Multi-class robot cross-mode collaborative scheduling method, system and device

Abstract

The application provides a cross-form cooperative scheduling method, system and equipment for multi-class robots, and relates to the technical field of robot control. The method comprises the following steps: in response to a request instruction of a robot, obtaining a robot passport; generating a compatibility score between the robot and each berth in the collection and distribution place according to the robot passport and real-time occupation information of each berth; distributing a target berth for the robot according to the compatibility score and combining the remaining power of the robot, the task urgency and the expected waiting time; controlling the robot to complete the docking fixation by using a corresponding multi-stage docking guidance strategy according to the type and interface type of the target berth; after the robot is docked and fixed, performing a preset operation on the robot according to the subsequent task demand and health state of the robot, and performing a preset scheduling operation on the robot after the preset operation is completed. The application improves the autonomous cooperation capability and long-term operation efficiency of the heterogeneous robot cluster in a complex scene.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and more specifically, to a cross-morphological collaborative scheduling method, system, and device for multiple types of robots. Background Technology

[0002] Currently, various types of robots, including drones, quadruped robots, wheeled mobile robots, and mobile operation robots, are showing a trend of collaborative operation in scenarios such as power line inspection, smart parks, warehousing and logistics, and emergency rescue. Different robot types leverage their respective advantages: drones are suitable for large-scale high-altitude inspections, quadruped robots can adapt to stairs and complex terrain, wheeled robots excel at high-speed delivery on leveled surfaces, and mobile operation robots can perform delicate actions such as opening doors, operating buttons, and grasping. The collaborative operation of multiple robot types can improve the coverage and execution efficiency of complex task scenarios. Therefore, it is necessary to provide a unified infrastructure for heterogeneous robot clusters, enabling them to complete a series of operations such as returning to port, recharging, reloading, data unloading, and redeployment during long-term unattended operation.

[0003] In related technologies, drone airports are designed only for landing, charging, and data transmission of a single drone model. Their lifting platforms, locking mechanisms, and cabin spaces are incompatible with quadrupedal, wheeled, or humanoid robots. Mobile robot charging stations are also only designed for specific wheeled robot models, making it difficult to simultaneously handle the needs of aircraft landing, legged robot standing, humanoid robot anti-tipping, and end-effector tool replacement. Because existing solutions lack a unified infrastructure compatible with multiple robot types, the mechanical interfaces, electrical parameters, and communication protocols of different robots are difficult to automatically match. This results in multiple types of robots still requiring manual intervention after returning to port to complete recharging, data exchange, or payload replacement. These shortcomings make it difficult for heterogeneous robots to automatically complete the series of operations after returning to port during long-term continuous operation, failing to meet the collaborative scheduling needs across different robot types and manufacturers in real-world scenarios. Summary of the Invention

[0004] The problem addressed by this invention is how to achieve distributed collaboration among multiple types of robots.

[0005] To address the aforementioned issues, this invention provides a cross-morphological collaborative scheduling method, system, and device for multiple types of robots.

[0006] In a first aspect, the present invention provides a cross-morphological cooperative scheduling method for multi-category robots, comprising: In response to the robot's request command, the robot passport is obtained, which includes robot category, shape envelope, interface type, battery parameters, communication protocol and task status; Based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, a compatibility score between the robot and each berth is generated. Based on the compatibility score and combined with the robot's remaining battery power, task urgency, and estimated waiting time, a target berth is assigned to the robot. Based on the type of the target berth and the type of the interface, a corresponding multi-stage docking guidance strategy is used to control the robot to complete docking and fixation. After the robot is fixed in the dock, a preset operation is performed on the robot according to the robot's subsequent task requirements and health status. After the preset operation is completed, a preset scheduling operation is performed on the robot.

[0007] Optionally, the method further includes, prior to responding to the robot's request instruction: The request message sent by the robot is received through the open adapter layer. The request message includes the robot's identity, current location, current battery level, and request type identifier. The request type identifier is used to indicate whether the request instruction is a return to port request or a dispatch request.

[0008] Optionally, obtaining the robot's robot passport in response to the robot's request instruction includes: The robot passport is retrieved based on the identity identifier carried in the request message; if the retrieval is successful, the robot passport is read. If the search fails, the robot's feature parameters are collected to generate a new robot passport, which is then stored.

[0009] Optionally, the step of generating a compatibility score between the robot and each berth based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, includes: The first score is determined based on the robot category, the shape envelope, the category requirements of the berth, and the size limits of the berth. The second score is determined based on the interface type, the communication protocol, and the interface configuration supported by the berth. Based on the battery parameters and the current location carried in the request message, the energy reachability score for the robot to reach the berth is determined. Based on the task status and the real-time occupancy information of the berth, determine the available score of the berth for the robot; A safety risk score is determined based on the robot category and the current battery level carried in the request message; Based on the queuing situation at the berths, a waiting time score is determined; The first score, the second score, the energy reachability score, and the available score are added together, and the safety risk score and the waiting time score are subtracted to obtain the compatibility score between the robot and the berth.

[0010] Optionally, the step of allocating a target parking space to the robot based on the compatibility score, combined with the robot's remaining battery power, task urgency, and estimated waiting time, includes: Based on the remaining battery power, determine the robot's battery urgency level, and generate a battery correction factor based on the battery urgency level; Based on the urgency of the task, the task priority of the robot is determined, and an urgency correction factor is generated based on the task priority; Based on the estimated waiting time, the robot's waiting tolerance is determined, and a waiting time penalty factor is generated based on the waiting tolerance. The power correction factor, the urgency correction factor, and the waiting time penalty factor are weighted and combined with the compatibility score to obtain the correction score for each berth. The berths are sorted from highest to lowest according to the corrected score, and the berth with the highest corrected score in the sorting result is selected as the target berth, and the target berth is assigned to the robot.

[0011] Optionally, the multi-stage docking guidance strategy includes: a long-distance guidance strategy, a medium-distance guidance strategy, and a short-distance guidance strategy. The step of controlling the robot to complete docking and fixing using the corresponding multi-stage docking guidance strategy based on the type of the target berth and the interface type includes: The long-distance guidance strategy is executed as follows: an entry path is generated based on the location information of the target berth, and the entry path and speed limit are sent to the robot through the task map to guide the robot into the distribution center entrance area; The mid-range guidance strategy is executed as follows: The robot's current position is calculated based on the UWB tag signal received from a UWB base station; alternatively, the robot's positioning data is acquired via an RTK receiver; or, the robot's relative position is determined by emitting a laser beam from a lidar and receiving the echo reflected from the reflector on the robot. The current or relative position is compared with a preset alignment point of the target berth to generate a position deviation. This position deviation is then converted into a correction command and sent to the robot, guiding it towards the target berth. The short-range guidance strategy is executed as follows: A corresponding visual target is activated based on the type of the target berth; the relative pose of the robot and the visual target is acquired through a visual sensor; an alignment adjustment command is generated based on the relative pose to control the robot to gradually align with the positioning mechanism of the target berth; during the process of the robot contacting the positioning mechanism, contact force information between the robot and the positioning mechanism is acquired through a force sensor; the robot's movement speed and posture are adjusted based on the contact force information to guide the robot to contact the positioning mechanism. After the robot contacts the positioning mechanism, the corresponding locking method is selected according to the interface type, and the locking mechanism is triggered to mechanically fix the robot in combination with the locking method. The positioning status of the locking mechanism is detected by the sensor. When the locking mechanism is detected to be in place and the robot's attitude sensor feedback indicates a stable docking state, it is confirmed that the robot has completed docking and securing.

[0012] Optionally, the preset operation includes: performing at least one of the following operations on the robot according to the subsequent task requirements and the health status: energy replenishment operation, battery swapping operation, tool replacement operation, load replacement operation, data unloading operation, health detection operation, and cleaning and maintenance operation; The preset scheduling operation includes: after the preset operation is completed, the robot is redeployed, transferred to the standby area, or transferred to the isolation maintenance area according to the subsequent task requirements and the health status.

[0013] Secondly, the cross-morphological collaborative scheduling system for multi-category robots of the present invention includes: The acquisition unit is used to acquire the robot's robot passport in response to the robot's request command. The robot passport includes robot category, shape envelope, interface type, battery parameters, communication protocol and task status. The berth allocation unit is used to generate a compatibility score between the robot and each berth based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center. Based on the compatibility score and in combination with the robot's remaining battery power, task urgency, and estimated waiting time, the unit allocates a target berth to the robot. The docking guidance unit is used to control the robot to complete docking and fixing according to the type of the target berth and the interface type, using a corresponding multi-stage docking guidance strategy. The job scheduling unit is used to perform preset job operations on the robot after the robot is fixed in the dock, based on the robot's subsequent task requirements and health status, and to perform preset scheduling operations on the robot after the preset job operations are completed.

[0014] Thirdly, the electronic device of the present invention includes: a processor and a memory, the memory being used to store a computer program; When the computer program is loaded by the processor, it causes the processor to execute the aforementioned cross-morphological cooperative scheduling method for multi-category robots.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described cross-morphological cooperative scheduling method for multi-category robots.

