A dynamic capability-graded traffic control method for heterogeneous robots

Abstract

The present invention discloses a dynamic capability-graded traffic management method for heterogeneous robots, including: S1 establishing a unified dispatching platform to receive capability registration information from heterogeneous robots; S2 defining the capability type of the robot, which is determined by a combination of a basic capability feature set and a non-empty subset of an extended capability feature set; S3 the unified dispatching platform dynamically generates a decision plan based on the robot's registered capability type and path conflicts; S4 monitoring queues and issuing queue positions to non-read-only robots, which then queue up in turn at a certain distance and speed; S5 detecting and identifying faulty robots, demarcating fault isolation zones, and executing avoidance based on capability type; S6 monitoring deadlocks and executing deadlock resolution strategies based on capability type; S7 fusing robot sensor data, with the platform and local terminals collaboratively executing avoidance. Through dynamic capability grading and targeted dispatching strategies, the operational efficiency and safety of heterogeneous robots and the overall system performance are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unified robot scheduling, and in particular to a dynamic capability-graded traffic control method for heterogeneous robots. Background Art

[0002] With the large-scale application of multi-brand and multi-type robots in scenarios such as logistics, warehousing, and industrial manufacturing, the collaborative work of these robots faces challenges such as inconsistent communication protocols and significant capability disparities. Traditional traffic control systems face these challenges, primarily due to the varying degrees of protocol and port accessibility across these robots, hindering data exchange. Furthermore, some brands of robots, due to technical protections, are unable to open unified dispatch ports and receive unified dispatch commands. Consequently, traffic control cannot be implemented using a single decision-making mechanism for these robots. Existing systems can only handle robots of a single brand or with similar technical capabilities, making it difficult to implement differentiated dispatch strategies for robots with different operational control capabilities (such as pauses, avoidance, and dynamic path replanning). Traditional avoidance mechanisms rely on local robot decisions, such as stopping or detouring, which are prone to command conflicts and response delays. This can be particularly prone to deadlock in high-density traffic scenarios, such as when oncoming vehicles simultaneously stop or detour. When multi-brand and multi-type robots form mixed formations to perform tasks, the lack of an effective hierarchical dispatch mechanism prevents the full utilization of the strengths of robots with different capabilities and overall efficiency optimization. The defects of existing technologies have led to the serious lack of applicability of robot traffic control systems in multi-brand, multi-type, highly dynamic and complex scenarios. They are also unable to fully utilize the performance advantages of different robots and are unable to meet the comprehensive requirements of traffic efficiency, safety and system robustness in actual applications.

[0003] Therefore, it is necessary to develop a dynamic capability-graded traffic control method for heterogeneous robots to address the above defects. Summary of the Invention

[0004] In response to the technical problems existing in the above-mentioned prior art, the purpose of this application is to provide a dynamic capability-graded traffic control method for heterogeneous robots. The specific steps are as follows:

[0005] S1: Establish a unified scheduling platform to receive the capability registration information of heterogeneous robots. The capability registration information at least includes the basic parameters, functional characteristics, and communication protocols of the robots;

[0006] S2: Define the robot's capability type, which is determined by a combination of a basic capability feature set and a non-empty subset of an extended capability feature set:

[0007] The basic capability feature set includes at least: information reporting (I), pause command response (P), and resume command response (R);

[0008] The extended capability feature set includes at least: avoidance point navigation (A), dynamic path replanning (D), sensor data sharing (S), and environmental interaction (E);

[0009] Capability division rules include but are not limited to:

[0010] If the capability feature subset contains {I} but does not contain {P, R, A, D, S, E}, it is defined as read-only.

[0011] If the capability feature subset includes {I, P, R} but does not include {A, D, S, E}, it is defined as the basic type.

[0012] If the capability feature subset includes {I, P, R, A} and at least one of {S, E}, it is defined as standard.

[0013] If the ability feature subset includes {I, P, R, A, D} and at least one of {S, E}, it is defined as smart type.

