Emergency energy supply scheduling method and system for wheeled and footed mobile robots

Abstract

The present invention provides a method and system for emergency energy supply scheduling of a wheeled mobile robot, which relates to the technical field of energy supply robot scheduling. The method comprises the following steps: obtaining vehicle attribute information, vehicle location information and vehicle power information of vehicles inside and outside a supply station, and establishing an initial matching scheme between the robot and the vehicle; prioritizing the vehicles in the initial matching scheme according to the purpose or urgency of the vehicle attribute information, and calculating the priority penalty value, path length, task energy consumption and load imbalance in the initial matching scheme; constructing an objective function, optimizing the objective function, and introducing constraint conditions to screen out the optimal matching scheme from the initial matching scheme, wherein the constraint conditions include one-to-one service between the robot and the vehicle, the robot power meeting the vehicle charging task requirements, and off-station tasks taking precedence over on-station tasks; and generating a dynamic path according to the matching results of the robot and the vehicle in the optimal matching scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of energy supply robot scheduling, and in particular to an emergency energy supply scheduling method and system for a one-wheeled, foot-mobile robot. Background Art

[0002] With the widespread popularity of electric vehicles (EVs), the demand for power supply is growing rapidly, becoming a major challenge in the fields of intelligent transportation and energy management.

[0003] However, existing electric vehicle energy recharge methods generally rely on power supply stations. While fixed charging piles within charging stations can provide energy recharge services, they are limited in number, require long recharge times, and face surging user demand. This often leads to vehicle queues and congestion within the stations, reducing recharge efficiency and potentially exacerbating traffic flow pressures. Furthermore, the operational efficiency of power supply stations is limited by the scalability of static infrastructure, making it difficult to quickly adapt to dynamic demand or emergencies, and making it difficult to address vehicle power supply issues during traffic congestion on roads outside the stations. Summary of the Invention

[0004] The purpose of the present invention is to provide an emergency energy supply scheduling method and system for a one-wheeled mobile robot to solve the problem of low computer supply efficiency of the existing electric vehicle power supply method mentioned in the above background technology and difficulty in solving the problem of vehicle power supply during traffic congestion on roads outside the station.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for emergency energy supply scheduling of a wheeled mobile robot, comprising the following steps: obtaining vehicle attribute information, vehicle position information and vehicle power information of vehicles inside and outside a supply station, and establishing an initial matching scheme between the robot and the vehicle; prioritizing the vehicles in the initial matching scheme according to the purpose or urgency of the vehicle attribute information, and calculating the priority penalty value in combination with the SOC weighting factor and the vehicle position weight, and calculating the path length, task energy consumption and load imbalance in the initial matching scheme through the vehicle position information and the vehicle power information; constructing an objective function based on the path length, the priority penalty value, the task energy consumption and the load imbalance, optimizing the objective function, and introducing constraints to screen out the optimal matching scheme from the initial matching scheme, wherein the constraints include one-to-one service between the robot and the vehicle, the robot power meeting the vehicle charging task requirements, and off-station tasks taking precedence over on-station tasks; generating a dynamic path based on the matching result of the robot and the vehicle in the optimal matching scheme.

[0006] Optionally, the step of optimizing the objective function specifically includes: minimizing the path length, the priority penalty value, the task energy consumption and the load imbalance.

[0007] Optionally, the step of generating a dynamic path according to the matching result between the robot and the vehicle in the optimal matching solution specifically includes: using A The algorithm plans the global path and adjusts the local path through the artificial potential field method in combination with real-time obstacle detection.

[0008] Optionally, the use of A The steps of the algorithm to plan the global path include: building a grid map with the current position of the robot as the starting point and the position of the vehicle as the end point, using A The algorithm calculates the globally optimal path.