[0016] This invention provides a cross-morphological collaborative scheduling method, system, device, and storage medium for multiple robot types. It uses a robot passport to structurally encapsulate multi-dimensional information such as robot type, shape envelope, interface type, battery parameters, communication protocol, and task status, enabling distribution centers to establish a unified information model for robots of different forms, such as drones, quadruped robots, wheeled robots, and mobile operation robots. Based on the robot type and shape envelope in the passport, and combined with real-time berth occupancy information, a compatibility score is generated, automatically selecting berths that match in physical size, mechanical interface, and electrical parameters. This fundamentally solves the problem of multiple robot types being unable to automatically match berths due to differences in mechanical interfaces and electrical parameters, eliminating the reliance on manual intervention.

[0017] Building upon compatibility scores, the system further integrates the robot's remaining battery power, task urgency, and expected waiting time for dock allocation. This technological feature allows the scheduling system to move beyond static compatibility and dynamically balance factors such as the robot's current energy crisis (low battery), task timeliness (urgent tasks), and queuing expectations (waiting time). For example, for a drone with nearly depleted battery power, even if its compatibility score is slightly lower than a certain dock, the system can prioritize allocating it to the nearest and fastest docking dock, thus avoiding operational interruptions or equipment damage. This mechanism significantly improves the robustness and task completion rate of heterogeneous robots during long-term unattended operation. Furthermore, based on the type of target dock, such as a lifting platform, charging station, or transshipment station, and the robot's interface type, a corresponding multi-stage docking guidance strategy is adopted. Unlike traditional single guidance modes (such as those applicable only to drones or wheeled robots), this system can perform differentiated actions such as coarse positioning, fine alignment, and mechanical locking in stages for different motion modes, such as aircraft (requiring landing accuracy control), legged robots (requiring standing stability control), and wheeled robots (requiring path following control). By employing a phased and switchable guidance logic, the system effectively solves cross-morphological docking challenges such as preventing quadruped robots from tipping over, preventing drones from landing in crosswinds, and maintaining the center of gravity of humanoid robots, thus achieving safe and reliable mechanical fixation and electrical docking.

[0018] After being docked and secured, the robots perform pre-set operations based on their subsequent task requirements and health status, such as recharging, data unloading, payload replacement, and software upgrades. This transforms the distribution center from a passive charging station or helipad into an intelligent node with proactive service capabilities. For example, for mobile robots about to perform delicate operations, the end effector can be proactively replaced or hydraulic oil replenished; for drones that have completed inspections but have not uploaded data, high-speed data unloading is automatically triggered. This on-demand, status-based operation mode significantly improves the continuous combat capability and task flexibility of various robot types. Furthermore, after completing the pre-set operations, pre-set scheduling operations are executed, such as wake-up, redeployment, standby / hibernation, and relocation to other distribution centers. This invention achieves a complete automated closed loop for robots from requesting to return to port to re-departure, without human intervention. Heterogeneous robots can automatically complete a series of operations such as recharging, changing equipment, data interaction, and redeployment, meeting the continuous operation requirements in long-term unattended scenarios. Furthermore, because the robot passport adopts an scalable data structure design, robots from different manufacturers and of different models can be identified and scheduled in a centralized manner simply by providing their own attribute information in a standard format. It can be seen that the present invention also has good openness and compatibility, avoiding the high cost of developing an adapter interface for each type of robot, and providing a feasible solution for the unified infrastructure deployment of large-scale heterogeneous robot clusters.

[0019] In summary, this invention achieves unified information representation through robot passports, optimizes berth allocation through dynamic compatibility scoring, ensures safe and secure docking through multi-stage docking guidance, and completes the return-to-port closed loop with health status perception and full-process operation scheduling. It realizes cross-morphological collaborative scheduling for multiple types of robots (such as drones, legged, wheeled, and manipulators), solving the fundamental problems of existing technologies such as lack of unified infrastructure, reliance on manual intervention, and inability to automatically complete a series of operations after returning to port. It also improves the autonomous collaborative capability and long-term operating efficiency of heterogeneous robot clusters in complex scenarios. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the cross-morphological collaborative scheduling method for multi-category robots according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the cross-morphological collaborative scheduling system for multi-category robots according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0022] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0024] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0025] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties. The collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0026] Combination Figure 1 As shown, this embodiment of the invention provides a cross-morphological cooperative scheduling method for multi-category robots, including: In response to a request from the robot, the robot passport is obtained, which includes robot category, shape envelope, interface type, battery parameters, communication protocol, and task status.

[0027] Specifically, in response to the robot's request command, the edge controller of the distribution center obtains the robot's passport through the open adapter layer. This robot passport is generated by the system when the robot first enters the distribution center, through manual input, import from the manufacturer's interface, QR code / NFC reading, or automatic identification. It includes at least the robot category, chassis size and maximum outer envelope, weight and center of gravity height, kinematic constraints and climbing and obstacle crossing capabilities, battery type and voltage level and charging interface, communication protocol (including ROS2, Open-RMF, MQTT, HTTP, CAN, EtherCAT or manufacturer's proprietary protocol), tool interface and sensor configuration, set of executable tasks and prohibited actions, safe speed and emergency stop lane, and maintenance cycle. The edge controller stores the robot passport locally and generates a robot capability map and interface dictionary based on it for subsequent berth matching and operation process selection.

[0028] Based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, a compatibility score is generated between the robot and each berth. Based on the compatibility score, combined with the robot's remaining battery power, task urgency, and estimated waiting time, a target berth is assigned to the robot.

[0029] Specifically, based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, the edge controller uses a compatibility scoring formula. The compatibility score between the robot and each berth is calculated; among which... This represents the compatibility score between the i-th robot and the j-th berth. This indicates the degree of matching between capacity and berth type. Indicates the interface matching degree. This indicates whether the robot has enough remaining energy to reach the berth. Indicates berth availability. Indicates security risk. This represents the expected waiting time, with α, β, γ, δ, λ, and μ as weights. Based on the compatibility score, the robot's remaining battery power is further considered. Estimate the available energy upon arrival, of which, This indicates the estimated available energy after the robot arrives at the distribution center. This represents the current battery percentage. For the battery's usable capacity, Energy consumption per unit of path To conserve energy for safety.

[0030] The urgency of the task (e.g., high-security inspection tasks take priority over regular delivery tasks) and the estimated waiting time are determined by... Estimate, where, where This indicates the estimated service time for berth j, determined by the docking time. Energy replenishment time Dressing time Detection time and data unloading Time-based configuration. Target berths are assigned to the robots; when multiple robots return to port simultaneously, the system calculates priorities based on remaining battery power, task risk, health status, and task value, prioritizing robots with low battery power and performing high-safety-level tasks, and guiding them to their corresponding entrances and waiting areas.

[0031] Based on the type of the target berth and the type of the interface, a corresponding multi-stage docking guidance strategy is adopted to control the robot to complete docking and fixation.

[0032] Specifically, based on the type of target berth and interface, the edge controller employs a corresponding multi-stage docking guidance strategy to control the robot to complete docking and fixation. In the long-distance stage, a task map and traffic negotiation are used, combined with UWB / RTK / laser reflectors, to provide meter- to decimeter-level guidance. The distribution center distributes local maps and spatiotemporal reservation windows through the open adapter layer, allowing the robot to obtain entry time, speed limit, and waiting point. In the mid-distance stage, visual targets, QR codes, LiDAR reflectors, and inertial measurement units are used for decimeter- to centimeter-level guidance. In the short-distance stage, optical flow, millimeter-wave radar, and force sensing are used for centimeter- to millimeter-level compliant docking. The system allows mechanical guidance, compliant grippers, floating contact points, and range compensation mechanisms to jointly eliminate errors. For non-standard docking... For robots of the same category, the docking and securing methods are as follows: For UAVs, the decision to open the cabin is first made based on the weather unit and flight path status. Then, RTK / UWB meter-level guidance, visual target decimeter-level guidance, and landing platform optical flow and force sensing centimeter-level alignment are used. After touching the ground, the locking mechanism secures the landing gear. For quadruped robots, foot positioning grids, pressure sensors, and attitude detection are used to determine whether the robot is standing firmly. Lateral flexible limiters to prevent tipping are only activated when the forces on the four legs are balanced and the body attitude meets the threshold. For wheeled robots, guide grooves and wheel limit blocks work together to align the contact points and complete the mechanical stop. For humanoid robots, foot positioning areas, waist or back flexible support, double-sided guardrails, and low-speed attitude verification processes are set up to ensure standing stability and prevent falls.

[0033] After the robot is fixed in the dock, a preset operation is performed on the robot according to the robot's subsequent task requirements and health status. After the preset operation is completed, a preset scheduling operation is performed on the robot.