[0014] S3: The unified scheduling platform dynamically generates decision plans based on the robot's registered capability type and path conflicts, including:

[0015] S3a: Prediction phase: Calculate the spatiotemporal conflict probability map of the robot encounter position and perform avoidance according to the ability type, including but not limited to:

[0016] Read-only: follow the original route;

[0017] Above basic: insert a pause instruction upstream of the conflict point;

[0018] Standard and above: Insert a pause instruction upstream of the conflict point, or drive to a designated avoidance point for avoidance;

[0019] Smart type: Insert a pause instruction upstream of the conflict point, or drive to an avoidance point to avoid it, or drive according to the route re-planned by the unified scheduling platform to bypass the conflict area.

[0020] S3b: Conflict response phase: Real-time detection of path conflicts and coordination based on conflict levels, including but not limited to:

[0021] Potential conflicts: Reduce the robot's speed and allow high-priority tasks to pass first;

[0022] Emergency collision: Send an emergency stop command to the non-read-only collision robot and generate a detour path for the smart robot.

[0023] The S1 also includes that the unified scheduling platform processes heterogeneous systems through an adaptation layer. The adaptation layer includes: a protocol conversion engine that converts the native protocols of robots of various brands into a unified communication protocol, a capability registry that dynamically records the capability types of robots, and a unified spatiotemporal coordinate system that integrates multiple positioning data into a unified map.

[0024] Said S3a further comprises:

[0025] Taking the path intersection as the node, calculate the time window for each robot to arrive;

[0026] If the time windows of the two robots overlap and the spatial distance is less than or equal to the safety distance, a conflict is marked and the safety distance is dynamically adjusted according to the positioning accuracy.

[0027] Said S3b further comprises:

[0028] Path intersections are detected in real time. When the distance between the conflicting robots and the intersection is less than or equal to the dynamic safety distance, it is upgraded to an emergency conflict.

[0029] Preferably, this solution also includes queue management:

[0030] S4: In the queuing area, the robot makes an appointment with the unified dispatching platform to apply for the right to use the resources in front. The unified dispatching platform prioritizes the appointment applications according to the position order of the reserved robots. When a robot with a high priority is waiting for the right to use, the unified dispatching platform sends a queue position instruction to the subsequent non-read-only robots, and the robots queue up in turn at a certain distance and speed.

[0031] Preferably, this solution also includes S5 fault handling:

[0032] The unified dispatching platform detects and identifies faulty robots and demarcates fault isolation zones, specifically including:

[0033] Generate dynamic detour paths for smart robots and insert upstream path deceleration instructions for non-read-only robots;

[0034] When the faulty robot recovers, the task integrity is verified after repositioning and a continuation path is generated.

[0035] Preferably, this solution also includes S6 deadlock management:

[0036] S6a: Deadlock Detection: The unified scheduling platform uses deadlock detection algorithms to detect whether the robot system has deadlocks, including but not limited to: building a resource-robot relationship graph, establishing dynamic relationships, periodic evaluation mechanisms, event-driven detection, path tracing analysis, and circular dependency judgment;

[0037] S6b: Deadlock resolution: When a deadlock is detected, the nearest smart robot is selected as the guide vehicle to generate a formation passage sequence based on the robots and resource sequences involved;

[0038] For standard robots, specify an avoidance point or a straight avoidance area that is greater than or equal to the robot's safe length.

[0039] Preferably, this solution also includes S7 sensor fusion avoidance:

[0040] Robots whose capability feature subset includes {S} share sensor feature-level data to the unified scheduling platform;

[0041] Platform side: Unified scheduling and fusion of data to build a global dynamic obstacle map and perform hierarchical avoidance:

[0042] Smart type: dynamic detour;

[0043] Standard: designated avoidance points;

[0044] When dense dynamic obstacles are detected, regional speed limits and audible and visual alarms are activated for robots whose capability feature subset includes {E};

[0045] Local side: The robot performs avoidance actions based on the raw sensor data.

[0046] Preferably, the protocol conversion engine supports bandwidth adaptive compression, switches to low-precision positioning data transmission when communication is delayed, and the unified space-time coordinate system performs timestamp synchronization at set time intervals.

