtaining a compensation radius based on the motion speed, motion freedom and motion load size, and updating the outer contour data of the current first electronic fence according to the compensation radius.

[0058] Specifically, the outer contour data corresponding to the first electronic fence is identified by image processing, edge detection or geometric operation, that is, the specific shape, coordinate point set or curve data of the initial boundary generated based on the geometric center and buffer radius is extracted and analyzed.

[0059] Logically analyze the input dynamic information to obtain real-time motion state parameters, including speed, degrees of freedom, and load size. Speed ​​refers to the distance the robot moves per unit time, which directly affects the time required for obstacle avoidance response. Degrees of freedom refer to the number of spatial dimensions in which the robot can move freely, typically a combination of translation and rotation in three-dimensional space. Load size refers to the weight of the load carried or supported by the robot, which may affect its braking ability and inertia at high speeds or under heavy loads.

[0060] Next, a compensation radius is calculated based on the movement speed, degrees of freedom, and load size. This radius is then expanded based on the original geo-fence to accommodate potential errors or offsets introduced by the robot's current motion state. For example, faster movement speeds require a larger compensation radius to prevent overshooting due to delayed response at high speeds. Larger loads also require additional safety distance to prevent coasting. Table 1 shows the most recent update to the contour data of the current first geo-fence. This shows that the compensation radius increases linearly with increasing speed, degrees of freedom, and load. This gradually increases the radius of the updated geo-fence, ensuring that the robot remains within its safety boundaries even under complex motion conditions.

[0061] Table 1: The latest update record of the outer contour data of the current first electronic fence

[0062]

[0063] The first electronic fence is the originally generated static safety range. The outer contour data of the first electronic fence is updated according to the compensation radius, and the original data is expanded, amplified or modified to form a dynamically adaptive boundary, making the electronic fence real-time and changeable.

[0064] Furthermore, the present application also includes: if d<(R1+R2), the contact detection return result is not empty; if d≥(R1+R2), the contact detection return result is empty; wherein, d is the real-time distance between the first robot and the second robot, R1 is the radius of the second electronic fence corresponding to the first robot, and R2 is the radius of the second electronic fence corresponding to the second robot.

[0065] Specifically, d is the real-time distance between the first and second robots, calculated by the positioning system or sensor. R1 is the radius of the second electronic fence corresponding to the first robot, and R2 is the radius of the second electronic fence corresponding to the second robot. Contact detection analyzes whether the boundaries of the two robots overlap or intersect. If the distance between the first and second robots is less than the sum of the fence radii of the first and second robots, the contact detection result is not null, indicating that an intersection has been detected, that is, the two robot fences are touching or overlapping, and relevant information is output, such as a contact warning or obstacle avoidance instruction.

[0066] If the real-time distance between the two robots is greater than or exactly equal to the sum of their fence radii, the contact detection return result is empty, which means that no contact or out-of-bounds behavior is detected. The robots can continue to move along the current path without triggering the obstacle avoidance response.

[0067] Furthermore, the present application also includes: the control terminal is an edge computing unit distributedly deployed locally on the robot, which is used to process electronic fence data and obstacle avoidance decisions in real time.

[0068] Specifically, a control terminal refers to the device that controls and manages the robot's operations and is a key component of intelligent decision-making and execution. The control terminal is an edge computing unit deployed locally on the robot in a distributed manner. Distributed deployment means that the control terminal is not centrally located on a central server, but is installed locally on each robot, with independent operation capabilities, which helps improve system response speed and stability. Local on the robot means that the terminal device is installed directly on the hardware platform of each specific robot and can directly obtain its own sensor data and motion status. An edge computing unit refers to a small computing device with sufficient data processing and computing capabilities. It can complete computing tasks directly near the data source, avoiding the delays and network pressure caused by transmitting data to a remote server, making it more suitable for time-sensitive application scenarios.

[0069] The control terminal is responsible for real-time processing of geo-fence data and obstacle avoidance decisions. This means the control terminal can analyze and respond to data immediately, processing geo-fence data and making obstacle avoidance decisions. Geo-fence data refers to each robot's current dynamic safety range information, including the fence radius, outer boundary coordinates, and geometric center. Obstacle avoidance decisions involve the terminal's actions, such as adjusting movement direction, controlling speed, or braking, when overlapping fences or potential collisions are detected, ensuring relative safety between robots.

[0070] To sum up, the multi-robot distributed control method for construction sites provided by this application has the following technical effects: by achieving the technical goal of building an electronic fence based on the dynamic geometric shape and real-time motion state of the robot and performing distributed obstacle avoidance control, the technical effect of improving the robot's obstacle avoidance response speed, reducing misjudgments and missed judgments, and ensuring the safe and efficient collaborative operation of multiple robots at the construction site is achieved.