[0034] Specifically, the pre-set operation includes at least several of the following: data unloading, health checks, power replenishment and battery replacement, and tool load replacement. Data unloading is completed via a high-speed wired interface, WiFi 6 / 7, or 5G. The system binds the collected images, point clouds, logs, and other data with the task number, robot number, timestamp, spatial coordinates, and checksum to form a trusted link. Health checks include appearance image detection, tire or foot rubber wear detection, screw loosening detection, joint temperature and motor current abnormality detection, sensor obstruction detection, and heat dissipation hole blockage detection. Cleaning and maintenance are performed through methods such as air blowing, contact wiping, and chassis dust removal. In the power replenishment and battery replacement operation, the system selects contact charging, wireless charging, fast charging, slow charging, equalization charging, or battery replacement according to the robot's task requirements and battery health status. Safety interlock calibration is performed before battery replacement. The system checks the robot's emergency stop status, main power cut-off status, battery latch release status, target battery temperature, target battery voltage, and the clamping force of the transfer mechanism. If any condition is not met, the battery swap will not be performed. When changing the tool load, the transfer mechanism selects the fixture according to the robot's passport and takes out the target module from the tool or load compartment. This module includes the inspection gimbal, infrared thermal imager, acoustic fingerprint collector, gas sensor, cleaning brush, sampling bottle, fire sprinkler, agricultural end effector, etc. The replacement is completed through mechanical positioning pins, quick-change latches, and data handshake. After the replacement, a functional self-test is performed, such as infrared thermal imager shutter calibration and acoustic fingerprint sensor noise baseline test.

[0035] Specifically, based on the latest task queue, robot availability status, and digital twin status, the robot is reassigned to perform the next task or transferred to the standby area to wait. If an unqualified health status, abnormal battery, or fault code is detected, the robot is guided to an isolated berth or the manual maintenance area is notified. Before reassignment, a final confirmation is performed, including battery lock confirmation, tool lock confirmation, sensor online confirmation, communication online confirmation, map version consistency confirmation, and safety rule loading completion confirmation. If any confirmation fails, the robot will not leave the distribution center. For robots that have completed recharging and equipment replacement and passed the health test, the system combines task priority and the capability map in the robot passport to automatically generate the next round of task assignment instructions, update the task status and digital twin status in the robot passport, and control the entry module and berth module to release the robot so that it leaves the distribution center to perform a new task. For robots in standby or isolated positions, the system continuously monitors their status and adds them to the scheduling queue when the conditions are met, thereby completing a cross-mode collaborative scheduling closed loop from returning to port, docking, operation to reassignment.

[0036] This embodiment presents a cross-morphological collaborative scheduling method for multiple robot types. It uses a robot passport to structurally encapsulate multi-dimensional information such as robot type, shape envelope, interface type, battery parameters, communication protocol, and task status. This allows the distribution center to establish a unified information model for robots of different shapes, such as drones, quadruped robots, wheeled robots, and mobile operation robots. Based on the robot type and shape envelope in the passport, and combined with real-time berth occupancy information, a compatibility score is generated, automatically selecting berths that match in physical size, mechanical interface, and electrical parameters. This fundamentally solves the problem of multiple robot types being unable to automatically match berths due to differences in mechanical interfaces and electrical parameters, eliminating the reliance on manual intervention.

[0037] Building upon compatibility scores, the system further integrates the robot's remaining battery power, task urgency, and expected waiting time for dock allocation. This technological feature allows the scheduling system to move beyond static compatibility and dynamically balance factors such as the robot's current energy crisis (low battery), task timeliness (urgent tasks), and queuing expectations (waiting time). For example, for a drone with nearly depleted battery power, even if its compatibility score is slightly lower than a certain dock, the system can prioritize allocating it to the nearest and fastest docking dock, thus avoiding operational interruptions or equipment damage. This mechanism significantly improves the robustness and task completion rate of heterogeneous robots during long-term unattended operation. Furthermore, based on the type of target dock, such as a lifting platform, charging station, or transshipment station, and the robot's interface type, a corresponding multi-stage docking guidance strategy is adopted. Unlike traditional single guidance modes (such as those applicable only to drones or wheeled robots), this system can perform differentiated actions such as coarse positioning, fine alignment, and mechanical locking in stages for different motion modes, such as aircraft (requiring landing accuracy control), legged robots (requiring standing stability control), and wheeled robots (requiring path following control). By employing a phased and switchable guidance logic, the system effectively solves cross-morphological docking challenges such as preventing quadruped robots from tipping over, preventing drones from landing in crosswinds, and maintaining the center of gravity of humanoid robots, thus achieving safe and reliable mechanical fixation and electrical docking.

[0038] After being docked and secured, the robots perform pre-set operations based on their subsequent task requirements and health status, such as recharging, data unloading, payload replacement, and software upgrades. This transforms the distribution center from a passive charging station or helipad into an intelligent node with proactive service capabilities. For example, for mobile robots about to perform delicate operations, the end effector can be proactively replaced or hydraulic oil replenished; for drones that have completed inspections but have not uploaded data, high-speed data unloading is automatically triggered. This on-demand, status-based operation mode significantly improves the continuous combat capability and task flexibility of various robot types. Furthermore, after completing the pre-set operations, pre-set scheduling operations are executed, such as wake-up, redeployment, standby / hibernation, and relocation to other distribution centers. This embodiment achieves a complete automated closed loop from robot return request to re-departure, without human intervention. Heterogeneous robots can automatically complete a series of operations such as recharging, reloading, data interaction, and redeployment, meeting the continuous operation requirements in long-term unattended scenarios. Furthermore, since the robot passport adopts an scalable data structure design, robots from different manufacturers and models only need to provide their own attribute information in a standard format to be identified and scheduled in a centralized manner. It can be seen that this embodiment also has good openness and compatibility, avoiding the high cost of developing an adapter interface for each type of robot, and providing a feasible solution for the unified infrastructure deployment of large-scale heterogeneous robot clusters.

[0039] In summary, this embodiment achieves unified information representation through robot passports, optimizes berth allocation through dynamic compatibility scoring, ensures safe and secure docking through multi-stage docking guidance, and completes the return-to-port closed loop with health status perception and full-process operation scheduling. It realizes cross-morphological collaborative scheduling for multiple types of robots (such as drones, legged, wheeled, and manipulators), solving the fundamental problems of existing technologies such as lack of unified infrastructure, reliance on manual intervention, and inability to automatically complete a series of operations after returning to port. It improves the autonomous collaborative capability and long-term operating efficiency of heterogeneous robot clusters in complex scenarios.

[0040] Optionally, the method further includes, prior to responding to the robot's request instruction: The request message sent by the robot is received through the open adapter layer. The request message includes the robot's identity, current location, current battery level, and request type identifier. The request type identifier is used to indicate whether the request instruction is a return to port request or a dispatch request.

[0041] Specifically, the edge controller at the distribution center receives request messages sent by the robot through an open adapter layer. This open adapter layer is configured to be compatible with ROS2, Open-RMF, MQTT, HTTP, OPC UA, or vendor-specific protocols, enabling the unified conversion of communication protocols from different robots into the distribution center's internal message format. The request message includes at least the robot's identification information, such as a unique robot ID or serial number, its current location (using GPS coordinates, UWB coordinates, or pose in the distribution center's local coordinate system), its current battery level (expressed as a percentage or remaining capacity), and a request type identifier. The request type identifier indicates whether the request is a return-to-port request or a dispatch request. When the robot needs to return to the distribution center for recharging, refitting, data unloading, or health checks, it sends a return-to-port request to the distribution center through its own communication module, with the request type identifier set to "return-to-port." When the robot has completed its current task and is ready to accept a new task and leave, it sends a dispatch request, with the identifier set to "dispatch." Upon receiving a message, the open adapter layer first checks its local database for a robot passport based on the robot's identifier. If a passport exists, the system updates the corresponding passport record with the real-time information (current location, current battery level) from the request message and triggers subsequent scheduling procedures. If a passport does not exist and the request type is "return to port," the system initiates the robot passport registration process. It collects the robot's shape envelope, approach angle, braking distance, communication quality, and power handshake results using a low-speed guidance mode. Information such as robot category, size, interface type, battery parameters, communication protocol, and task capabilities are supplemented through manual input, import from the manufacturer's interface, or QR code / NFC reading. A complete robot passport is then generated and stored in the edge controller. For dispatch requests, after receiving the message, the system also needs to consider the task status, health status, and current task queue at the distribution center in the robot passport to determine if dispatch conditions are met. If the robot has not yet completed data unloading or health checks for the previous task, the dispatch request is not responded to; instead, the robot is guided to the waiting area or continues executing unfinished tasks.