[0009] Optionally, the step of adjusting the local path by the artificial potential field method in combination with real-time obstacle detection specifically includes: in the process of the robot moving along the global path, detecting the surrounding environment in real time and updating the obstacle set including dynamic obstacles and static obstacles; when a dynamic obstacle is detected, using the artificial potential field method to perform local dynamic adjustment; comparing the local path generated by the artificial potential field method with the A The algorithm's global path fusion updates the length and energy consumption of each path segment and verifies whether the updated path meets the constraints.

[0010] Optionally, the steps of performing local dynamic adjustment using the artificial potential field method specifically include: constructing a gravitational field and a repulsive field, calculating the resultant force of gravity and repulsion, and determining the local movement vector of the robot; adjusting the motion trajectory of the robot according to the local movement vector, generating local path segments, and updating the path segment set; when the artificial potential field method leads to a local minimum, recalculating the resultant force by increasing the gravitational coefficient until the local minimum state is left.

[0011] Optionally, the step of optimizing the objective function and introducing constraints to select the optimal matching solution from the initial matching solutions specifically includes: minimizing the objective function using a particle swarm optimization algorithm, representing particles as candidate matching solutions, the particle position vector including the probability value of the robot and the vehicle matching, and determining the matching variable by discretizing the probability value; initializing the particle swarm, randomly generating the position and speed of the particles, wherein the initial position value of the vehicle outside the station is set in a high probability interval, and the initial position value of the vehicle inside the station is randomly distributed in a low probability interval; calculating the fitness of each particle by minimizing the objective function to guide the iterative optimization of the particle swarm and output the optimal matching solution.

[0012] Optionally, the particle swarm iterative optimization steps specifically include: updating the global optimal solution of the particle swarm, forcibly correcting it to include at least one off-station task if the global optimal solution does not cover the off-station task, updating the speed and position of each particle, imposing constraints on the probability of off-station tasks to preferentially shift toward the global optimum or individual optimum, discretizing the updated current position of the particle into a matching scheme, and iterating to continuously verify the constraints until a preset termination condition is met.

[0013] Optionally, the calculation formula for updating the speed and position of each particle is: ; Where, For particles In time speed, is the inertia weight, is the individual learning factor, is the global learning factor, and is a random number, and , is the optimal position of an individual, is the global optimal position, is the current position of the particle.

[0014] On the other hand, the present invention also provides an emergency energy supply scheduling system for a wheeled mobile robot, comprising: an acquisition module for acquiring vehicle attribute information, vehicle location information and vehicle power information of vehicles inside and outside a supply station, and establishing an initial matching scheme between the robot and the vehicle; a priority module for prioritizing the vehicles in the initial matching scheme according to the purpose or urgency of the vehicle attribute information, and calculating the priority penalty value in combination with the SOC weighting factor and the vehicle position weight, and calculating the path length, task energy consumption and load imbalance in the initial matching scheme through the vehicle location information and the vehicle power information; an optimization module for constructing an objective function, minimizing the path length, the priority penalty value, the task energy consumption and the load imbalance to optimize the objective function, and introducing constraints to screen out the optimal matching scheme from the initial matching scheme, wherein the constraints include one-to-one service between the robot and the vehicle, the robot power meeting the vehicle charging task requirements, and off-station tasks taking precedence over on-station tasks; a path generation module for generating a dynamic path based on the matching results of the robot and the vehicle in the optimal matching scheme.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] This application achieves the optimal solution for robot task allocation by optimizing the objective function and comprehensively considering four dimensions: path length, vehicle priority, task energy consumption, and load balancing. It significantly improves scheduling efficiency and adaptability through dynamic adjustment and differentiated constraints for in-station / out-of-station scenarios. In particular, in complex scenarios of in-station congestion and out-of-station emergency power supply, it ensures a rapid response to high-priority vehicles and optimizes robot resource utilization.

[0017] This application realizes the integration of global optimal path planning and local dynamic obstacle avoidance, which can effectively deal with dynamic obstacles, ensure the rapid response of emergency tasks inside and outside the station and the efficiency of the path within the station.