[0047] Preferably, the feature-level data includes: obstacle bounding box, running vector, and semantic label with confidence greater than or equal to a preset value; when the density of dynamic obstacles is greater than the preset density, the platform divides the speed limit into a low-speed running area.

[0048] Compared with the prior art, the method of hierarchical traffic control using the dynamic capabilities of heterogeneous robots in the present invention has the following main advantages or beneficial effects:

[0049] Effectively manage heterogeneous robot traffic through dynamic capability classification and targeted scheduling;

[0050] Develop personalized decision-making plans based on differences in robot capabilities to improve system compatibility and task execution efficiency;

[0051] Setting up a path conflict prediction and response mechanism can adjust the robot's behavior in real time, reduce collision risks, and ensure operational safety.

[0052] Functions such as queue management, fault handling, deadlock resolution, and sensor fusion avoidance further optimize the robot collaborative workflow and enhance system stability and reliability.

[0053] The solution of the present invention comprehensively improves the operating efficiency and intelligence level of robots of multiple brands and types in complex scenarios, and has significant innovative value and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Shown is a schematic flow chart of the application method. DETAILED DESCRIPTION

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to illustrate the present invention and are not intended to limit the present invention.

[0056] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "comprising" are used in this specification, they indicate the presence of features, steps, operations, parts or modules, components and / or their combinations;

[0057] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products or apparatuses.

[0058] It should be understood that the solutions of the present invention may be implemented by hardware, software, or other devices, singly or in combination. In the following description of the embodiments, the methods and steps of the present invention may be implemented by storing them in storage devices including but not limited to hard disks, removable storage devices, magnetic disks, optical disks, etc.

[0059] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other. Example

[0060] like Figure 1 As shown, a dynamic capability-graded traffic control method for heterogeneous robots includes the following steps:

[0061] S1: Build a unified scheduling platform:

[0062] S1a: Receiving capability registration information: The unified scheduling platform receives capability registration information sent by heterogeneous robots through the communication network. This information contains at least the robot's basic parameters (such as size, weight, maximum speed, etc.), functional characteristics (such as sensor type, navigation accuracy, etc.), and communication protocols (such as TCP / IP, UDP, and other native protocols).

[0063] S1b: Adaptation layer processing:

[0064] Protocol conversion engine: uses a protocol conversion algorithm to convert the native protocols of robots of various brands into a unified communication protocol. For example, brand A robots use the TCP / IP protocol, and brand B robots use the UDP protocol. The protocol conversion engine implements data format conversion, transmission control and other operations between the two protocols based on preset conversion rules, ensuring the compatibility and stability of communication between the unified scheduling platform and each robot.

[0065] Preferably, the protocol conversion engine monitors the communication bandwidth. When the bandwidth is insufficient or the communication is delayed, an image compression algorithm (such as JPEG\H.264, etc.) is used to adaptively compress the transmitted positioning data (such as high-precision maps, sensor image data, etc.) to reduce the data volume and ensure the real-time transmission of data.

[0066] Capability registry: This stores the capability type registered by each robot in a dynamic record format. When a robot registers or updates its capability information, the capability registry updates the record in real time, providing a basis for subsequent scheduling decisions.

[0067] Unified spatiotemporal coordinate system: Integrates multiple positioning data (such as lidar and camera data) into a unified map. Through coordinate conversion algorithms, the robot position information obtained by different positioning systems is converted into a unified spatiotemporal coordinate system, ensuring that the unified scheduling platform can accurately grasp and uniformly manage the robot position.

[0068] Preferably, the unified space-time coordinate system performs timestamp synchronization at set time intervals (such as every second or every 10 seconds, etc.), and adopts a time synchronization algorithm (such as NTP, PTP and other protocols) to ensure the time consistency between each robot and the unified scheduling platform, and ensure that time-related data such as robot position information and scheduling instructions are accurate.