[0071] In the second embodiment, based on the same inventive concept as the multi-robot distributed control method for construction sites in the above embodiment, the present application also provides a multi-robot distributed control system for construction sites, see the attached Figure 2 , including: an information acquisition module 11, used to obtain multiple static geometric morphology information of multiple robots distributed in the construction area; a fence determination module 12, used to analyze the multiple static geometric morphology information according to the geometric modeling algorithm, determine multiple geometric centers, perform electronic fence modeling based on the multiple geometric centers to determine multiple first electronic fences, and pre-store the first electronic fences to the control terminals corresponding to the multiple robots respectively; a fence update module 13, used to identify the multiple real-time motion states of the multiple robots by the control terminal, update the first electronic fence according to the multiple dynamic geometric morphology information corresponding to the multiple real-time motion states, and obtain multiple second electronic fences; an obstacle avoidance control module 14, used for the control terminals corresponding to the multiple robots to perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not empty, it is determined that there is a collision risk for the identified robot group, and obstacle avoidance control is performed on the identified robot group.

[0072] Furthermore, the multi-robot distributed control system for construction sites is also used to: perform priority factor analysis on each robot in the identification robot group, the priority factors including task urgency, robot volume and historical interaction data, wherein the number of identification robot groups is at least 2; calculate the obstacle avoidance priority of each robot in the identification robot group based on the task urgency, robot volume and historical interaction data; determine the obstacle avoidance execution robot of the identification robot group according to the size of the obstacle avoidance priority, and control the obstacle avoidance execution robot to execute the obstacle avoidance strategy.

[0073] Furthermore, the multi-robot distributed control system for construction sites is also used to: determine the motion trajectory of the obstacle avoidance execution robot; use the contact detection return result of null as the optimization target, adjust the motion trajectory according to the A-star pathfinding algorithm, and calculate the cost data of the adjusted motion trajectory through a heuristic cost function. If the cost data meets the convergence condition, a converged motion trajectory is obtained; and use the converged motion trajectory as the obstacle avoidance strategy to control the obstacle avoidance execution robot.

[0074] Furthermore, the multi-robot distributed control system for construction sites is also used to: perform contour extraction based on the multiple static geometric morphology information to determine whether the contours of the multiple robots are regular; if the contours of the multiple robots are regular, use an external expansion radius to respectively expand the radius of the multiple geometric centers, and output multiple first electronic fences; if the multiple robots are of irregular geometric type, calculate the buffer radius of the multiple robots according to the convex hull algorithm, use the buffer radius to respectively expand the radius of the multiple geometric centers, and output multiple first electronic fences.

[0075] Furthermore, the multi-robot distributed control system for construction sites is also used to: obtain regular outer contour data of a regular robot; determine a first radius of the regular robot based on the distance between the regular outer contour data and the geometric center, use the first radius as the outer expansion radius to perform radius expansion on the multiple geometric centers respectively, and output multiple first electronic fences; wherein, the first radius is the radius with the largest distance from the geometric center in the regular outer contour data.

[0076] Furthermore, the multi-robot distributed control system for construction sites is also used to: obtain irregular outer contour data of irregular robots; calculate approximate outer contour data of the irregular outer contour data according to a convex hull algorithm; determine the first radius of the regular robot according to the distance between the approximate outer contour data and the geometric center, and use the first radius as the expansion radius to expand the radius of the multiple geometric centers respectively, and output multiple first electronic fences; wherein, the first radius is the radius with the largest distance from the geometric center in the approximate outer contour data.

[0077] Furthermore, the multi-robot distributed control system for the construction site is also used to: identify the outer contour data corresponding to the first electronic fence; judge the multiple real-time motion states, the parameters of the real-time motion states include motion speed, motion freedom and motion load size, obtain the compensation radius according to the motion speed, motion freedom and motion load size, and update the outer contour data of the current first electronic fence according to the compensation radius.

[0078] Furthermore, the multi-robot distributed control system for construction sites is also used for: if d < (R1 + R2), the contact detection return result is not empty; if d ≥ (R1 + R2), the contact detection return result is empty; wherein, d is the real-time distance between the first robot and the second robot, R1 is the radius of the second electronic fence corresponding to the first robot, and R2 is the radius of the second electronic fence corresponding to the second robot.

[0079] Furthermore, the multi-robot distributed control system for construction sites is also used for: the control terminal is an edge computing unit distributedly deployed locally on the robot, which is used for real-time processing of electronic fence data and obstacle avoidance decisions.

[0080] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The multi-robot distributed control method for construction sites and the specific examples in the aforementioned embodiment one are also applicable to the multi-robot distributed control system for construction sites in this embodiment. Through the aforementioned detailed description of the multi-robot distributed control method for construction sites, those skilled in the art can clearly understand the multi-robot distributed control system for construction sites in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0081] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A multi-robot distributed control method for construction sites, characterized in that: include: Obtain multiple static geometric information of multiple robots distributed in the construction area; Analyzing the plurality of static geometric morphology information according to a geometric modeling algorithm to determine a plurality of geometric centers, performing electronic fence modeling based on the plurality of geometric centers to determine a plurality of first electronic fences, and pre-storing the first electronic fences in control terminals corresponding to the plurality of robots respectively; The control terminal identifies multiple real-time motion states of the multiple robots, and updates the first electronic fence according to multiple dynamic geometric morphological information corresponding to the multiple real-time motion states to obtain multiple second electronic fences; The control terminals corresponding to the multiple robots respectively perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not empty, the identified robot group with collision risk is determined, and obstacle avoidance control is performed on the identified robot group.