[0042] In this optional embodiment, the real-time status update mechanism upon successful retrieval synchronously writes information such as the current location and current battery level from the request message into the passport status field. This ensures that the robot passport is not only a static configuration file but also a dynamically updated real-time status carrier. This keeps parameters such as remaining battery level and location information, upon which subsequent compatibility score calculations depend, always up-to-date, avoiding berth mismatches or energy misjudgments caused by status lag. Secondly, the low-speed guided feature acquisition process initiated after a failed retrieval actively issues low-speed movement commands from the distribution center and automatically acquires key parameters such as shape envelope, approach angle, and braking distance using the vision, laser, force sensing, and communication handshake units of the entry module. This avoids subjective errors and safety hazards caused by manual measurement and also evaluates the robot's motion controllability and communication reliability in real time during the acquisition process. If the robot experiences an anomaly during low-speed guidance, such as braking failure... In the event of a failure or communication interruption, the system can immediately suspend registration and guide the robot to a safe area, thus minimizing the risk of initial access for unknown robots. Furthermore, the registration process uses the completion of a standard docking maneuver as a baseline, ensuring that the generated new passport inherently contains performance data of the robot's actual docking operation, such as whether the approach angle meets the ramp design requirements and whether the braking distance is within the berth's safe range. This provides personalized parameter basis for the multi-stage guidance strategy during the robot's first formal docking, improving the success rate of the first docking. Finally, by binding and storing the identification identifier with the passport, the system achieves adaptive management of robot population changes. When a new robot model appears during long-term operation at the distribution center, without requiring downtime for configuration or software upgrades, the system can automatically complete registration and be included in the scheduling system upon the first return-to-port request, ensuring the continuous operation capability of multi-type robot clusters in dynamically expanding scenarios.

[0043] Optionally, obtaining the robot's robot passport in response to the robot's request instruction includes: The robot passport is retrieved based on the identity identifier carried in the request message; if the retrieval is successful, the robot passport is read. If the search fails, the robot's feature parameters are collected to generate a new robot passport, which is then stored.

[0044] Specifically, the edge controller first retrieves the robot passport from its local database based on the identity identifier carried in the request message. The identity identifier is the robot's unique ID or serial number, which is recorded and bound to the passport when the robot first connects. If the retrieval is successful, meaning a passport record corresponding to the identity identifier already exists in the local database, the edge controller directly reads the robot passport and extracts information such as robot category, shape envelope, interface type, battery parameters, communication protocol, and task status for subsequent compatibility score calculation and parking space allocation. At the same time, it updates the current location, current battery level, and other real-time information from the request message to the corresponding status field of the passport. If the retrieval fails, meaning the record for that identity does not exist in the local database, indicating that the robot has not yet been registered at the distribution center, the system will initiate the robot passport registration process to generate a new robot passport. First, the robot's characteristic parameters are collected using a low-speed guidance mode. The distribution center then issues low-speed movement commands to the robot through the open adapter layer, guiding it to complete a standard docking maneuver within a safe area. During this process, the system automatically collects the robot's shape envelope, such as length, width, height and their variation range, approach angle and braking distance (reflecting the robot's kinematics and braking performance), communication quality (packet loss rate, latency, signal-to-noise ratio), and charging handshake results (charging interface type, voltage level, and whether the communication protocol is valid). (Matching); Simultaneously, information such as robot category, chassis size, weight and center of gravity height, kinematic constraints, hill-climbing and obstacle-crossing capabilities, battery model and voltage level, charging interface, communication protocol, tool interface, sensor configuration, task capabilities, prohibited actions, safe speed, emergency stop channels, and maintenance cycle are supplemented through manual input, manufacturer interface import, or QR code / NFC reading. The edge controller encapsulates the collected and input feature parameters in a structured manner, generating a complete robot passport according to a predefined format. This passport includes at least the robot's unique identifier, robot category, shape envelope, interface type, battery parameters, communication protocol, and task status. The passport is stored in the local database, and a corresponding capability map and interface dictionary are generated for subsequent scheduling. After the new passport registration is completed, the system uses this passport as the robot passport and continues to execute subsequent berth compatibility score calculation, target berth allocation, and docking guidance processes.

[0045] In this optional embodiment, for robots already registered at the distribution center, existing passports are quickly retrieved and read using their identity identifiers. Simultaneously, real-time information such as current location and current battery level from the request message is updated to the passport status field, avoiding duplicate registration and data redundancy, and ensuring the consistency and real-time nature of the robot's status. Secondly, for unregistered new robots, the system automatically initiates the passport registration process. It collects the robot's shape envelope, approach angle, braking distance, communication quality, and power handshake results through a low-speed guided mode. This data is then combined with manual input, manufacturer interface import, or QR code / NFC reading to supplement information such as robot category, size, interface type, battery parameters, communication protocol, and task capabilities, generating a complete robot passport and storing it in the edge controller. The mechanism enables automated access capabilities of "registering immediately upon arrival, without pre-configuration," significantly shortening the new robot access cycle from several days to weeks in traditional solutions to several hours to one day. This significantly reduces the engineering complexity and operation and maintenance costs of accessing distribution centers for robots from different manufacturers and with different forms. Finally, regardless of whether the retrieval is successful or a new passport is generated after a retrieval failure, the system outputs complete robot information (including robot category, shape envelope, interface type, battery parameters, communication protocol, and task status) with a unified passport data structure. This provides a reliable data foundation for subsequent compatibility score calculation, target berth allocation, multi-stage docking guidance, and operation scheduling, thereby ensuring the efficient collaboration and scalability of heterogeneous robot clusters in long-term unattended operation.

[0046] Optionally, the step of generating a compatibility score between the robot and each berth based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, includes: The first score is determined based on the robot category, the shape envelope, the category requirements of the berth, and the size limits of the berth. The second score is determined based on the interface type, the communication protocol, and the interface configuration supported by the berth. Based on the battery parameters and the current location carried in the request message, the energy reachability score for the robot to reach the berth is determined. Based on the task status and the real-time occupancy information of the berth, determine the available score of the berth for the robot; A safety risk score is determined based on the robot category and the current battery level carried in the request message; Based on the queuing situation at the berths, a waiting time score is determined; The first score, the second score, the energy reachability score, and the available score are added together, and the safety risk score and the waiting time score are subtracted to obtain the compatibility score between the robot and the berth.

[0047] Specifically, the edge controller first reads the robot category and shape envelope parameters from the robot passport, including length, width, height, and the envelope range during movement. Simultaneously, it retrieves the berth category requirements recorded in the self-description file of each berth, such as the berth's design for drone landing, quadruped robot standing, wheeled robot docking, or humanoid robot stabilization, as well as dimensional limits—the maximum length, width, height, and movement space the berth can accommodate. The controller matches the robot category with the berth category: if the robot belongs to a category supported by the berth design and its shape envelope is completely within the berth's dimensional limits, the first score is set to 1.0; if the category matches but the shape envelope exceeds the limits by a certain percentage, for example, less than 10%, the score is linearly reduced according to the excess percentage; if the category does not match or the shape severely exceeds the limits, the first score is set to 0.

[0048] The controller compares the interface types (mechanical locking interface type, charging interface type and voltage level, communication protocol type) in the robot passport with the interface configuration supported by the parking space. For mechanical interfaces, it checks whether the adjustable grips, guide cones, or locking slots of the parking space are compatible with the robot's landing gear, chassis, or foot structure. For electrical interfaces, it verifies whether the voltage level (e.g., 12V, 24V, 48V) and charging method (contact or wireless) provided by the parking space match the robot's battery parameters. For data interfaces, it confirms whether the parking space's protocol gateway supports the communication protocols declared in the robot passport (e.g., ROS2, CAN, MQTT, etc.). If all three are perfectly matched, the second score is 1.0; if only partially matched, such as electrical and data matching but mechanical matching requiring adapter board replacement, the score is adjusted based on the time and cost required for adaptation, typically ranging from 0.3 to 0.7; if completely mismatched, the score is 0.

[0049] The controller calculates the energy reachability score for the robot to reach the berth based on the robot's current position carried in the request message and the kinematic constraints and unit energy consumption parameters in the robot's passport. It plans the shortest feasible path from the current position to the berth entrance, sums the estimated energy consumption of each segment of the path, and obtains the total energy consumption. The remaining battery power of the robot is subtracted from the safety reserve energy and compared with the total energy consumption. If the remaining available energy is greater than 1.2 times the consumed energy (leaving a margin), the energy reachability score is 1.0; if it is between 1.0 and 1.2 times, the score is 0.8; if it is between 0.8 and 1.0 times, the score is 0.5; if it is less than 0.8 times, the score is 0, and low-battery priority scheduling is triggered.

[0050] The controller determines the available score based on the task status in the robot's passport, such as the remaining task time, task priority, and real-time occupancy information of the berth (idle, occupied but with estimated release time, under maintenance). If the berth is idle and all integrated operation modules (power replenishment, equipment replacement, and testing) are functioning normally, the available score is 1.0; if the berth is occupied but the remaining service time is short, such as less than 2 minutes, the available score is calculated inversely proportional to the remaining time, such as 0.9 for 1 minute remaining; if the berth is in a faulty or isolated state, the available score is 0.