[0069] S2: Define the robot capability type:

[0070] S2a: Determine the basic capability feature set and the extended capability feature set: The basic capability feature set includes but is not limited to: information reporting (I), pause command response (P), resume command response (R), etc. For example, the robot can report its own status information (such as position, speed, power, etc.) to the unified scheduling platform (I), and can respond to the pause (P) and resume (R) commands sent by the platform; the extended capability feature set includes but is not limited to: avoidance point navigation (A), dynamic path replanning (D), sensor data sharing (S), environmental interaction (E), etc. For example, the robot can navigate to the designated avoidance point according to the platform's instructions (A), replan the path according to real-time environmental information or drive according to the path replanned by the platform (D), share sensor data with other robots or platforms (S), interact with objects in the environment (such as shelves, elevators, etc.), and perform sound and light alarms (E).

[0071] S2b: Capability Classification Rules: In this embodiment, to facilitate the distinction between different types of robots and facilitate the subsequent implementation of different avoidance strategies, the robots are classified into capability categories based on the capability registry, including but not limited to:

[0072] Read-only: If the capability feature subset includes {I} but does not include {P, R, A, D, S, E}, that is, the robot only has the information reporting (I) capability and no other capabilities, it is classified as read-only. Such robots can only follow preset paths and cannot execute complex avoidance and scheduling commands. In addition, the unified scheduling platform cannot control read-only robots.

[0073] Basic type: If the capability feature subset includes {I, P, R} but does not include {A, D, S, E}, that is, the robot has the ability to report information (I), respond to pause commands (P), and respond to resume commands (R), but does not have the capabilities in the extended capability feature set, it is defined as basic type. This type of robot can perform simple pause / resume operations in the event of a conflict;

[0074] Standard: If the capability feature subset includes {I, P, R, A} and at least one of {S, E}, that is, the robot has the ability to report information (I), respond to pause commands (P), respond to resume commands (R), and navigate to avoidance points (A), and at least one of the capabilities of sensor data sharing (S) or environment interaction (E), then it is considered standard. In addition to pausing, this type of robot can also drive to a designated avoidance point when a conflict occurs.

[0075] Smart: If the capability feature subset includes {I, P, R, A, D} and at least one of {S, E}, that is, the robot has the ability to report information (I), respond to pause commands (P), respond to resume commands (R), navigate to avoidance points (A), and dynamically replan paths (D), and at least one of the capabilities of sensor data sharing (S) or environmental interaction (E), then it is considered smart. This type of robot can perform the most flexible avoidance maneuvers, such as driving along the route replanned by the platform and bypassing the conflict area.

[0076] It should be noted that the above are only the capability types listed in this embodiment. Other capability types can be divided according to the needs of specific embodiments and have the same technical effects, so they will not be repeated here.

[0077] S3: The unified scheduling platform generates dynamic decision-making solutions based on robot capability types and path conflicts:

[0078] S3a: Prediction stage:

[0079] Calculate the spatiotemporal conflict probability graph: Using path intersections as nodes, establish a mathematical model to calculate the time window for each robot to reach the intersection:

[0080] , Formula 1,

[0081] Among them, t arrive : The time when the robot is expected to arrive at the intersection, which is calculated based on the robot's current position, speed and path planning; Δt: Time deviation, which is determined by taking into account factors such as robot speed fluctuation and positioning error.

[0082] If the time windows of the two robots overlap and the spatial distance is less than or equal to the safety distance, a conflict is marked and the safety distance is dynamically adjusted:

[0083] , Formula 2,

[0084] Where, k is the safety factor, which is set according to factors such as the robot's operating environment and speed, and d precision : Positioning accuracy is determined by the performance of the positioning system.

[0085] Avoidance strategy execution: Execute avoidance by capability type, including but not limited to:

[0086] Read-only: follow the original route;

[0087] Above basic: insert a pause instruction upstream of the conflict point;

[0088] Standard and above: Insert a pause instruction upstream of the conflict point, or drive to a designated avoidance point for avoidance;

[0089] Smart type: insert a pause instruction upstream of the conflict point, or drive to the avoidance point to avoid it, or drive according to the route re-planned by the unified dispatching platform to bypass the conflict area.

[0090] It should be noted that the avoidance strategy can be formulated according to the capability types of different robots, and an avoidance strategy different from this solution can also be formulated with the same technical effect, which will not be repeated here.