[0018] This application proposes differentiated scheduling strategies for both in-station and off-station power supply scenarios, targeting congestion within power supply stations and emergency power supply on highways. Inside the station, an environmental awareness network monitors congestion in real time, while off-station priority service constraints ensure rapid response to emergency tasks. This collaborative optimization mechanism effectively alleviates the pressure on power supply within the station while improving off-station emergency response speeds, breaking through the bottleneck of traditional power supply systems in multi-scenario collaborative scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the process steps of the present invention.

[0020] Figure 2 Schematic diagram of the objective function optimization process of the present invention.

[0021] Figure 3 This is a schematic diagram of the dynamic path planning process of the present invention.

[0022] Figure 4 Schematic diagram of the system structure of the present invention.

[0023] In the figure: 10-acquisition module, 20-priority module, 30-optimization module, 40-path generation module. DETAILED DESCRIPTION

[0024] The following will provide a clear and complete description of the solutions of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0027] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0028] It should be understood that the sequence numbers and sizes of the steps in this embodiment do not imply the order of execution. The order of execution of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of this application.

[0029] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] Please refer to Figures 1-4 The present invention provides a method for dispatching emergency energy supply for a wheeled mobile robot, comprising the following steps:

[0031] S1. Obtain vehicle attribute information, vehicle location information, and vehicle power information of vehicles inside and outside the supply station, and establish an initial matching plan between the robot and the vehicle.

[0032] Specifically, the system collects vehicle attribute information, location information, and battery information through on-board communication modules and in-station sensors. The system then performs intelligent dispatch based on vehicle location. In-station scenarios, the system uses an environmental perception network to monitor the number of vehicles queuing at the power recharge station in real time. If the number exceeds a preset threshold, a temporary power recharge task is triggered. In off-station scenarios, the system receives distress signals from vehicles on the highway, confirms the vehicle's location and SOC through the on-board communication module, and triggers an emergency power recharge task.

[0033] Optionally, the vehicle's SOC, location, and destination information are collected, and the battery loss is predicted based on road conditions and environmental factors. If the battery is insufficient to reach the destination, it is marked as "needing battery recharge" and the process proceeds to the next stage. If sufficient, the data is recorded and the process ends.

[0034] The optional wheeled and footed mobile power supply robot adopts a modular design, integrating a four-wheel high-speed mobile structure and a four-legged multi-scenario application structure. It has the ability to adapt to the flat ground in the power supply station and the complex terrain of the highway. The robot is equipped with a high-capacity energy storage unit and a multi-axis robotic arm to support dynamic path planning and precise docking. It has a built-in multi-objective optimization scheduling unit and path planning unit, as well as a central processing unit to handle task allocation and route optimization in real time.

[0035] The intelligent power supply service station on highways consists of a group of power supply piles, a central control unit and an environmental perception unit. The central control unit integrates a data processing module and a communication relay station, and is responsible for vehicle demand analysis and issuing robot dispatch instructions; the environmental perception unit includes infrared sensors, cameras and weather stations, which monitor congestion status, obstacle location and ambient temperature in real time, providing support for robot path planning.

[0036] S2. Prioritize the vehicles in the initial matching scheme according to the purpose or urgency of the vehicle attribute information, and calculate the priority penalty value in combination with the SOC weighting factor and the vehicle position weight. Calculate the path length, task energy consumption and load imbalance in the initial matching scheme based on the vehicle position information and the vehicle power information.

[0037] Specifically, priorities are divided according to vehicle attributes, including first-level priority, second-level priority, and third-level priority. First-level priority is used for energy supply of vehicles for emergency and special purposes, such as ambulances or fire trucks; second-level priority is used for energy supply of public service vehicles, such as buses, logistics transport vehicles, etc.; third-level priority is used for energy supply of vehicles for commercial and private purposes, such as private cars, taxis, etc.