[0051] Meanwhile, the controller calculates a safety risk score based on the robot category and current battery level. First, it calculates the dynamic safety radius using a formula: based on the robot's static radius, it adds the product of the current speed and braking delay, the product of positioning uncertainty and confidence coefficient, and finally adds an additional safety margin for the robot category. For example, drones have an additional 0.3 meters due to rotor risk, humanoid robots have an additional 0.5 meters due to fall risk, and quadrupedal and wheeled robots have an additional 0.1 meters. Next, it assesses the environmental risk coefficient based on the surrounding environment of the parking space, such as whether there are charging batteries nearby or whether it is a fire lane. The dynamic safety radius is then multiplied by the environmental risk coefficient and normalized to obtain a safety risk score between 0 and 1, with a higher score indicating a greater risk.

[0052] Furthermore, the controller calculates a waiting time score based on the queuing situation at the berth. It reads the number of robots currently in the waiting queue at that berth and their estimated service times. Each robot's service time consists of a fixed docking time, recharging or battery swapping time, health check time, and data unloading time; these are summed to obtain the estimated waiting time for the current robot. A baseline waiting time threshold is set, for example, 5 minutes. If the waiting time is less than the threshold, the waiting time score is 0; if it exceeds the threshold, the score increases linearly according to the excess time, up to a maximum of 1.0.

[0053] Finally, the first score, second score, energy reachability score, and availability score are added together, and then the safety risk score and waiting time score are subtracted to obtain the compatibility score between the robot and the berth. The controller iterates through all available berths and selects the berth with the highest compatibility score as the target berth. If the highest scores are the same, the berth with the lower safety risk score or the lower waiting time score is selected first.

[0054] In this optional embodiment, the compatibility score calculation scheme quantifies berth allocation decisions into a weighted combination of a first score (category and shape matching), a second score (interface and protocol matching), energy reachability score, availability score, safety risk score, and waiting time score. This achieves optimal berth selection under multi-objective constraints: on the one hand, it uniformly maps heterogeneous features such as mechanical interfaces, electrical parameters, and communication protocols into comparable numerical scores, transforming the originally difficult-to-automatically match cross-morphology robot docking problem into a quantifiable optimization problem, avoiding the subjectivity and inefficiency of human experience judgment; on the other hand, by introducing a dynamic safety radius (fusion... The system incorporates speed, latency, positioning uncertainty, and additional margins for robot categories, along with energy accessibility assessments (based on a fine comparison of path energy consumption and remaining battery power). This allows for the pre-emptive elimination of safety hazards and energy shortage risks during the allocation phase. Additionally, robots with low battery power can trigger priority scheduling to avoid job interruptions. Furthermore, the introduction of waiting time scores enables the system to dynamically adjust allocations based on berth queue status, preventing one berth from becoming overcrowded while others remain idle. The edge controller quickly sorts and selects the optimal berth based on compatibility scores, enabling the scheduling system to achieve adaptive collaboration among robots of different shapes while balancing safety, efficiency, energy, and task urgency.

[0055] Optionally, the step of allocating a target parking space to the robot based on the compatibility score, combined with the robot's remaining battery power, task urgency, and estimated waiting time, includes: Based on the remaining battery power, determine the robot's battery urgency level, and generate a battery correction factor based on the battery urgency level; Based on the urgency of the task, the task priority of the robot is determined, and an urgency correction factor is generated based on the task priority; Based on the estimated waiting time, the robot's waiting tolerance is determined, and a waiting time penalty factor is generated based on the waiting tolerance. The power correction factor, the urgency correction factor, and the waiting time penalty factor are weighted and combined with the compatibility score to obtain the correction score for each berth. The berths are sorted from highest to lowest according to the corrected score, and the berth with the highest corrected score in the sorting result is selected as the target berth, and the target berth is assigned to the robot.

[0056] Specifically, the edge controller first reads the robot's current remaining battery power (expressed as a percentage) and determines the battery urgency level based on a preset battery threshold range. For example, three thresholds are set: when the remaining battery power is above 40%, it is defined as low urgency, indicating that the robot has sufficient energy and requires no special care; when the remaining battery power is between 20% and 40%, it is defined as medium urgency, indicating that the robot needs to replenish its energy in a short period of time; when the remaining battery power is below 20%, it is defined as high urgency, meaning that the robot is in a dangerous state of impending power loss. Based on the battery urgency level, the controller generates a corresponding battery correction factor: the correction factor for low urgency is 1.0 (no extra points), the correction factor for medium urgency is 1.2 (moderately increases allocation priority), and the correction factor for high urgency is 1.5 (significantly increases priority), making it easier for low-battery robots to obtain high-scoring parking spaces.

[0057] Next, the controller determines the task urgency based on the task status field in the robot's passport. Task status includes remaining time (e.g., an inspection task must be completed in 10 minutes), task safety level (e.g., thermal anomaly detection of electrical equipment is high safety level, while routine material delivery is low safety level), and task value (e.g., emergency rescue tasks are more valuable than regular inspections). The controller categorizes task urgency into three levels based on these factors: ordinary tasks (no time pressure, low safety level) have an urgency coefficient of 1.0; urgent tasks (with time constraints or medium safety level) have a coefficient of 1.3; and extremely urgent tasks (very short remaining time, high safety level, or emergency rescue) have a coefficient of 1.6. Based on the task urgency, the controller generates an urgency correction factor, which is directly used in subsequent weighting.

[0058] The controller then determines the robot's waiting tolerance based on the estimated waiting time. The estimated waiting time is calculated by summing the service times of all robots in the current queue at the berth. Service time includes fixed docking time, recharging or battery swapping time, health check time, and data unloading time. The controller sets a tolerance baseline (e.g., 5 minutes): if the estimated waiting time is less than 2 minutes, the waiting tolerance is "high," and the waiting time penalty factor is set to 1.0 (no deduction); if the estimated waiting time is between 2 and 10 minutes, the tolerance is "medium," and the penalty factor is set to 0.9 (slight deduction); if the estimated waiting time exceeds 10 minutes, the tolerance is "low," and the penalty factor is set to 0.7 (significant deduction). This penalty factor reflects the negative effects of prolonged waiting, such as missing task windows or exacerbating energy depletion. After obtaining the battery level correction factor, urgency level correction factor, and waiting time penalty factor, the controller weights and combines these factors with the previously calculated compatibility score to calculate the corrected score for each berth. The specific combination method is as follows: first, multiply the compatibility score by the battery level correction factor, then by the urgency level correction factor, and finally by the waiting time penalty factor. That is, the corrected score = compatibility score × battery level correction factor × urgency level correction factor × waiting time penalty factor. This multiplicative combination allows robots with low battery and high urgency tasks to significantly improve their parking score, while long waiting times will reduce the score, achieving a comprehensive trade-off among multiple factors.

[0059] Finally, the controller sorts all available berths in the distribution center according to their correction scores from highest to lowest, selects the berth with the highest correction score as the target berth, and assigns it to the robot. If two berths have the same correction score, the berth with the lower safety risk score is prioritized, or the berth closer to the robot's current location is selected to reduce energy consumption. After allocation, the controller updates the berth status to "assigned," records the estimated arrival time and planned service time, and notifies the robot of the target berth's location information and docking route. Through the dynamic correction mechanism of power urgency level, task priority, and waiting tolerance, the system can prioritize robots with low power and performing high-safety-level tasks when multiple robots return to port simultaneously, while avoiding task delays caused by long berth queues.

[0060] In this optional embodiment, by quantifying the remaining power into three urgency levels—low, medium, and high—and assigning corresponding correction factors to each, the parking score of robots on the verge of power outage is significantly amplified, thereby automatically granting them priority allocation rights during scheduling. This effectively avoids work interruptions or equipment damage caused by energy depletion, and is particularly suitable for scenarios where mid-operational shutdowns are not permitted, such as power inspections and emergency rescues. Secondly, by determining the urgency coefficient based on the remaining task time, safety level, and task value (e.g., normal 1.0, urgent 1.3, and extremely urgent 1.6), robots carrying critical inspection data, such as substation thermal anomaly images, or performing high-safety-level tasks, can skip the normal queue and directly obtain high-scoring parking spaces, ensuring the continuity of high-value tasks and the timeliness of data. Thirdly, by introducing waiting time penalty factors (e.g., high tolerance 1.0, medium tolerance 0.9, and low tolerance...), With a score of 0.7, the system automatically suppresses the tendency to send robots to long queue berths, thereby balancing the load distribution among berths and preventing a berth from becoming a bottleneck due to historical task backlog. It also reduces the risk of low-battery robots consuming further energy while queuing. It maximizes the score when low battery, high urgency, and short waiting time are simultaneously met, upgrading the distribution center from a simple mechanical connection device to an intelligent hub with task-level operational capabilities. The cross-morphological collaborative scheduling system for multi-type robots in this embodiment no longer mechanically allocates berths according to first-come, first-served order, but can dynamically adjust the service order according to the energy crisis level and task time sensitivity of each robot. Ultimately, it significantly reduces the average refueling and reloading waiting time, while ensuring that robots with the highest task urgency are given priority to docking, significantly improving the overall task completion rate and resource utilization efficiency in multi-robot return-to-port scenarios.