[0091] It should be noted that the path planning algorithm of the unified scheduling platform can adopt A*, D*, BFS, DFS, etc. commonly used or optimized in this field, and the appropriate algorithm can be selected according to the actual calculation situation.

[0092] S3b: Conflict Response Phase:

[0093] Real-time detection and conflict escalation judgment: Real-time detection of robot path intersections. When the distance between the conflicting robots and the intersection is less than or equal to the dynamic safety distance, it is upgraded to an emergency conflict. The dynamic safety distance calculation formula is the same as the above safety distance (Formula 2), but the k value can be appropriately increased in the case of an emergency conflict to improve safety. It will not be repeated here.

[0094] Implementation of coordination measures: coordination according to conflict levels, including but not limited to:

[0095] Potential conflicts: Reduce the robot's speed and allow high-priority tasks to pass first;

[0096] Emergency collision: Send an emergency stop command to the non-read-only collision robot and generate a detour path for the smart robot;

[0097] If the above coordination measures still cannot resolve the problem, an alarm will be triggered and manual intervention will be required to resolve the conflict.

[0098] S4: Queue management: In certain operating scenarios, when queue robots need or are about to apply for the same resource, they need to queue and wait: in the queue waiting area, the robot makes an appointment with the unified dispatching platform to apply for the right to use the resources in front (such as regional access rights, elevator use rights, etc.). The unified dispatching platform prioritizes the appointment applications according to the position order of the appointment robots. When a robot with a high priority is waiting for the right to use, the unified dispatching platform sends queue position instructions (position coordinates, pause, resume, etc.) to the subsequent non-read-only robots. The robots queue up in turn at a certain distance and speed to ensure the orderly progress of the queuing process and avoid congestion and deadlock caused by the robots entering later. For uncontrollable read-only robots, other non-read-only robots queue up in front and behind them according to their status and position information. If congestion and deadlock occur due to the detour of the read-only robots, an alarm is triggered and manual intervention is required to resolve it.

[0099] S5: Fault handling: If a heterogeneous robot in the fleet fails, follow the following process:

[0100] S5a: Fault Identification and Isolation: The unified dispatch platform monitors robot status information (such as heartbeat signals and fault codes) to identify faulty robots. When demarcating the fault isolation zone, it considers factors such as the faulty robot's location, size, and surrounding environment to determine the isolation zone's scope. It generates dynamic detour paths for smart robots, using path planning algorithms to avoid the fault isolation zone while ensuring path optimality. It inserts upstream path deceleration instructions for non-read-only robots, calculating a reasonable deceleration value based on factors such as the robot's distance from the fault point and its speed, ensuring the robot's safe passage or waiting for the fault to be cleared.

[0101] S5b: Fault recovery and task continuation: When the faulty robot recovers, the task integrity is verified after repositioning. By comparing the task information and position information before and after the fault, the remaining part of the task is determined and a continuation path is generated so that the robot can continue to perform the unfinished task from the fault point.

[0102] S6: Deadlock Management: In multi-robot traffic control scenarios, deadlock may occur when multiple robots are unable to move due to path planning conflicts, uncoordinated avoidance actions, etc., and fall into a stalemate where each robot waits for the other to avoid the robot while the other robot fails to take effective avoidance measures, making it impossible to complete the established work process normally.

[0103] Therefore, in a multi-robot operation scenario, it is necessary to monitor the deadlock status and resolve the deadlock through the system, as follows:

[0104] S6a: Deadlock Detection: The unified scheduling platform uses a deadlock detection algorithm to detect whether the robot system is deadlocked, including whether there is a deadlock and the robot and resource sequence involved in the deadlock. In this embodiment, the deadlock detection algorithm is as follows. Those skilled in the art may also use other deadlock detection algorithms according to computing requirements. The algorithm of this embodiment is as follows:

[0105] (1) Construct a resource-robot relationship graph: Construct a directed graph G = (V, E), where the vertex set V contains robot vertices R = {R1, R2, ..., Rn} and resource vertices H = {H1, H2, ..., Hm}. The robot vertices represent the various robot entities in the system, and the resource vertices represent the various resources that the robot may need to access.

