Low power adaptive cooperative task allocation method based on composite robot of mechanical arm

Abstract

The application discloses a low-power adaptive cooperative task allocation method based on a mechanical arm composite robot and belongs to the technical field of mechanical arm composite robot scheduling. The method firstly collects robot, task and charging pile data in real time, constructs a three-dimensional associated data model; then adaptively selects a single machine or a multi-machine operation mode by using an improved greedy algorithm; plans a constraint type cooperative path by using an improved A* algorithm, and optimally selects a cargo transfer node by using a weighted clustering algorithm; realizes segmented transfer and relay transportation of multi-robot by relying on a mechanical arm; simultaneously, the robot and task states are monitored in real time at a fixed frequency, dynamic re-planning is triggered after an exception occurs, and algorithm parameters are iteratively optimized by using historical data. The application solves the technical problem of insufficient adaptive cooperative scheduling capability of a robot cluster under a low-power working condition, reduces the single machine power redundancy requirement, reduces the robot empty running loss, improves the utilization rate of the low-power robot and the stability of the cluster operation, and is suitable for multi-parallel operation scenes such as industrial storage and intelligent logistics.

Description

Technical Field

[0001] This invention relates to a low-battery adaptive collaborative task allocation method for robotic arm-composite robots, belonging to the field of robotic arm-composite robot scheduling technology. Background Technology

[0002] With the rapid development of industrial warehousing, intelligent logistics, and flexible manufacturing, hybrid robots integrating mobile chassis and robotic arms are widely used. These robots can perform integrated operations of cargo grasping, transfer, and delivery, and multi-robot swarm parallel operation has become the mainstream application mode. During swarm operations, the rational allocation and scheduling of transportation order tasks directly determines the overall operational efficiency, energy consumption, and equipment resource utilization of the robots. Therefore, an efficient and adaptable task allocation method is the core key to ensuring the stable operation of the hybrid robot swarm.

[0003] Current task allocation and scheduling solutions for robotic arms and composite robots generally adopt a mode where each robot independently completes the entire cargo transportation task. To avoid the risk that a robot cannot return to its charging station due to insufficient power after completing a task, traditional solutions require setting a high power redundancy threshold for each robot, reserving power far exceeding the actual energy consumption requirements for task execution and return charging. In this mode, robots with low remaining power that do not meet the high redundancy standard are directly excluded from task scheduling and cannot participate in any operations, resulting in a large amount of robot equipment resources being idle and wasted for a long time.

[0004] When the remaining battery power of nearby robots within the work area is insufficient to independently undertake the entire transportation task, the existing scheduling mechanism lacks local collaborative operation capabilities and can only dispatch robots with sufficient battery power to the site from a distance. This significantly increases the ineffective travel distance of the robots, generates unnecessary additional power consumption, and prolongs the task response time, significantly reducing the overall operational efficiency and equipment utilization of the robot cluster.

[0005] Meanwhile, the existing conventional scheduling architecture does not design multi-robot collaborative task allocation logic for low-battery operation scenarios, nor does it make full use of the cargo gripping and transfer functions of the robotic arm. It cannot break down a single long-distance transportation task into multiple segments, and cannot complete the entire transportation process by having multiple low-battery robots work together in a relay. This further amplifies the industry pain points of idle robot resources and long-distance empty runs.

[0006] Furthermore, most existing scheduling systems adopt a static task allocation model, lacking a real-time monitoring mechanism for the entire process, including task execution progress, robot's remaining power, energy consumption status, and robotic arm working status. They also lack the ability to dynamically adjust strategies in case of changes in the work environment or abnormal power consumption. Once sudden changes in robot energy consumption or changes in the on-site work environment occur, the system cannot optimize task allocation and travel paths in a timely manner, which can easily lead to malfunctions such as task execution stagnation and robot shutdown due to power depletion. This makes it difficult to adapt to complex and dynamic actual work scenarios.