[0038] Optionally, vehicle priority is mainly divided into vehicle attributes and road type distribution, specifically including: vehicle priority is divided into three levels based on a comprehensive assessment of the purpose or urgency of vehicle attributes, and the priority weight is dynamically calculated through a formula: Level 1 priority: Applicable to vehicles with high social value, such as medical transport vehicles, rescue vehicles, etc., or high urgency, such as a breakdown on the highway; Level 2 priority: Applicable to vehicles for public service purposes, such as buses and school buses, or medium urgency, such as vehicles queuing in stations; Level 3 priority: Applicable to vehicles for personal use, such as private cars and rental cars, or low urgency, such as non-emergency travel. Road types are divided according to the traffic characteristics inside and outside the smart power supply service station on the highway, which affects scheduling and route planning: Main roads outside the station: Covering the main line of the highway, with the highest priority, requiring rapid emergency response. Main channels inside the station: Located in the core area of ​​the service station, with heavy traffic flow, it has the second highest priority and is prioritized for scheduling rapid response tasks.

[0039] S3. Construct an objective function based on the rigidly quantified path length, the priority penalty value, the task energy consumption, and the load imbalance, minimize the path length, the priority penalty value, the task energy consumption, and the load imbalance to optimize the objective function, and introduce constraints to screen out the optimal matching solution from the initial matching solution, wherein the constraints include one-to-one service between the robot and the vehicle, the robot's power meets the vehicle charging task requirements, and off-site tasks take precedence over on-site tasks.

[0040] S4. Generate a dynamic path according to the matching result between the robot and the vehicle in the optimal matching solution.

[0041] It is understandable that this application achieves the optimal solution for robot task allocation by optimizing the objective function, comprehensively considering four dimensions: path length, vehicle priority, task energy consumption, and load balancing. Dynamic adjustment and differentiated constraints for in-station / out-of-station scenarios significantly improve scheduling efficiency and adaptability. This ensures rapid response to high-priority vehicles, especially in complex scenarios such as in-station congestion and out-of-station emergency power supply, while optimizing robot resource utilization. This application integrates global optimal path planning with local dynamic obstacle avoidance, effectively addressing dynamic obstacles and ensuring rapid response to emergency tasks both inside and outside the station and efficient in-station paths. This application proposes differentiated scheduling strategies for in-station and out-of-station power supply scenarios, targeting congestion within power supply stations and emergency power supply on highways. Congestion status is monitored in real time within the station through an environmental perception network, while priority service constraints are used outside the station to ensure rapid response to emergency tasks. This collaborative optimization mechanism effectively alleviates the pressure on in-station power supply while improving the speed of out-of-station emergency response, breaking through the bottleneck of traditional power supply systems in multi-scenario collaborative scheduling.

[0042] In some embodiments, the step of constructing the objective function specifically includes: constructing the objective function based on the standardized path length, the priority penalty value, the task energy consumption and the load imbalance.

[0043] Specifically, the objective function comprehensively considers path length, priority penalty, task energy consumption and load. Specifically, the multi-objective optimization scheduling algorithm aims to minimize path length cost, minimize priority penalty, optimize task energy consumption and achieve load balancing. The objective function is defined as: , where is the objective function, is the length of the path the robot travels when performing a task, is the vehicle priority penalty value, is the task energy consumption, The load imbalance factor, which incorporates four factors into robot scheduling, includes the path length required for the robot to complete the task, minimizing vehicle priority penalties, the energy consumption required by the robot and the central system to perform the task, and balancing the load consumed by each robot during the task. This formula can better schedule robots that meet the task completion requirements to complete the task.

[0044] Specifically, the calculation formula for the path length traveled by the robot to perform a task is: , where is the length of the path the robot travels when performing a task,

[0045] is the weight of the path length, used to adjust the importance of this goal, The total distance of the paths chosen for all robots, For robots Along the path To the vehicle distance, Select the variable for the path, and , if the robot Select Path Go to service vehicle ,but is 1, otherwise is 0. This formula is designed to minimize the total path length, ensure the robot moves efficiently, and reduce time and energy consumption.