[0106] (2) Establish dynamic relationships: By monitoring the interaction between robots and resources, the resource-robot relationship graph is dynamically updated. When a robot applies for or releases a resource, the corresponding edge is added or removed from the graph G. If the robot R i Application Resource H j , add directed edge Ri →H j ; If resource H j Assigned to robot R i , add directed edge H j →R i ;Define the edge where the robot applies for resources as the request edge, and the edge where resources are allocated to the robot as the allocation edge;

[0107] (3) Periodic evaluation mechanism: Establish a periodic evaluation mechanism to regularly analyze the resource-robot relationship graph to identify potential deadlock states. The evaluation period can be adjusted according to the system scale and complexity to ensure timely detection of deadlock risks. The formula is as follows:

[0108] , Formula 3,

[0109] Among them, T min : Minimum value of the evaluation period, T max : The maximum value of the evaluation cycle, N: The number of robots in the system, α is the adjustment coefficient, N threshold : Threshold for the number of robots.

[0110] (4) Event-driven detection: Set up an event-driven detection mechanism to immediately start the deadlock detection process when key events occur, such as when the robot applies for or releases resources or when the task status changes, to ensure that it can quickly respond to changes in the system status and improve the timeliness of deadlock detection.

[0111] (5) Path tracing analysis: Using the depth-first search (DFS) algorithm, starting from each undetected node in the resource-robot relationship graph, trace the path of resource application and allocation, and initialize the visited node set V visited = , initialize DFS (v0), the DFS recursive process is as follows:

[0112] , Formula 4,

[0113] Among them, V visited : A set of visited nodes, V0: starting node, V: current node, N(v): a set of all adjacent nodes of node V, : Difference operation of sets, indicating selection from unvisited adjacent nodes, w: adjacent node, DFS(v): recursive process of depth-first search, if node v has been visited, it returns, otherwise, visit node v, mark it as visited, and recursively call DFS for all unvisited adjacent nodes v.

[0114] (6) Circular dependency judgment: If one of the robots indirectly or directly waits for a resource that it has already occupied, it indicates a deadlock risk, triggers deadlock resolution, and records the sequence of robots and resources involved as the input for the deadlock resolution strategy. The formula is as follows:

[0115] ,

[0116] , Formula Five,

[0117] Where, P: access path, v0, v1, …, v k : nodes in the path, i, j: position indices in the path. If there exists i < j and v i = v j , it means that in the path, the node v j has been visited, indicating a cycle. Cycle: result of circular dependency judgment. If the value is True, it means that a circular dependency is found during the search process. If the value is False, it means that no circular dependency is found.

[0118] Further, before deadlock detection, the resources are pre-classified to distinguish critical resources and non-critical resources. Higher priority is given to the application and allocation of critical resources, and stricter monitoring is carried out. During deadlock detection, a hierarchical detection mechanism is adopted. First, the robots and resource nodes related to critical resources are detected, and then it is extended to the entire system to improve the detection efficiency and ensure the stable operation of key system functions.

[0119] S6b: Deadlock resolution strategy: When a deadlock is detected, according to the sequence of robots and resources involved, the nearest intelligent robot is selected as the leading vehicle to generate a formation passing sequence. According to factors such as the positions and speeds of each robot, the leading vehicle is determined, and a guiding path is planned for it to lead other robots to resolve the deadlock. Avoidance points or straight avoidance areas greater than or equal to the body safety length are preset for standard robots to ensure that standard robots have safe avoidance spaces and paths during deadlock resolution and avoid new conflicts. If the unified scheduling platform cannot resolve the deadlock, an alarm is triggered and manual intervention is required to resolve it.

[0120] S7: Sensor fusion avoidance: In some embodiments, the unified scheduling platform can fuse the sensors uploaded by the robots and formulate a collaborative avoidance mechanism for the platform side and the local side, as follows:

[0121] S7a: Sensor data sharing and fusion: Robots whose capability feature subset includes {S} share the feature-level data of their sensors to the unified scheduling platform, converting data in different coordinate systems to a unified coordinate system. The platform uses data fusion algorithms, such as the Kalman filter fusion algorithm, to fuse sensor data from different robots and construct a global dynamic obstacle map.