[0007] In summary, existing robotic arm and composite robot task allocation technologies generally suffer from inherent technical defects such as excessively high single-machine power redundancy settings, low utilization rate of low-power robots, lack of close-range collaboration and only remote adjustment for idle operation, and lack of real-time monitoring and dynamic adaptive adjustment mechanisms. These defects fail to meet the operational needs of robot clusters in scenarios such as industrial warehousing and intelligent logistics, which require low-power adaptation, efficient collaboration, and energy saving. Summary of the Invention

[0008] This invention aims to address the lack of adaptive collaborative scheduling capabilities in existing robotic arm composite robot clusters under low-battery conditions. This leads to unreasonable redundancy in robot power configuration, low equipment utilization, excessive wasted runs, and an inability to dynamically adapt task scheduling to changes in operating conditions. This invention provides a low-battery adaptive collaborative task allocation method for robotic arm composite robots, the technical solution of which is as follows: Step 1: Collect robot status data, task information, and charging station location data in real time to build a three-dimensional correlation data model of robot-task-charging station; Step 2: Use the improved A* algorithm with multi-dimensional quantitative constraints to perform multi-mode path pre-planning, select the optimal travel path, and output two types of path schemes: single-machine direct and multi-machine segmented, along with the corresponding operation energy consumption, mileage, and time parameters; the multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. Step 3: Based on the robot-task-charging pile three-dimensional association data model, a weighted clustering model is constructed by integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience, and the optimal cargo transfer node in the planned path of Step 2 is selected to split the transportation segment; Step 4: Based on path energy consumption and time consumption data, with work efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints, the feasibility of robot operation is verified by a greedy algorithm, and the task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation is adaptively selected. Step 5: Send the task allocation plan determined in Step 4 to the robot and control the robot to execute the task.

[0009] Optionally, the method further includes: in a multi-robot collaborative operation scenario, if the robot that has completed the delivery of goods has enough remaining power to return to the nearest charging station, it can be directly scheduled to return for charging; if the remaining power is insufficient for return, other robots with sufficient power in the collaborative task group or idle robots on site can be scheduled to be guided to a temporary charging point for emergency charging, or scheduled to wait near a charging station for unified charging.

[0010] Optionally, the method further includes: real-time monitoring of robot status, task progress, and robotic arm status, setting abnormal thresholds for each, and dynamically replanning the task, path, and transfer nodes after triggering them.

[0011] Optionally, the method further includes: optimizing the allocation and path planning algorithm parameters using historical data through a linear regression iterative algorithm.

[0012] Optionally, step 3 employs a weighted K-Means clustering algorithm to achieve a comprehensive score. S As a weighting factor, high-scoring nodes are preferentially aggregated to form clusters. After iterative convergence, the node with the highest comprehensive score in the cluster is selected as the optimal relay node. The comprehensive score... S The expression is:

[0013] in, , , These represent the weights of the three indicators, , , These represent the normalized location equilibrium index, energy consumption index, and energy replenishment convenience index, respectively.

[0014] Optionally, the robot status data includes device number, coordinates, remaining battery power, no-load / load energy consumption, robotic arm motion energy consumption, safety redundancy battery power, and online status.

[0015] Optionally, the task information includes: task number, cargo weight, origin and destination coordinates, estimated mileage, and priority.

[0016] Secondly, the present invention provides a low-battery adaptive collaborative task allocation system based on a robotic arm-based composite robot, the system being configured to implement the low-battery adaptive collaborative task allocation method based on a robotic arm-based composite robot as described in any of the preceding claims, comprising: The data model building module is configured to collect robot status data, task information, and charging pile location data in real time, and build a three-dimensional data model relating robot, task, and charging pile. The path planning module is configured to use an improved A* algorithm that integrates multi-dimensional quantitative constraints to perform multi-mode path pre-planning, and to obtain the optimal travel path through hierarchical screening and output two types of path schemes: single-machine direct and multi-machine segmented, along with the corresponding operation energy consumption, mileage, and time parameters. The multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. The transfer node determination module is configured to construct a weighted clustering model based on the robot-task-charging pile three-dimensional association data model, integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience, and to screen out the optimal cargo transfer node in the path planned by the path planning module to split the transportation segment. The task scheme determination module is configured to use path energy consumption and time consumption data as optimization objectives, with operation efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints to verify the feasibility of robot operation through a greedy algorithm, and adaptively select a task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation. The task execution module is configured to send the determined task allocation plan to the robot and control the robot to execute the task.

[0017] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-power adaptive collaborative task allocation method based on the robotic arm composite robot as described in any of the preceding claims.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the low-power adaptive collaborative task allocation method based on the robotic arm composite robot as described in any of the preceding claims.