[0046] Specifically, the calculation formula for vehicle priority penalty is:

[0047] , where is the vehicle priority penalty, is the weight of the priority penalty, which is the highest compared to other weights. For vehicles The basic priority reflects the importance of vehicle attributes. is the position weight, reflecting the importance of the vehicle position, is the weighting factor of SOC, and lower power has a larger value, thus having a greater priority. Then the product of each weight is squared to amplify the penalty of high-priority vehicles and emphasize urgency. is an assignment variable, and , if the robot Service Vehicles ,but is 1, otherwise Is 0, if 1 indicates a vehicle Not being served, i.e. If it is 0, a penalty will be generated. If the vehicle is served, then is 0, that is If it is 1, there is no penalty. This formula is to ensure that high priority vehicles are served first. The no-service penalty for high-priority vehicles is significantly increased, forcing the system to prioritize resource allocation to these vehicles.

[0048] Specifically, the calculation formula for the energy consumption of the robot optimization task is:

[0049] , where is the task energy consumption, is the energy consumption weight, is the effect of path distance and obstacle density on mobile energy consumption, is the environmental influencing factor, which is determined by the temperature and humidity of the environment around the path segment. is the load influencing factor. The greater the load, the more energy the robot consumes when traveling. Select a variable for the path.

[0050] Specifically, the calculation formula for load imbalance is: , where is the load imbalance, For robots Number of tasks assigned The number of tasks assigned equally to all robots, is the weight of the load imbalance. The entire formula represents the variance of task assignments across all robots. Its goal is to minimize this variance, ensuring that the number of tasks assigned to each robot is as close to the average as possible. This ensures a balanced distribution of tasks across robots and prevents overloading of some robots.

[0051] Specifically, the multi-objective optimization algorithm works as follows: the system generates all possible allocation scenarios, including combinations of in-station and out-station vehicles with wheeled robots. The objective function value for each scenario is calculated, and then the optimal scenario is selected by comparing the objective function values. Although the allocation is calculated uniformly, in-station and out-station vehicles are subject to different constraints during the optimization process to ensure that the scheduling strategy meets actual needs.

[0052] In some embodiments, the step of generating a dynamic path based on the matching result between the robot and the vehicle in the optimal matching solution specifically includes: using A The algorithm plans the global path and adjusts the local path using an artificial potential field method in combination with real-time obstacle detection. This formula is designed to minimize energy consumption, extend the robot's operating time, and improve the system's economical and sustainable performance.

[0053] In some embodiments, the use of A The steps of the algorithm to plan the global path include: building a grid map with the current position of the robot as the starting point and the position of the vehicle as the end point, using A The algorithm calculates the globally optimal path.

[0054] Specifically, the global path planning adopts A Algorithms, robots The current position of the vehicle is the starting point. As the end point, build a grid map, define the obstacle area, initialize the open list and closed list, and use A Algorithm calculates the global optimal path , the path consists of consecutive path segments Each path segment Has length . And A The algorithm heuristic function is: , where A Algorithm heuristic function, From the starting point to the current node The cumulative path length, For nodes To the end The Euclidean distance of , For nodes The coordinates of each selection The minimum node is expanded and the path is updated until the end is reached.

[0055] In some embodiments, the step of adjusting the local path by the artificial potential field method in combination with real-time obstacle detection specifically includes: in the process of the robot moving along the global path, detecting the surrounding environment in real time and updating the obstacle set including dynamic obstacles and static obstacles; when a dynamic obstacle is detected, using the artificial potential field method to perform local dynamic adjustment; comparing the local path generated by the artificial potential field method with the A The algorithm's global path fusion updates the length and energy consumption of each path segment and verifies whether the updated path meets the constraints.

[0056] Specifically, robots Along the global path When moving, use sensors such as lidar and ultrasound to detect surrounding obstacles in real time , update the obstacle set ,and ,The obstacle set includes dynamic obstacles, such as moving vehicles, and static obstacles, such as roadblocks.

[0057] Specifically, when a dynamic obstacle is detected, the artificial potential field method is used to perform local dynamic adjustment, and then the local path generated by the artificial potential field method is compared with A Global path of the algorithm Fusion, update each path segment Length and energy consumption , and verify whether the updated path satisfies the constraints.