[0122] Furthermore, feature-level data includes obstacle bounding boxes (rectangular or irregularly shaped boxes surrounded by coordinate points, indicating the position and range of the obstacle in space), running vectors (vectors containing information such as the obstacle's movement direction and speed), semantic labels with confidence levels greater than or equal to preset values ​​(such as the confidence level of the obstacle category judgment. If it is greater than a preset value, the label is considered reliable and can be used for subsequent avoidance decisions), etc. The robot extracts these feature-level data from the raw sensor data through data processing algorithms.

[0123] S7b: Graded avoidance execution:

[0124] Platform-side graded avoidance: Based on the global dynamic obstacle map, graded avoidance strategies are implemented for robots of different capabilities: Smart robots perform dynamic detours, planning detour paths in real time to avoid obstacles based on real-time obstacle information and their own kinematic models;

[0125] The standard robot drives to the designated avoidance point. The platform assigns a suitable avoidance point to the standard robot based on the obstacle location and the robot's capabilities, and guides it to the point to wait. When dense dynamic obstacles are detected, the regional speed limit and audible and visual alarms are activated (for robots with the capability feature subset containing {E}). This reduces the robot's speed to improve safety, and the audible and visual alarms alert surrounding pedestrians and vehicles to avoid the obstacle.

[0126] Furthermore, when the density of dynamic obstacles is greater than the preset density, the platform is divided into a low-speed operation zone with a speed limit. In this zone, the robot's operating speed is reduced to improve safety. The division is based on the following:

[0127] , Formula 6,

[0128] Among them, A low_speed : Low speed operation area, D obs (x,y,z): dynamic obstacle density at the spatial point (x,y,z), D threshold : The preset density threshold.

[0129] Local avoidance: The local robot uses raw sensor data, such as lidar point cloud data and camera image data, and its own preset algorithms to quickly execute avoidance actions. For example, the robot can autonomously navigate around obstacles, and the robot on a preset track can stop and wait when encountering obstacles. This can respond to sudden close-range obstacles and ensure real-time, flexible, and safe avoidance.

[0130] It should be noted that in addition to the traffic control measures such as path conflict, queue management, fault handling, deadlock resolution, sensor fusion avoidance, etc. listed in this embodiment, in the complex situation where multiple brands and multiple types of robots are running at the same time, there may be other conflicts. This embodiment cannot cover and explain them one by one. Without departing from the principles of the present invention, the traffic control method using dynamic capability classification is considered to be within the scope of protection of the present invention.

[0131] The present invention can effectively improve the deficiencies of existing technologies. Through dynamic capability grading and targeted scheduling strategies, it can effectively improve the operating efficiency, safety and overall system performance of heterogeneous robots, enhance the compatibility, scalability and intelligence level of the system, and provide a reliable and efficient solution for multi-robot collaboration, which has great promotion value.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A dynamic capability-graded traffic control method for heterogeneous robots, characterized by: include: S1: Establish a unified scheduling platform to receive capability registration information of heterogeneous robots, where the capability registration information includes at least basic parameters, functional characteristics, and communication protocols of the robots; S2: Define the robot's capability type, which is determined by a combination of a basic capability feature set and a non-empty subset of an extended capability feature set: The basic capability feature set includes at least: information reporting (I), pause command response (P), and resume command response (R); The extended capability feature set includes at least: avoidance point navigation (A), dynamic path replanning (D), sensor data sharing (S), and environmental interaction (E); Capability division rules include but are not limited to: If the capability feature subset contains {I} but does not contain {P, R, A, D, S, E}, it is defined as read-only. If the capability feature subset includes {I, P, R} but does not include {A, D, S, E}, it is defined as the basic type. If the capability feature subset includes {I, P, R, A} and at least one of {S, E}, it is defined as standard. If the ability feature subset includes {I, P, R, A, D} and at least one of {S, E}, it is defined as smart type. S3: The unified scheduling platform dynamically generates decision plans based on the robot's registered capability type and path conflicts, including: S3a: Prediction phase: Calculate the spatiotemporal conflict probability map of the robot encounter position and perform avoidance according to the ability type, including but not limited to: Read-only: follow the original route; Above basic: insert a pause instruction upstream of the conflict point; Standard and above: Insert a pause instruction upstream of the conflict point, or drive to a designated avoidance point for avoidance; Smart: Insert a pause command upstream of the conflict point, or drive to an avoidance point to avoid the conflict, or follow the route replanned by the unified dispatch platform to bypass the conflict area; S3b: Conflict response phase: Real-time detection of path conflicts and coordination based on conflict levels, including but not limited to: Potential conflicts: Reduce the robot's speed and allow high-priority tasks to pass first; Emergency collision: Send an emergency stop command to the non-read-only collision robot and generate a detour path for the smart robot.