[0019] The beneficial effects of this invention are: This invention addresses the fundamental technical problem of existing robotic arm composite robot clusters lacking adaptive collaborative scheduling capabilities under low-battery conditions. It breaks through the traditional model of relying solely on a single robot for independent operation by constructing an adaptive task allocation mechanism that integrates real-time data acquisition and modeling, and compares the efficiency of single / multi-robot operations. This invention eliminates excessively high power redundancy for individual robots, retaining only a basic safety redundancy. Through adaptive optimization, it selects between single-robot independent operation and multi-robot collaborative segmented operation modes, fundamentally solving the shortcomings of traditional solutions such as unreasonable power redundancy configuration and the inability of low-battery robots to participate in operations. This effectively revitalizes idle equipment resources within the cluster and significantly improves the overall utilization rate of the robotic arm composite robot cluster.

[0020] This invention integrates an improved A* path planning algorithm with a distance-battery-charging-pile weighted clustering transfer node planning strategy, leveraging the cargo transfer function of a robotic arm to achieve segmented relay transfer of multiple low-battery robots. It eliminates the need to remotely deploy robots with surplus battery power for cross-regional support when nearby robots are low on power. Prioritizing the use of nearby low-battery robots on-site to collaboratively complete tasks significantly reduces unnecessary robot mileage, lowers additional power consumption, shortens task response time, and effectively improves the overall operational efficiency and energy-saving operation level of the cluster.

[0021] This invention establishes a real-time monitoring and dynamic replanning optimization mechanism throughout the entire process. It collects robot battery power, energy consumption, task progress, and robotic arm working status in real time at a fixed frequency and sets anomaly judgment threshold. When abnormal power consumption, robotic arm movement lag, or task timeout occurs, the task can be dynamically reallocated, and the travel path and transfer nodes can be reconstructed to ensure that the task is not interrupted and the robot does not stop due to power failure. This significantly improves the stability of task execution and the adaptability to working conditions in complex work scenarios.

[0022] Meanwhile, this invention iteratively optimizes algorithm parameters through historical operating data, continuously improves task allocation and judgment rules and path planning models, and can adaptively adapt to various operating scenarios such as industrial warehousing, intelligent logistics, and flexible manufacturing. It is compatible with transportation tasks of different cargo weights and different transportation distances. While ensuring the robot's battery life and safety, it also takes into account operational efficiency, energy economy and cluster scheduling versatility, making it more widely applicable. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the low-battery adaptive collaborative task allocation method for robotic arm composite robots based on the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0026] Example 1: This embodiment provides a low-battery adaptive collaborative task allocation method based on a robotic arm-composite robot, including: Step 1: Collect robot status data, task information, and charging station location data in real time to build a three-dimensional correlation data model of robot-task-charging station; Step 2: Use the improved A* algorithm with multi-dimensional quantitative constraints to perform multi-mode path pre-planning, select the optimal route and output two types of route schemes: single-machine direct route and multi-machine segmented route, along with the corresponding operation energy consumption, mileage, and time parameters; the multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. Step 3: Based on the three-dimensional correlation data model of robot-task-charging pile, a weighted clustering model is constructed by integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience to screen out the optimal cargo transfer node in the planned path in Step 2 and split the transportation segment. Step 4: Based on path energy consumption and time consumption data, with work efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints, the feasibility of robot operation is verified by a greedy algorithm, and the task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation is adaptively selected. Step 5: Send the task allocation plan determined in Step 4 to the robot and control the robot to execute the task.

[0027] Example 2: This embodiment provides a low-battery adaptive collaborative task allocation method based on a robotic arm-integrated robot, applied to an industrial warehousing operation scenario. It deploys 10 integrated robots with mobile chassis and robotic arms, a scheduling system, 10 battery detection modules, a task management module, and 8 charging stations. Each robot has a total battery capacity of 1000Wh, with a basic safety redundancy of 8% (80Wh). The task to be performed involves transporting 50kg of goods from warehouse A to warehouse C, a distance of 5km. The task is of medium priority, with the robot consuming 100Wh / km under load. Each robotic arm grasping and transferring action consumes 50Wh. The average return charging path distance after task completion is 1km, strictly adhering to the requirement of not using robotic arms for assisted driving.

[0028] Specifically, it includes the following steps: Step 1: Real-time data collection.

[0029] The power detection module collects real-time data on the remaining power, power consumption per unit time (energy consumption rate) under no-load and loaded states, and the real-time position coordinates of each robot for the 10 robotic arm composite robots on site. In this embodiment, 3 robots are in a low-power state, with remaining power of 300Wh, 320Wh, and 350Wh respectively, while the remaining 7 robots all have ≥500Wh of remaining power, indicating that they are in a fully powered state.