[0061] Optionally, the multi-stage docking guidance strategy includes: a long-distance guidance strategy, a medium-distance guidance strategy, and a short-distance guidance strategy. The step of controlling the robot to complete docking and fixing using the corresponding multi-stage docking guidance strategy based on the type of the target berth and the interface type includes: The long-distance guidance strategy is executed as follows: an entry path is generated based on the location information of the target berth, and the entry path and speed limit are sent to the robot through the task map to guide the robot into the distribution center entrance area; The mid-range guidance strategy is executed as follows: The robot's current position is calculated based on the UWB tag signal received from a UWB base station; alternatively, the robot's positioning data is acquired via an RTK receiver; or, the robot's relative position is determined by emitting a laser beam from a lidar and receiving the echo reflected from the reflector on the robot. The current or relative position is compared with a preset alignment point of the target berth to generate a position deviation. This position deviation is then converted into a correction command and sent to the robot, guiding it towards the target berth. The short-range guidance strategy is executed as follows: A corresponding visual target is activated based on the type of the target berth; the relative pose of the robot and the visual target is acquired through a visual sensor; an alignment adjustment command is generated based on the relative pose to control the robot to gradually align with the positioning mechanism of the target berth; during the process of the robot contacting the positioning mechanism, contact force information between the robot and the positioning mechanism is acquired through a force sensor; the robot's movement speed and posture are adjusted based on the contact force information to guide the robot to contact the positioning mechanism. After the robot contacts the positioning mechanism, the corresponding locking method is selected according to the interface type, and the locking mechanism is triggered to mechanically fix the robot in combination with the locking method. The positioning status of the locking mechanism is detected by the sensor. When the locking mechanism is detected to be in place and the robot's attitude sensor feedback indicates a stable docking state, it is confirmed that the robot has completed docking and securing.

[0062] Specifically, when executing the long-distance guidance strategy, the edge controller first plans a safe docking path from the robot's current position to the entrance area of ​​the target berth based on the location information of the assigned target berth, including the berth's spatial coordinates and entrance channel number within the distribution center, combined with the distribution center's task map—a pre-constructed local map containing the entrance area, waiting area, berth area, and channel topology. The controller then sends this docking path and speed limits (e.g., no more than 0.5 m / s for wheeled robots and no more than 1 m / s for drones) to the robot through the open adapter layer. Simultaneously, the controller sends the spatiotemporal reservation window information for the distribution center's entrance area, including the time period the robot can enter, the waiting points where it must stop, and the abnormal exit points. The robot autonomously navigates to the distribution center's entrance area according to the sent path and speed limits, and reports its position to the distribution center upon reaching the waiting point, awaiting entry into the next stage. For drones, the long-distance stage also includes weather assessment: if the outdoor wind speed exceeds a preset threshold, such as level 5, the controller temporarily suspends cabin opening and instructs the drone to hover in the air or land at an alternative entrance.

[0063] When executing a mid-range guidance strategy, the edge controller selects one or more positioning methods based on the positioning infrastructure configured at the hub. If the hub has UWB base stations, the robot carries a UWB tag, and the controller receives tag signals from multiple base stations, calculating the robot's real-time position using time difference of arrival or angle of arrival methods, with an accuracy down to the decimeter level. If the hub has RTK base stations, the robot carries an RTK receiver, and the controller acquires centimeter-level positioning data from the robot. If the hub has LiDAR and reflectors, the robot is equipped with a laser reflector, and the controller determines the robot's angle and distance relative to the reflector by emitting a laser beam from the LiDAR and receiving the echo. The controller compares the calculated current position of the robot with the preset alignment point of the target berth (which is preset 0.5 to 1 meter in front of the entrance depending on the berth type), calculating the position deviation, including lateral deviation, longitudinal deviation, and heading angle deviation. Subsequently, the controller converts the position deviation into a speed correction command, such as "shift left by 0.1 meters, advance by 0.3 meters / second," and sends it to the robot through the open adapter layer, guiding the robot to gradually approach the target berth. During this process, the controller continuously monitors the robot's motion status. If the robot deviates from the path or exceeds the speed limit, a correction command is reissued. For quadruped robots, the mid-distance phase also requires the robot to report its body posture (pitch angle, roll angle), and the controller determines whether it is suitable to enter the ramp.

[0064] When executing a near-field guidance strategy, the edge controller activates the corresponding visual target based on the type of the target parking space. For example, for a drone parking space, a QR code or illuminated pattern target on the landing platform is activated; for a wheeled robot parking space, a guide line or QR code target on the ground is activated; and for a quadruped robot parking space, a visual marker in the foot positioning grid area is activated. The robot's onboard visual sensors, such as fisheye or downward-facing cameras, capture images of the visual targets. The controller uses image processing algorithms to calculate the relative pose between the robot and the target, including lateral offset, longitudinal distance, and rotation angle. Based on the relative pose, the controller generates alignment adjustment commands, such as "move 5 cm to the left, move 10 cm forward, rotate 2 degrees," controlling the robot frame-by-frame to gradually align with the positioning mechanism of the target parking space, such as the center of the drone's landing platform, the guide slot entrance for the wheeled robot, or the foot positioning grid for the quadruped robot. During the robot's contact with the positioning mechanism, distributed force sensors, such as pressure sensors, contact sensors, or torque sensors, collect real-time contact force information between the robot and the positioning mechanism. The controller dynamically adjusts the robot's speed and posture based on contact force information: for example, when the contact force in a certain direction exceeds a threshold, it immediately reduces the speed in that direction or issues a reverse fine-tuning command; for drones, when the landing gear touches the ground, the force sensor detects the impact force, and the controller immediately commands the drone to stop rotating its rotors and lock them; for humanoid robots, when the soles of the feet contact the positioning area, the controller determines whether the robot is stable based on the pressure distribution on the soles of the feet, and if the pressure on one side is too high, it issues a center of gravity adjustment prompt. Through this compliant docking method, the system allows mechanical guidance, gripper floating, and range compensation to jointly eliminate the effects of sensor errors and uneven ground.

[0065] After the robot contacts the positioning mechanism, the edge controller selects the corresponding locking method based on the interface type. For drones, the locking method is an electromagnetic or mechanical locking mechanism to secure the landing gear; for wheeled robots, the locking method is a wheel limit block or electromagnetic stop to hold the wheels; for quadruped robots, the locking method is a magnetic or clamping device within the foot positioning grid; for humanoid robots, the locking method is the closure of the double-sided guardrails and the extension of the flexible waist support. The controller triggers the corresponding locking mechanism action and detects the locking mechanism's positioning status through sensors such as proximity switches, limit switches, or Hall effect sensors. For example, if the locking claws are detected to be fully closed, the limit blocks to be ejected, or the guardrails to be locked, then the locking is confirmed to be successful.

[0066] When the locking mechanism is detected in place and the robot's attitude sensors indicate a stable docking state (e.g., a drone's attitude angle is less than 1 degree and there is no vibration, a quadruped robot's four legs are evenly stressed and its body is level, and a wheeled robot's chassis is not slipping), the controller confirms that the robot has successfully docked and updates the docking status to "secured." The system then records the docking completion information to the log and notifies subsequent operation modules (data unloading, health checks, and refueling / reloading) to prepare for the preset operation. If the locking or stability conditions are not met, the system automatically repeats the locking action or issues an alarm, and determines whether to initiate a safety isolation procedure based on the fault type.

[0067] In this optional embodiment, during the long-range phase, the docking path and speed limit are issued via the mission map, and weather judgments are added to the UAV (such as temporarily suspending cabin opening when the wind speed exceeds the threshold), allowing the robot to smoothly transition from field operation to a controllable area of ​​the distribution center, avoiding the safety risks of forced landing of the UAV in severe weather; during the mid-range phase, multiple positioning methods such as UWB, RTK, and LiDAR reflectors are integrated, and can be flexibly selected or switched according to different scenarios, so that the positioning accuracy gradually converges from the meter level to the centimeter level. At the same time, attitude reporting and verification are added to the quadruped robot to ensure that it enters with a suitable attitude for the ramp, solving the problem of positioning failure or insufficient accuracy of traditional single guidance methods on heterogeneous robots; during the close-range phase, differentiated visual targets (UAV QR code, wheeled guide lines, quadruped foot positioning grids) are activated according to the berth type, and frame-by-frame alignment instructions are generated through visual pose calculation, combined with force sensing. The device monitors contact force in real time and dynamically adjusts movement speed and attitude. For example, it immediately stops and locks when the drone touches the ground, and issues a center of gravity adjustment prompt when the humanoid robot's foot pressure distribution is abnormal. This embodiment significantly improves the docking success rate through a dual closed-loop mechanism of visual coarse alignment and force-sensory compliant fine adjustment, reducing the risk of docking failure due to sensor noise, uneven ground, or braking errors. Finally, corresponding locking methods are selected for different robot types (drone landing gear locking, wheel limit blocks, quadruped foot magnetic attraction, humanoid protective railing closure, and waist support), and the stable docking state is confirmed by both positioning and attitude sensors, ensuring the mechanical safety of various robots during recharging, changing, and testing operations. In particular, anti-fall support and foot pressure equalization verification are added for humanoid robots, effectively solving the problem of stability of high-center-of-gravity robots in unattended environments.