2. A dynamic capability-graded traffic control method for heterogeneous robots according to claim 1, characterized in that: The S1 also includes that the unified scheduling platform processes heterogeneous systems through an adaptation layer. The adaptation layer includes: a protocol conversion engine that converts the native protocols of robots of various brands into a unified communication protocol, a capability registry that dynamically records the capability types of robots, and a unified spatiotemporal coordinate system that integrates multiple positioning data into a unified map.

3. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Said S3a further comprises: Taking the path intersection as the node, calculate the time window for each robot to arrive; If the time windows of the two robots overlap and the spatial distance is less than or equal to the safety distance, a conflict is marked and the safety distance is dynamically adjusted according to the positioning accuracy.

4. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Said S3b further comprises: Path intersections are detected in real time. When the distance between the conflicting robots and the intersection is less than or equal to the dynamic safety distance, it is upgraded to an emergency conflict.

5. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Also includes queue management: S4: In the queuing area, the robot makes an appointment with the unified dispatching platform to apply for the right to use the resources in front. The unified dispatching platform prioritizes the appointment applications according to the position order of the reserved robots. When a robot with a high priority is waiting for the right to use, the unified dispatching platform sends a queue position instruction to the subsequent non-read-only robots, and the robots queue up in turn at a certain distance and speed.

6. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Also includes S5 troubleshooting: The unified dispatching platform detects and identifies faulty robots and demarcates fault isolation zones, specifically including: Generate dynamic detour paths for smart robots and insert upstream path deceleration instructions for non-read-only robots; When the faulty robot recovers, the task integrity is verified after repositioning and a continuation path is generated.

7. The method of dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Also includes S6 deadlock management: S6a: Deadlock Detection: The unified scheduling platform uses deadlock detection algorithms to detect whether the robot system has deadlocks, including but not limited to: building a resource-robot relationship graph, establishing dynamic relationships, periodic evaluation mechanisms, event-driven detection, path tracing analysis, and circular dependency judgment; S6b: Deadlock resolution: When a deadlock is detected, the nearest smart robot is selected as the guide vehicle to generate a formation passage sequence based on the robots and resource sequences involved; For standard robots, specify an avoidance point or a straight avoidance area that is greater than or equal to the robot's safe length.

8. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 1, characterized in that: Also includes S7 sensor fusion avoidance: Robots whose capability feature subset includes {S} share sensor feature-level data to the unified scheduling platform; Platform side: Unified scheduling and fusion of data to build a global dynamic obstacle map and perform hierarchical avoidance: Smart type: dynamic detour; Standard: designated avoidance points; When dense dynamic obstacles are detected, regional speed limits and audible and visual alarms are activated for robots whose capability feature subset includes {E}; Local side: The robot performs avoidance actions based on the raw sensor data.

9. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 2, characterized in that: The protocol conversion engine supports bandwidth adaptive compression, switches to low-precision positioning data transmission when communication is delayed, and the unified space-time coordinate system performs timestamp synchronization at set time intervals.

10. The method for dynamic capability-graded traffic control for heterogeneous robots according to claim 8, characterized in that: The feature-level data includes: obstacle bounding box, running vector, and semantic label with confidence greater than or equal to a preset value; when the density of dynamic obstacles is greater than the preset density, the platform divides the speed limit into a low-speed running zone.

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