[0030] The task management module retrieves detailed information about tasks to be executed, including cargo weight, coordinates of the origin and destination, task priority, and estimated distance. In this embodiment, the task information to be collected includes a cargo weight of 50kg, coordinates of the origin (area A), coordinates of the destination (area C), medium task priority, and an estimated transportation distance of 5km.

[0031] The real-time geographical location, occupancy status, and charging power of eight charging piles were collected using a charging pile positioning module.

[0032] The robot status data, task information, and charging pile data are synchronized to the data processing unit in real time. A three-dimensional correlation data model of robot-task-charging pile is built using three tables plus a correlation index table to provide standardized input for various subsequent algorithms. The specific parameters included in each dimension are described below.

[0033] (1) Robot table: Equipment number, coordinates, remaining power, no-load / load energy consumption, robotic arm motion energy consumption, safety redundancy power, online status; (2) Task list: Task number, cargo weight, origin and destination coordinates, estimated mileage, priority; (3) Charging pile table: pile number, location coordinates, idle status, charging power.

[0034] The association rules are as follows: The association rule between robots and tasks is: bind candidate robots around the task based on spatial distance; The association rule between the robot and the charging station is: match the nearest charging station according to the coordinates; The association rule between tasks and charging stations is: bind to available charging stations along the task route.

[0035] Three entity data tables plus one related index table (storage device number, task number, station number, spacing) allow for quick retrieval of cross-dimensional constraint parameters via the index.

[0036] Step 2: Perform multi-path pre-planning based on the improved A* algorithm.

[0037] The improved A* algorithm decomposes the actual walking cost g(n) and the estimated destination cost h(n), and incorporates various constraints into the cost value after quantification and conversion, so that constraints participate in path branch selection. The various constraints are as follows: (1) Collision hard constraint: When expanding the next candidate node, if the point / path falls within an obstacle or restricted area, the branch path is directly pruned and discarded to avoid collisions from the source; (2) Energy consumption constraints for no-load / load: Combine the current robot carrying 50kg of goods to match the corresponding energy consumption coefficient, use the road segment mileage × unit energy consumption to convert the road segment power consumption, convert it into energy consumption cost and incorporate it into the actual walking cost g(n); (3) Remaining power constraint: When traversing candidate expansion nodes, it is predicted whether the current remaining power after deducting the basic safety redundancy can cover the power consumption of this section of the route. If the power is insufficient, the expansion path is directly eliminated; if the path passes through an idle charging pile, the emergency energy replenishment loss is deducted and the comprehensive cost of this path is reduced. (4) Location constraints of charging piles: In long-distance transportation scenarios, for candidate paths near available charging piles, preferential weights are added to the estimated cost h(n) at the destination, and the algorithm prioritizes feasible routes that pass through charging piles.

[0038] Based on the above four constraints, and combined with the three objectives of collision-free operation, shortest time consumption, and lowest energy consumption, a collaborative optimization is performed according to the following rules: (1) No collision is a hard constraint that vetoes all paths that interfere with obstacles and are no longer included in the time and energy consumption optimization calculation. (2) Driving time and total energy consumption are flexible indicators with adjustable weights: The total driving time and total energy consumption of a single route are normalized respectively; the time weight of short-distance small-item transportation is increased to ensure timeliness, and the energy consumption weight of the 5km medium- and long-distance heavy-load task in this embodiment is increased to control power consumption. (3) Path selection determination: All compliant collision-free paths are sorted in ascending order of weighted comprehensive cost, and the route with the minimum comprehensive cost is selected as the optimal driving route.

[0039] The above selection process adopts a hierarchical selection logic of hard constraints and flexible weighted normalization to balance three optimization objectives. Finally, two sets of schemes are pre-generated: a single-machine direct alternative route and a multi-machine segmented alternative route. The actual driving mileage, segment intervals, segment energy consumption, and estimated total time of each scheme are output, providing accurate data for the next step of efficiency calculation and task grouping.

[0040] Step 3: Use a weighted clustering algorithm to filter forwarding nodes.

[0041] Using the 3D interconnected data model of robot-task-charging station constructed in step 1 as the core input, the model is used to call task coordinates, robot position and energy consumption, charging station locations, and feasible area data on the map. The optimal cargo transfer node is solved by a distance-energy-charging station weighted clustering algorithm. The specific implementation is as follows: (1) Read the three-dimensional associated model data, including: the starting and ending point coordinates of the task dimension, the real-time position and energy consumption parameters of the robot dimension, the coordinates and idle status of the charging pile dimension, and the obstacle and restricted area data of the map topology. The parameters are quickly matched and loaded through the associated index table.