[0058] In some embodiments, the steps of performing local dynamic adjustment using the artificial potential field method specifically include: constructing a gravitational field and a repulsive field, calculating the resultant force of gravity and repulsion, and determining the local movement vector of the robot; adjusting the motion trajectory of the robot according to the local movement vector, generating local path segments, and updating the path segment set; when the artificial potential field method leads to a local minimum, recalculating the resultant force by increasing the gravitational coefficient until it leaves the local minimum state.

[0059] Specifically, the potential field is constructed as a gravitational field and a repulsive field, where the gravitational field is the vehicle The position is the target point, and the gravitational force is: , where is the gravitational coefficient, is the vehicle position, is the robot position, For robots To the vehicle The Euclidean distance.

[0060] Specifically, the repulsive field takes the obstacle as the source point, and the repulsive force is: , where is the repulsion coefficient, For robots to obstacles distance, is the range of repulsive force.

[0061] Specifically, the total force is: , the direction of the resultant force determines the local movement vector of the robot; according to the total resultant force Adjust the direction and size of the robot The motion trajectory is generated, local path segments are generated, and the path segment set is updated. , and if the artificial potential field method leads to a local minimum, that is, the robot falls into a repulsive equilibrium, increase the gravitational coefficient , recalculate the resultant force until it is disengaged.

[0062] In some embodiments, the step of optimizing the objective function and introducing constraints to select the optimal matching solution from the initial matching solution specifically includes: minimizing the objective function using a particle swarm optimization algorithm, representing particles as candidate matching solutions, the particle position vector including the probability value of the robot and the vehicle matching, and determining the matching variable by discretizing the probability value; initializing the particle swarm, randomly generating the position and speed of the particles, wherein the initial position value of the vehicle outside the station is set to be in a high probability interval, and the initial position value of the vehicle inside the station is randomly distributed in a low probability interval; calculating the fitness of each particle by minimizing the objective function to guide the iterative optimization of the particle swarm and output the optimal matching solution.

[0063] Specifically, the system defines the allocation scheme of robots and vehicles requiring power supply as the optimization objective, and each particle represents a candidate allocation scheme. The particle position vector is , where Assemble for robots, For vehicle collection, For robots service vehicles The probability of . Finally assign the variable By discretization, if If it is greater than the preset threshold, , that is, robots service vehicles ,otherwise , that is, robots No service vehicles .

[0064] Specifically, a number of particles are randomly generated to form an initial particle group. The position of each particle and speed Initialized to a random value. To ensure that off-site emergency tasks are prioritized, the initial If set to a higher value, the initial value of vehicles in the station is randomly distributed in a lower range. Pre-calculated by the path planning algorithm as an initial distance reference.

[0065] Specifically, the fitness of each particle is calculated by the objective function F, and the goal is to minimize the objective function F. The calculation formula is: , where For fitness, is the objective function, is a small constant, the objective function The smaller it is, the greater the fitness.

[0066] In some embodiments, the particle swarm iterative optimization step specifically includes: updating the global optimal solution of the particle swarm, forcibly correcting it to include at least one off-station task if the global optimal solution does not cover the off-station task, updating the speed and position of each particle, imposing constraints on the off-station task probability to preferentially shift toward the global optimum or individual optimum, discretizing the updated current position of the particle into a matching solution, and iterating to continuously verify the constraints until a preset termination condition is met.

[0067] Specifically, the updated Discretization into allocation schemes ( If it is greater than the preset threshold ). And check whether the constraints are satisfied.

[0068] Specifically, the constraints are the prerequisites that must be followed by the robot to participate in the task, specifically:

[0069] Each vehicle is served by at most one robot to ensure that each vehicle waiting for power replenishment At most one robot Services, to avoid duplicate allocation, the calculation formula is: , where Assign variables to tasks, 1 represents the robot service vehicles , 0 means no service. And it also ensures that a single robot Service one vehicle at most , to prevent overload, the calculation formula is: .