2. The method according to claim 1, wherein The method for performing obstacle avoidance control on the identification robot group includes: Performing a priority factor analysis on each robot in the identified robot group, wherein the priority factors include task urgency, robot size, and historical interaction data, wherein the number of the identified robot groups is at least 2; Calculating the obstacle avoidance priority of each robot in the identified robot group based on the task urgency, robot volume, and historical interaction data; The obstacle avoidance execution robot of the identified robot group is determined according to the size of the obstacle avoidance priority, and the obstacle avoidance execution robot is controlled to execute the obstacle avoidance strategy.

3. The method according to claim 2, wherein The method for obtaining the obstacle avoidance strategy includes: Determining a motion trajectory of the obstacle avoidance execution robot; Taking the contact detection result as the optimization goal, the trajectory of the motion is adjusted according to the A-star pathfinding algorithm, and the cost data of the adjusted motion trajectory is calculated by the heuristic cost function. If the cost data meets the convergence condition, the converged motion trajectory is obtained; The obstacle avoidance execution robot is controlled using a converged motion trajectory as an obstacle avoidance strategy.

4. The method according to claim 1, wherein Performing electronic fence modeling based on the multiple geometric centers to determine multiple first electronic fences includes: Performing contour extraction based on the plurality of static geometric morphology information to determine whether the contours of the plurality of robots are regular; If the contours of the multiple robots are regular, the multiple geometric centers are respectively radius-expanded using an outward expansion radius to output multiple first electronic fences; If the multiple robots are of irregular geometric types, the buffer radii of the multiple robots are calculated according to the convex hull algorithm, and the multiple geometric centers are respectively radius-extended using the buffer radii to output multiple first electronic fences.

5. The method according to claim 4, wherein The plurality of geometric centers are respectively radius-expanded using an outward expansion radius, the method comprising: Obtain the regular outer contour data of the regular robot; Determining a first radius of the regular robot according to the distance between the regular outer contour data and the geometric center, and using the first radius as an outer expansion radius to perform radius expansion on the multiple geometric centers respectively, and outputting multiple first electronic fences; The first radius is the radius with the largest distance from the geometric center in the regular outer contour data.

6. The method according to claim 4, wherein Calculating the buffer radius of the multiple robots according to a convex hull algorithm, and using the buffer radius to perform radius expansion on the multiple geometric centers respectively, the method includes: Obtain irregular outer contour data of irregular robots; Calculating approximate outer contour data of the irregular outer contour data according to a convex hull algorithm; Determining a first radius of the regular robot according to the distance between the approximate outer contour data and the geometric center, and using the first radius as an extension radius to perform radius extension on each of the multiple geometric centers to output multiple first electronic fences; The first radius is the radius of the approximate outer contour data that is the longest distance from the geometric center.

7. The method according to claim 1, wherein The method for updating the first electronic fence according to the multiple dynamic geometric form information corresponding to the multiple real-time motion states includes: Identifying outer contour data corresponding to the first electronic fence; Determine the multiple real-time motion states, the parameters of the real-time motion states include motion speed, motion freedom and motion load size, obtain the compensation radius according to the motion speed, motion freedom and motion load size, and update the outer contour data of the current first electronic fence according to the compensation radius.

8. The method according to claim 1, wherein The control terminals corresponding to the plurality of robots respectively perform electronic fence contact detection on the plurality of robots according to the plurality of second electronic fences, the method comprising: If d < (R1 + R2), the contact detection return result is not empty; If d≥(R1+R2), the contact detection return result is empty; Wherein, d is the real-time distance between the first robot and the second robot, R1 is the radius of the second electronic fence corresponding to the first robot, and R2 is the radius of the second electronic fence corresponding to the second robot.

9. The method according to claim 1, wherein The control terminal is an edge computing unit distributed and deployed locally on the robot, used for real-time processing of electronic fence data and obstacle avoidance decisions.

10. A multi-robot distributed control system for construction sites, characterized by: The steps for implementing the multi-robot distributed control method for a construction site as described in any one of claims 1 to 9 include: An information acquisition module is used to obtain multiple static geometric morphological information of multiple robots distributed in the construction area; a fence determination module, configured to analyze the plurality of static geometric morphology information according to a geometric modeling algorithm to determine a plurality of geometric centers, perform electronic fence modeling based on the plurality of geometric centers to determine a plurality of first electronic fences, and pre-store the first electronic fences in control terminals corresponding to the plurality of robots; a fence updating module, configured to allow the control terminal to identify multiple real-time motion states of the multiple robots, and update the first electronic fence according to multiple dynamic geometric morphological information corresponding to the multiple real-time motion states to obtain multiple second electronic fences; The obstacle avoidance control module is used for the control terminals corresponding to the multiple robots to perform electronic fence contact detection on the multiple robots according to the multiple second electronic fences. If the contact detection return result is not empty, the robot group with the collision risk is determined, and obstacle avoidance control is performed on the robot group.

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