[0042] (2) Generate candidate forwarding nodes according to the following rules: Using the line connecting the starting point A and the ending point C as the central axis, map points within a preset range on both sides of the axis that are suitable for docking, have no obstacles, and are adapted to robotic arm operations are extracted to generate an initial set of candidate nodes; prohibited areas, shelf obstruction points, and non-operation docking points are automatically eliminated, and compliant and feasible candidate nodes are retained.

[0043] (3) Perform min-max normalization on the three core indicators to eliminate dimensional differences, and calculate the comprehensive score according to the preset weights. The indicators and their weights include: ① Location equilibrium index p : The distance fit between candidate nodes and task start and end points, weighted at 40%; ② Energy consumption indicators e The percentage of electricity consumed by the robot upon arrival at the node, weighted at 30%; ③Energy replenishment convenience indicators c : Distance between the node and the nearest available charging station, weighted at 30%; The comprehensive scoring formula is:

[0044] in, , , These represent the weights of the three indicators, which are set to 0.4, 0.3, and 0.3 respectively in this embodiment. , , These represent the normalized location equilibrium index, energy consumption index, and energy replenishment convenience index, respectively.

[0045] (4) Using the midpoint of the line connecting the start and end points of the tasks as the initial cluster center, the weighted K-Means clustering algorithm is used to complete the node aggregation; the clustering process is based on a comprehensive score. S As a weighting factor, high-scoring nodes are prioritized to form clusters; after iterative convergence, the node with the highest comprehensive score in the cluster is selected as the optimal relay node.

[0046] The specific solution in this embodiment is as follows: The weights of the position balance index, energy consumption index, and energy replenishment convenience index are set to 0.4, 0.3, and 0.3, respectively. All feasible operation areas between area A and area C are traversed, and areas with obstacles are eliminated. The comprehensive score of each candidate node is calculated one by one. Area B with the highest score is selected as the only optimal transfer node. The transportation path is split into 2km from area A to area B and 3km from area B to area C to ensure that each low-battery collaborative robot can safely arrive within the remaining battery range, and the robotic arm can achieve interference-free and accurate cargo transfer.

[0047] Step 4: Use a greedy algorithm to determine the task allocation scheme.

[0048] Based on the energy consumption and time consumption data for each path output in step 2, and with the goals of maximizing execution efficiency, minimizing total energy consumption, and minimizing idle distance, a greedy algorithm is used to determine the final task allocation scheme, taking into account constraints such as location, battery power, segmented energy consumption, and basic safety redundancy. The specific steps include: (1) Calculate the execution efficiency separately. The efficiency of a single robot executing independently is expressed as: E1 = total task time ÷ total task energy consumption. The efficiency of multiple robots executing collaboratively is: E2 = total task time ÷ total task energy consumption.

[0049] (2) Conduct a feasibility assessment of the robot task and verify whether the remaining power of a single robot meets the total energy consumption of the entire task, the energy consumption for return charging, and the basic safety redundancy power. Calculations show that in this embodiment, a robot with a remaining power of 600Wh can be selected, and its single-robot task execution is deemed feasible. Furthermore, E1≥E2 is calculated. According to the allocation rules, the transportation task is directly assigned to this single robot for independent execution, reserving only 80Wh of basic safety redundancy power, without the need for additional power redundancy. If the remaining power of all robots on site is insufficient to independently complete the entire task, the scheduling system automatically activates the multi-robot collaborative mode, selecting two or more robots from highest to lowest remaining power to form a collaborative task group. This ensures that the overall total range of the collaborative group can cover the entire transportation and return charging needs, generating the final collaborative operation task.

[0050] Step 5: Multi-robot collaborative execution and cargo transfer.

[0051] In single-robot operation mode, the 600Wh battery robot assigned to the task travels to warehouse A area according to the path planned by the improved A* algorithm, uses its own robotic arm to grab 50kg of goods, travels along the planned path to C area, and completes the unloading and placement of the goods. The total power consumption of the entire task is 550Wh, and the remaining power is 50Wh after the task is completed.