[0068] Optionally, the preset operation includes: performing at least one of the following operations on the robot according to the subsequent task requirements and the health status: energy replenishment operation, battery swapping operation, tool replacement operation, load replacement operation, data unloading operation, health detection operation, and cleaning and maintenance operation; The preset scheduling operation includes: after the preset operation is completed, the robot is redeployed, transferred to the standby area, or transferred to the isolation maintenance area according to the subsequent task requirements and the health status.

[0069] Specifically, regarding the preset operation: After the robot completes docking and the locking mechanism is confirmed to be in place, the edge controller selects at least one operation from the following to execute: power replenishment operation, battery swapping operation, tool replacement operation, load replacement operation, data unloading operation, health detection operation, and cleaning and maintenance operation, based on the robot's subsequent task requirements (determined by the next task assigned to the robot in the task queue) and health status (assessed in real time by the health detection module).

[0070] Data unloading operation: The edge controller first establishes a data channel with the robot via a wired high-speed interface (such as Gigabit Ethernet) or wireless communication (WiFi 6 / 7 or 5G) to download images, point clouds, acoustic signatures, temperature records, and system logs collected during the task from the robot's memory. During the download process, the system generates a task number, robot number, sensor number, timestamp, spatial coordinates (from the robot's positioning trajectory), and checksum for each batch of data, forming a reliable data link to facilitate subsequent inspection report generation, fault tracing, and algorithm training. After data unloading is complete, the controller sends a confirmation command to the robot, which can then release its local storage space.

[0071] Health Detection Operation: The edge controller controls the health detection module to perform centralized checks on the robot. The checks include: capturing images of the robot's exterior using cameras to identify lens contamination, LiDAR obstruction, shell damage, or loose screws; measuring tire tread depth or quadruped foot rubber thickness using wear detection devices such as laser profilometers or contact displacement sensors; detecting temperature distribution in joints, motors, and battery packs using thermal imaging cameras or temperature sensors; monitoring abnormal fluctuations in motor current for each joint using current sensors; and testing the stability of the positioning module and wireless communication. The detection data is compared with the maintenance cycles and standard thresholds recorded in the robot's passport to generate a health status report. If abnormalities are detected, such as lens contamination, excessive foot wear, or abnormal battery temperature rise, the system automatically records the fault type and severity level.

[0072] Cleaning and maintenance operations: Based on health monitoring results, the system can trigger cleaning and maintenance. For example, if the lens or sensor is obstructed, the air blowing device (high-pressure airflow to blow away the lens surface) or wiping mechanism (sponge or non-woven fabric roller) will be activated; if the charging contacts are oxidized or dirty, the oxides will be removed by the contact cleaning brush or electrochemical cleaning method; if dust or dirt accumulates on the robot chassis, it will be cleaned using the suction port or rotating brush; in medical or food scenarios, the ultraviolet lamp or atomizing disinfection module can also be activated to disinfect the robot surface.

[0073] Recharging and Battery Swapping Operations: The edge controller determines whether to use charging or battery swapping based on the robot's battery parameters (battery model, voltage level, and capacity recorded in the passport) and the required range for subsequent tasks. If charging is selected, the controller adjusts the charging voltage and current of the parking space to the range allowed by the robot's battery (supporting automatic switching between multiple voltage levels such as 12V, 24V, and 48V), and initiates contact charging (via floating contact docking) or wireless charging (via electromagnetic induction). During the charging process, the controller monitors the battery temperature, voltage, and current curves in real time. If bulging, excessively rapid temperature rise, or abnormal charging curves are detected, the power supply is immediately cut off and safety isolation is activated. If battery swapping is selected, the controller calls the multi-degree-of-freedom transfer mechanism and first executes the battery swapping safety interlock: simultaneously verifying the robot's emergency stop status (pressed), main power cut-off status (disconnected), battery latch release status (unlocked), target battery temperature (below 45℃) and voltage (matching robot parameters), and the transfer mechanism's clamping force (within the specified range). Once all conditions are met, the transfer mechanism removes the old battery from the robot's battery compartment and places it in an empty slot for charging or health assessment. Simultaneously, it removes a fully charged and healthy battery of the same model from the battery compartment, inserts it into the robot via mechanical positioning pins and a quick-change interface, and locks the battery latch. After the battery swap is complete, the controller updates the battery parameters and remaining power in the robot's passport and records the battery usage log.

[0074] Tool and payload replacement operations: If a subsequent task requires replacement of the end-effector tool or payload, the edge controller queries the inventory status of the tool and payload bins, selects the target tool (e.g., inspection pan-tilt unit, infrared thermal imager, acoustic signature sensor, gas sensor, cleaning brush, sampling bottle, fire sprinkler head, agricultural end effector, etc.) or the target payload (e.g., cargo box, sample box, emergency supply kit). The transfer mechanism selects the appropriate gripper based on the tool interface type in the robot passport, moves it to the slot where the target tool is located, and grasps the tool using visual positioning and force guidance. Then, the transfer mechanism moves to the robot's tool quick-change interface, aligns and inserts it, and locks it with the quick-change lock, completing the data handshake (automatically identifying the tool model and function, and loading the corresponding driver). After replacement, the system performs functional self-checks: for example, the infrared thermal imager performs shutter calibration, the acoustic signature sensor tests the noise baseline, and the gripper performs opening and closing tests. After the tool or payload replacement is completed, the controller updates the current tool configuration and task capability map in the robot passport.

[0075] Regarding preset scheduling operations: After the preset task operation is completed, the edge controller will perform one of the following scheduling operations based on the robot's subsequent task requirements and health status.

[0076] Re-dispatch operation: If the robot's health status is "qualified" (i.e., all checks have passed or minor abnormalities do not affect task execution), and there are pending tasks in the subsequent task queue, the controller adds the robot to the re-dispatch queue. Final confirmation before re-dispatch: Confirm that the battery is locked in place, the tool is locked in place, sensors are online (e.g., cameras and LiDAR can collect data normally), communication is online (stable connection with the controller), the map version is consistent with the task area, and safety rules have been loaded. After all confirmations are passed, the controller opens the entrance gate, issues the task target point and path planning, and instructs the robot to leave the distribution center to execute the new task. The task status in the robot's passport is updated to "in progress," and the departure time is recorded.

[0077] Transfer to Standby Area Operation: If the robot's health status is satisfactory, but there are currently no tasks to be performed (e.g., the task window has not yet arrived or the system load is low), the controller will guide the robot to the standby area berth in the distribution center. The standby area berth provides basic charging maintenance (low-power trickle charging) and data heartbeat maintenance, and the robot enters a low-power standby state. When a new task arrives, the controller wakes up the robot from the standby area according to the nearest or most suitable principle and initiates the re-dispatch process.

[0078] Transfer to the isolation maintenance area: If a serious fault is detected during health checks, such as battery bulging, severely abnormal joint motor current, foot or tire wear exceeding safety thresholds, permanent sensor failure, or abnormal posture or communication interruption during docking / operation, the controller marks the robot as "faulty" and guides it to the isolation maintenance area berth. The isolation maintenance area is physically separated from the normal operation area, such as by fireproof partitions or a separate compartment. The system cuts off the robot's recharging channel and notifies maintenance personnel or verifies the issue remotely. If the fault can be repaired remotely, such as through software reset or calibration parameter updates, the system may attempt automatic repair. If manual intervention is required, the fault details are recorded in the safety audit log, and maintenance prompts are displayed on the personnel terminal. After isolation, the robot will not participate in subsequent task assignments until the fault is resolved and it passes the health check again.

[0079] In this optional embodiment, the task number, timestamp, spatial coordinates, and verification value binding mechanism in the data unloading operation ensures the traceability of data collected by multiple robots, avoiding data confusion and sample contamination in subsequent algorithm training. The health detection operation centrally checks for external wear, joint temperature rise, sensor obstruction, and communication stability, generating a health status report that enables the system to accurately identify robots requiring maintenance. Combined with air blowing, wiping, vacuuming, or disinfection in cleaning and maintenance operations, this effectively extends the lifespan of robot sensors and moving parts, reducing the probability of sudden failures. During the recharging operation, the voltage level is automatically switched according to battery parameters, and the charging curve is monitored in real time. Before the battery swapping operation, multiple safety interlock checks are performed, including emergency stop, main power disconnection, battery temperature and voltage, and clamping force of the transfer mechanism. This reduces the risk of failures caused by hot-plugging, mis-clamping, or battery thermal runaway. Safety incidents; tool and load replacement operations are handled through quick-change latches, data handshakes, and post-replacement functional self-checks, such as infrared shutter calibration and gripper opening and closing tests, ensuring the reliability and functional integrity of the task module before departure, enabling the same robot to quickly switch between different capability forms according to task requirements; finally, the final confirmation before redeployment includes battery locking, tool locking, sensor online status, communication online status, map version consistency, and safety rule loading, thereby further preventing the risk of robots departing in poor condition or not being ready. The low-power maintenance of the standby area and the physical isolation and remote diagnostic mechanism of the isolation maintenance area enable the distribution center to have robot population-level health management and task flexibility. Healthy robots continue to operate, and faulty robots automatically exit and notify maintenance, significantly improving the continuous operation capability and system safety of multiple types of robots in unattended scenarios.