[0070] Specifically, the robot's initial power It must be sufficient to cover the moving energy consumption and the returning energy consumption. The calculation formula is: , where is the effect of path distance and obstacle density on mobile energy consumption, is the environmental impact factor, which is determined by the path segment The temperature and humidity of the surrounding environment determine is the load influence factor, is the impact of path distance and obstacle density on return energy consumption.

[0071] Based on the robot's initial battery power and round-trip energy consumption, the off-site response distance must not exceed 30 kilometers. The response distance of off-site scenes cannot exceed 30 kilometers. And because the electricity consumption scenarios outside the station are more urgent, when an off-station task occurs, the robot must give priority to serving vehicles outside the station. Therefore, when an emergency power supply task occurs on the highway outside the highway intelligent power supply service station, the system will give priority to assigning off-station tasks to the robot.

[0072] Specifically, the iterative optimization steps are repeated until a termination condition is met, which includes reaching the maximum number of iterations or the global optimal F not changing significantly for several consecutive times.

[0073] Specifically, output the allocation plan corresponding to the global optimal position G , the task assignment timestamp and robot and vehicle matching results are recorded through smart contracts to ensure the process is transparent and cannot be tampered with. The final solution combines A The algorithm and artificial potential field method generate dynamic paths to complete task execution.

[0074] In some embodiments, the calculation formula for updating the speed and position of each particle is: ; Where, For particles In time speed, is the inertia weight, is the individual learning factor, is the global learning factor, and is a random number, and , is the optimal position of an individual, is the global optimal position, is the current position of the particle.

[0075] On the other hand, the present invention also provides an emergency energy supply scheduling system for a wheeled mobile robot, comprising: an acquisition module 10 for acquiring vehicle attribute information, vehicle location information and vehicle power information of vehicles inside and outside a supply station, and establishing an initial matching scheme between the robot and the vehicle; a priority module 20 for prioritizing the vehicles in the initial matching scheme according to the purpose or urgency of the vehicle attribute information, and calculating a priority penalty value in combination with an SOC weighting factor and a vehicle location weight, and calculating the path length, task energy consumption and load imbalance in the initial matching scheme through the vehicle location information and the vehicle power information; an optimization module 30 for constructing an objective function, minimizing the path length, the priority penalty value, the task energy consumption and the load imbalance to optimize the objective function, and introducing constraints to screen out the optimal matching scheme from the initial matching scheme, wherein the constraints include one-to-one service between the robot and the vehicle, the robot power meeting the vehicle charging task requirements, and off-station tasks taking precedence over on-station tasks; a path generation module 40 for generating a dynamic path based on the matching results of the robot and the vehicle in the optimal matching scheme.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0078] The above are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dispatching emergency energy supply for a wheeled mobile robot, characterized in that the steps include: Obtain vehicle attribute information, vehicle location information, and vehicle power information for vehicles inside and outside the supply station, and establish an initial matching plan between the robot and the vehicle; Prioritize the vehicles in the initial matching solution according to the purpose or urgency of the vehicle attribute information, calculate a priority penalty value based on a SOC weighting factor and a vehicle position weight, and calculate a path length, task energy consumption, and load imbalance in the initial matching solution based on the vehicle position information and the vehicle power information; An objective function is constructed based on the path length, the priority penalty value, the task energy consumption, and the load imbalance, the objective function is optimized, and constraints are introduced to screen out the optimal matching solution from the initial matching solutions, wherein the constraints include one-to-one service between the robot and the vehicle, the robot's power level meets the vehicle's charging task requirements, and off-site tasks take precedence over on-site tasks; A dynamic path is generated according to the matching result between the robot and the vehicle in the optimal matching solution.

2. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 1, characterized in that: The step of optimizing the objective function specifically includes: Minimize the path length, the priority penalty value, the task energy consumption, and the load imbalance.

3. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 1, characterized in that: The step of generating a dynamic path according to the matching result between the robot and the vehicle in the optimal matching solution specifically includes: Use A The algorithm plans the global path and adjusts the local path through the artificial potential field method in combination with real-time obstacle detection.

4. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 3, characterized in that: The use of A The steps of the algorithm to plan the global path include: The grid map is constructed with the robot's current position as the starting point and the vehicle's position as the end point, using A The algorithm calculates the globally optimal path.

5. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 3, characterized in that: The step of adjusting the local path by using the artificial potential field method in combination with real-time obstacle detection specifically includes: As the robot moves along the global path, it detects the surrounding environment in real time and updates the obstacle set containing dynamic obstacles and static obstacles; When a dynamic obstacle is detected, the artificial potential field method is used to perform local dynamic adjustments; The local path generated by the artificial potential field method is compared with A The algorithm's global path fusion updates the length and energy consumption of each path segment and verifies whether the updated path meets the constraints.

6. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 5, characterized in that: The steps of performing local dynamic adjustment using the artificial potential field method specifically include: Construct gravitational and repulsive fields, calculate the net force of gravitational and repulsive forces, and determine the local movement vector of the robot; Adjust the motion trajectory of the robot according to the local movement vector, generate a local path segment, and update the path segment set; When the artificial potential field method leads to a local minimum, the resultant force is recalculated by increasing the gravitational coefficient until it escapes from the local minimum state.

7. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 1, characterized in that: The step of optimizing the objective function and introducing constraints to select the optimal matching solution from the initial matching solutions specifically includes: A particle swarm optimization algorithm is used to minimize the objective function, particles are represented as candidate matching solutions, particle position vectors include probability values ​​of matching between the robot and the vehicle, and matching variables are determined by discretizing the probability values; Initialize the particle swarm and randomly generate the positions and velocities of the particles. The initial position values ​​of vehicles outside the station are set to be in the high probability interval, while the initial position values ​​of vehicles inside the station are randomly distributed in the low probability interval. The fitness of each particle is calculated by minimizing the objective function to guide the iterative optimization of the particle swarm and output the optimal matching solution.

8. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 7, characterized in that: The particle swarm iterative optimization steps specifically include: Update the global optimal solution of the particle swarm. If the global optimal solution does not cover the off-site tasks, it is forced to be corrected to include at least one off-site task. Update the speed and position of each particle, impose constraints on the probability of off-site tasks to prioritize the shift to the global optimum or individual optimum, discretize the current position of the updated particle into a matching solution, and iterate to continuously verify the constraints until the preset termination conditions are met.

9. The method for emergency energy supply scheduling of a wheeled mobile robot according to claim 8, characterized in that: The calculation formula for updating the speed and position of each particle is: ； ； Where, For particles In time speed, is the inertia weight, is the individual learning factor, is the global learning factor, and is a random number, and , is the optimal position of an individual, is the global optimal position, is the current position of the particle.

10. An emergency energy supply and dispatching system for a wheeled mobile robot, characterized in that: include: The acquisition module is used to obtain vehicle attribute information, vehicle location information, and vehicle power information of vehicles inside and outside the supply station, and establish an initial matching plan between the robot and the vehicle; a priority module, configured to prioritize vehicles in the initial matching solution according to the purpose or urgency of the vehicle attribute information, calculate a priority penalty value in combination with an SOC weighting factor and a vehicle position weight, and calculate a path length, task energy consumption, and load imbalance in the initial matching solution using the vehicle position information and the vehicle power information; an optimization module, configured to construct an objective function, minimize the path length, the priority penalty value, the task energy consumption, and the load imbalance, so as to optimize the objective function, and introduce constraints to select the optimal matching solution from the initial matching solutions, wherein the constraints include one-to-one service between the robot and the vehicle, the robot's power level meeting the vehicle's charging task requirements, and off-site tasks taking precedence over on-site tasks; A path generation module is used to generate a dynamic path according to the matching result between the robot and the vehicle in the optimal matching solution.

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