[0052] In a multi-robot collaborative operation mode, robots within the collaborative task group travel along a planned segmented path to the preset transfer node B. A robot with 350Wh of remaining battery power, sufficient to reach the transfer node, is selected as the first receiving robot. It uses its robotic arm to grab goods from the task starting point A and transports them to the transfer node B. A robot with 320Wh of remaining battery power then becomes the subsequent receiving robot, precisely receiving the goods at the transfer node using its robotic arm, undertaking the segmented transportation task from B to C. If the subsequent receiving robot's battery power is insufficient to complete the second half of the transportation, the scheduling system immediately adds a transfer node and dispatches a low-battery robot with 300Wh of remaining battery power to join the collaborative group for relay transfer. Through multi-robot division of labor and the coordination of robotic arm cargo transfer, the entire transportation task is completed.

[0053] Step 6: Task completion and return charging scheduling.

[0054] In a single-robot operation scenario, after the robot completes the cargo transportation task in Zone C, the dispatch system checks its remaining power in real time. It finds that the remaining 50Wh is insufficient to support the 1km return charging journey. Immediately, based on the availability of charging piles on site, it dispatches an idle robot with 700Wh of power to guide the robot to the nearest temporary charging point to replenish emergency power. After the power is sufficient, the robot autonomously drives to the nearest idle charging pile to complete the charging.

[0055] In multi-robot collaborative operation scenarios, the first receiving robot that completes the transfer of goods can return to the nearest charging station autonomously if it has remaining power, and is directly dispatched to return for charging; if it has insufficient remaining power to return, other robots with sufficient power in the collaborative task group or idle robots on site are dispatched to a temporary charging point for emergency charging, or dispatched to stand by near a charging station to wait for unified charging, thus preventing the problem of robots stopping midway or running out of power.

[0056] Step 7: Real-time monitoring and dynamic optimization.

[0057] The scheduling system monitors the remaining battery power, real-time energy consumption rate, and task execution progress of all robots every 10 seconds, while also monitoring the robotic arm's movement status and the accuracy of cargo delivery.

[0058] Set abnormal thresholds for operation: power consumption rate deviation > 20%, robotic arm movement delay > 3 seconds, task execution timeout > 30 minutes; when any indicator triggers the abnormal threshold, the dynamic replanning algorithm is started to re-model the task allocation, solve the path and transfer node, and quickly adjust the division of labor of the collaborative team members, the driving path or the location of the transfer node to ensure the uninterrupted and continuous execution of the transportation task.

[0059] Once all tasks are completed, the scheduling system inputs historical data such as task execution time, power consumption, and multi-robot collaborative operation efficiency into the algorithm optimization unit. Using a linear regression iterative algorithm, it updates the energy consumption calculation coefficient, efficiency allocation weight, and transfer node screening parameters, continuously optimizing the single robot task feasibility judgment criteria, multi-robot collaborative screening rules, and improving the A* path planning algorithm, thereby iteratively improving the rationality of subsequent task allocation and the overall utilization rate of the robot cluster.

[0060] Example 3: This embodiment provides a low-battery adaptive collaborative task allocation system based on a robotic arm-composite robot, including: The data model building module is configured to collect robot status data, task information, and charging pile location data in real time, and build a three-dimensional data model relating robot, task, and charging pile. The path planning module is configured to use an improved A* algorithm that integrates multi-dimensional quantitative constraints for multi-mode path pre-planning, and to obtain the optimal travel path through hierarchical screening and output two types of path schemes: single-machine direct and multi-machine segmented, along with the corresponding operation energy consumption, mileage, and time parameters. The multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. The transfer node determination module is configured to build a weighted clustering model based on a three-dimensional correlation data model of robot-task-charging pile, integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience, and to select the optimal cargo transfer node in the path planned by the path planning module to split the transportation segment. The task scheme determination module is configured to use path energy consumption and time consumption data as optimization objectives, with operation efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints to verify the feasibility of robot operation through a greedy algorithm, and adaptively select a task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation. The task execution module is configured to send the determined task allocation plan to the robot and control the robot to execute the task.