[0080] Combination Figure 2 As shown, the cross-morphological collaborative scheduling system for multi-category robots according to an embodiment of the present invention includes: The acquisition unit is used to acquire the robot's robot passport in response to the robot's request command. The robot passport includes robot category, shape envelope, interface type, battery parameters, communication protocol and task status. The berth allocation unit is used to generate a compatibility score between the robot and each berth based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center. Based on the compatibility score and in combination with the robot's remaining battery power, task urgency, and estimated waiting time, the unit allocates a target berth to the robot. The docking guidance unit is used to control the robot to complete docking and fixing according to the type of the target berth and the interface type, using a corresponding multi-stage docking guidance strategy. The job scheduling unit is used to perform preset job operations on the robot after the robot is fixed in the dock, based on the robot's subsequent task requirements and health status, and to perform preset scheduling operations on the robot after the preset job operations are completed.

[0081] The cross-morphological collaborative scheduling system for multi-type robots of the present invention has the same advantages over the prior art as the cross-morphological collaborative scheduling method for multi-type robots described above, and will not be repeated here.

[0082] Combination Figure 3 As shown, the electronic device of this embodiment includes: a processor and a memory, wherein the memory is used to store computer programs; When the computer program is loaded by the processor, it causes the processor to execute the aforementioned cross-morphological cooperative scheduling method for multi-category robots.

[0083] The electronic device of the present invention has the same advantages over the prior art as the cross-morphological collaborative scheduling method for multi-type robots described above, and will not be repeated here.

[0084] The computer-readable storage medium of this invention stores a computer program thereon, which, when executed by a processor, implements the above-described cross-morphological cooperative scheduling method for multi-category robots.

[0085] The computer-readable storage medium of the present invention has the same advantages over the prior art as the cross-morphological cooperative scheduling method for multi-type robots described above, and will not be repeated here.

[0086] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A cross-morphological cooperative scheduling method for multi-category robots, characterized in that, include: The request message sent by the robot is received through the open adapter layer. The request message includes the robot's identity, current location, current battery level, and request type identifier. The request type identifier is used to indicate whether the request instruction is a return to port request or a dispatch request. In response to the robot's request command, the robot passport is obtained, which includes robot category, shape envelope, interface type, battery parameters, communication protocol and task status; Based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center, a compatibility score between the robot and each berth is generated. Specifically, this includes: determining a first score based on the robot category and shape envelope and the berth's category requirements and size limits; determining a second score based on the interface type and communication protocol and the interface configuration supported by the berth; and determining the robot's energy reachability score for reaching the berth based on the battery parameters and the current location carried in the request message. Based on the task status and the real-time occupancy information of the berth, the availability score of the berth for the robot is determined; based on the robot category and the current battery level carried in the request message, a safety risk score is determined; based on the queuing situation of the berth, a waiting time score is determined; the first score, the second score, the energy availability score, and the availability score are added together, and the safety risk score and the waiting time score are subtracted to obtain the compatibility score between the robot and the berth; based on the compatibility score and combined with the robot's remaining battery level, the urgency of the task, and the estimated waiting time, a target berth is assigned to the robot; Based on the type of the target berth and the type of the interface, a corresponding multi-stage docking guidance strategy is used to control the robot to complete docking and fixation. After the robot is fixed in the dock, a preset operation is performed on the robot according to the robot's subsequent task requirements and health status. After the preset operation is completed, a preset scheduling operation is performed on the robot.

2. The cross-morphological cooperative scheduling method for multi-category robots according to claim 1, characterized in that, The step of obtaining the robot's robot passport in response to the robot's request instruction includes: The robot passport is retrieved based on the identity identifier carried in the request message; if the retrieval is successful, the robot passport is read. If the search fails, the robot's feature parameters are collected to generate a new robot passport, which is then stored.

3. The cross-morphological cooperative scheduling method for multi-category robots according to claim 1, characterized in that, The process of allocating target parking spaces to the robot based on the compatibility score, combined with the robot's remaining battery power, task urgency, and estimated waiting time, includes: Based on the remaining battery power, determine the robot's battery urgency level, and generate a battery correction factor based on the battery urgency level; Based on the urgency of the task, the task priority of the robot is determined, and an urgency correction factor is generated based on the task priority; Based on the estimated waiting time, the robot's waiting tolerance is determined, and a waiting time penalty factor is generated based on the waiting tolerance. The power correction factor, the urgency correction factor, and the waiting time penalty factor are weighted and combined with the compatibility score to obtain the correction score for each berth. The berths are sorted from highest to lowest according to the corrected score, and the berth with the highest corrected score in the sorting result is selected as the target berth, and the target berth is assigned to the robot.

4. The cross-morphological cooperative scheduling method for multi-category robots according to claim 3, characterized in that, The multi-stage docking guidance strategy includes: a long-distance guidance strategy, a medium-distance guidance strategy, and a short-distance guidance strategy. The step of controlling the robot to complete docking and fixing using the corresponding multi-stage docking guidance strategy based on the type of the target berth and the interface type includes: The long-distance guidance strategy is executed as follows: an entry path is generated based on the location information of the target berth, and the entry path and speed limit are sent to the robot through the task map to guide the robot into the distribution center entrance area; The mid-range guidance strategy is executed as follows: The robot's current position is calculated based on the UWB tag signal received from a UWB base station; alternatively, the robot's positioning data is acquired via an RTK receiver; or, the robot's relative position is determined by emitting a laser beam from a lidar and receiving the echo reflected from the reflector on the robot. The current or relative position is compared with a preset alignment point of the target berth to generate a position deviation. This position deviation is then converted into a correction command and sent to the robot, guiding it towards the target berth. The short-range guidance strategy is executed as follows: A corresponding visual target is activated based on the type of the target berth; the relative pose of the robot and the visual target is acquired through a visual sensor; an alignment adjustment command is generated based on the relative pose to control the robot to gradually align with the positioning mechanism of the target berth; during the process of the robot contacting the positioning mechanism, contact force information between the robot and the positioning mechanism is acquired through a force sensor; the robot's movement speed and posture are adjusted based on the contact force information to guide the robot to contact the positioning mechanism. After the robot contacts the positioning mechanism, the corresponding locking method is selected according to the interface type, and the locking mechanism is triggered to mechanically fix the robot in combination with the locking method. The positioning status of the locking mechanism is detected by the sensor. When the locking mechanism is detected to be in place and the robot's attitude sensor feedback indicates a stable docking state, it is confirmed that the robot has completed docking and securing.

5. The cross-morphological cooperative scheduling method for multi-category robots according to claim 1, characterized in that, The preset operation includes: performing at least one of the following operations on the robot according to the subsequent task requirements and the health status: energy replenishment operation, battery swapping operation, tool replacement operation, load replacement operation, data unloading operation, health detection operation, and cleaning and maintenance operation; The preset scheduling operation includes: after the preset operation is completed, the robot is redeployed, transferred to the standby area, or transferred to the isolation maintenance area according to the subsequent task requirements and the health status.

6. A cross-morphological collaborative scheduling system for multiple types of robots, characterized in that, For implementing the cross-morphological cooperative scheduling method for multi-category robots as described in any one of claims 1 to 5, the cross-morphological cooperative scheduling system for multi-category robots comprises: The acquisition unit is used to acquire the robot's robot passport in response to the robot's request command. The robot passport includes robot category, shape envelope, interface type, battery parameters, communication protocol and task status. The berth allocation unit is used to generate a compatibility score between the robot and each berth based on the robot category, shape envelope, battery parameters, communication protocol, and task status in the robot passport, as well as the real-time occupancy information of each berth in the distribution center. Based on the compatibility score and in combination with the robot's remaining battery power, task urgency, and estimated waiting time, the unit allocates a target berth to the robot. The docking guidance unit is used to control the robot to complete docking and fixing according to the type of the target berth and the interface type, using a corresponding multi-stage docking guidance strategy. The job scheduling unit is used to perform preset job operations on the robot after the robot is fixed in the dock, based on the robot's subsequent task requirements and health status, and to perform preset scheduling operations on the robot after the preset job operations are completed.

7. An electronic device, characterized in that, include: Processor and memory, the memory being used to store computer programs; When the computer program is loaded by the processor, it causes the processor to execute the cross-morphological cooperative scheduling method for multi-category robots as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cross-morphological cooperative scheduling method for multi-category robots as described in any one of claims 1 to 5.

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