[0061] Some steps in the embodiments of the present invention can be implemented using software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-battery adaptive collaborative task allocation method based on a robotic arm-based composite robot, characterized in that, The method includes: Step 1: Collect robot status data, task information, and charging station location data in real time to build a three-dimensional correlation data model of robot-task-charging station; Step 2: Use the improved A* algorithm with multi-dimensional quantitative constraints to perform multi-mode path pre-planning, select the optimal travel path, and output two types of path schemes: single-machine direct and multi-machine segmented, along with the corresponding operation energy consumption, mileage, and time parameters; the multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. Step 3: Based on the robot-task-charging pile three-dimensional association data model, a weighted clustering model is constructed by integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience, and the optimal cargo transfer node in the planned path of Step 2 is selected to split the transportation segment; Step 4: Based on path energy consumption and time consumption data, with work efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints, the feasibility of robot operation is verified by a greedy algorithm, and the task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation is adaptively selected. Step 5: Send the task allocation plan determined in Step 4 to the robot and control the robot to execute the task.

2. The low-battery adaptive collaborative task allocation method based on a robotic arm composite robot according to claim 1, characterized in that, The method also includes: in a multi-robot collaborative operation scenario, if a robot that has completed the transfer of goods has remaining power and can return to the nearest charging station autonomously, it can be directly scheduled to return for charging; if it has insufficient remaining power to return, other robots with sufficient power in the collaborative task group or idle robots on site can be scheduled to be guided to a temporary charging point for emergency charging, or scheduled to wait near a charging station for unified charging.

3. The low-battery adaptive collaborative task allocation method based on a robotic arm-composite robot according to claim 1, characterized in that, The method also includes: real-time monitoring of robot status, task progress and robotic arm status, setting abnormal thresholds for each, and dynamically replanning tasks, paths and transfer nodes after triggering them.

4. The low-battery adaptive collaborative task allocation method based on a robotic arm-composite robot according to claim 1, characterized in that, The method also includes: using historical data to optimize the allocation and path planning algorithm parameters through a linear regression iterative algorithm.

5. The low-battery adaptive collaborative task allocation method based on a robotic arm composite robot according to claim 1, characterized in that, Step 3 employs a weighted K-Means clustering algorithm to achieve a comprehensive score. S As a weighting factor, high-scoring nodes are preferentially aggregated to form clusters. After iterative convergence, the node with the highest comprehensive score in the cluster is selected as the optimal relay node. The comprehensive score... S The expression is: in, , , These represent the weights of the three indicators, , , These represent the normalized location equilibrium index, energy consumption index, and energy replenishment convenience index, respectively.

6. The low-battery adaptive collaborative task allocation method based on a robotic arm-composite robot according to claim 1, characterized in that, The robot status data includes device number, coordinates, remaining power, no-load / load energy consumption, robotic arm motion energy consumption, safety redundancy power, and online status.

7. The low-battery adaptive collaborative task allocation method based on a robotic arm-composite robot according to claim 1, characterized in that, The task information includes: task number, cargo weight, origin and destination coordinates, estimated mileage, and priority.

8. A low-battery adaptive collaborative task allocation system based on a robotic arm composite robot, characterized in that, The system is configured to implement the low-power adaptive collaborative task allocation method based on a robotic arm-composite robot as described in any one of claims 1-7, including: The data model building module is configured to collect robot status data, task information, and charging pile location data in real time, and build a three-dimensional data model relating robot, task, and charging pile. The path planning module is configured to use an improved A* algorithm that integrates multi-dimensional quantitative constraints to perform multi-mode path pre-planning, and to obtain the optimal travel path through hierarchical screening and output two types of path schemes: single-machine direct and multi-machine segmented, along with the corresponding operation energy consumption, mileage, and time parameters. The multi-dimensional quantitative constraints include collision constraints, energy consumption constraints, remaining power constraints, and charging pile location constraints. The transfer node determination module is configured to construct a weighted clustering model based on the robot-task-charging pile three-dimensional association data model, integrating multi-dimensional evaluation indicators such as location, energy consumption, and charging convenience, and to screen out the optimal cargo transfer node in the path planned by the path planning module to split the transportation segment. The task scheme determination module is configured to use path energy consumption and time consumption data as optimization objectives, with operation efficiency, total energy consumption and empty driving distance as optimization objectives, and combined with power safety constraints to verify the feasibility of robot operation through a greedy algorithm, and adaptively select a task allocation scheme of single-machine independent operation or multi-machine collaborative segmented operation. The task execution module is configured to send the determined task allocation plan to the robot and control the robot to execute the task.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the low-power adaptive collaborative task allocation method based on a robotic arm composite robot as described in any one of claims 1 to 7.

10. 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 steps of the low-power adaptive collaborative task allocation method based on a robotic arm composite robot as described in any one of claims 1 to 7.

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