Robot control method and related equipment

Abstract

The invention discloses a robot control method and related equipment, and relates to the technical field of robots, and the method comprises the steps: determining a target robot corresponding to a target task; according to the resource consumption corresponding to the target task, detecting a resource satisfaction state of the target robot; when the resource satisfaction state is a resource insufficiency state, the target robot is controlled to execute a cabin changing task according to the missing resource type of the target robot; and when the resource satisfaction state is a resource sufficient state, controlling the target robot to execute the target task. Through resource consumption pre-detection and a dynamic supplement mechanism, the target robot can flexibly match task requirements, and adaptively supplement and continue to execute the task when resources are insufficient, so that the task success rate, execution efficiency and adaptability are improved.

Description

Technical Field

[0001] This application relates to the field of robot technology. More specifically, this application relates to a robot control method and related devices. Background Art

[0002] With the rapid development of intelligent technology, robots, as important automated execution units, have been widely used in fields such as intelligent manufacturing, service industries, and logistics transportation, especially in the hotel industry. However, during the execution of complex tasks by robots, the dynamic changes in their resource status pose many challenges to task management and scheduling. Especially in the case of insufficient resources, the problems of task interruption or failure are particularly prominent. Therefore, research on robot task management and resource scheduling is particularly important.

[0003] In related technologies, the task execution status of robots is usually managed by static allocation or preset resource thresholds. However, although this method is simple to implement, it cannot accurately capture the complexity of the dynamic changes in robot resources during task execution, and fails to provide a flexible resource replenishment mechanism. Especially in the scenario of resource exhaustion during task execution, the lack of an effective response mechanism leads to task failure, seriously affecting the overall operation efficiency and task reliability of the robot system. In addition, the existing technologies lack refined management of resource consumption detection and scheduling, resulting in a low degree of matching between resources and task requirements, restricting the task adaptation ability of robots and the efficient processing ability in multi-task scenarios. That is, there are technical problems in the low efficiency of robot task management and resource scheduling in the existing technologies. Summary of the Invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section of this application, which will be further elaborated in the Detailed Description section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0005] The robot control method and related devices provided by this application can enable the target robot to flexibly match task requirements through pre-detection of resource consumption and a dynamic replenishment mechanism, adaptively replenish and continue to execute tasks when resources are insufficient, thereby improving the task success rate, execution efficiency, and adaptability.

[0006] In a first aspect, the present application provides a robot control method, including: determining a target robot corresponding to a target task; detecting a resource satisfaction status of the target robot according to a resource consumption amount corresponding to the target task; when the resource satisfaction status is a resource shortage status, controlling the target robot to execute a cabin replacement task according to a missing resource type of the target robot; when the resource satisfaction status is a resource sufficient status, controlling the target robot to execute the target task.

[0007] In some embodiments, the determining the target robot corresponding to the target task includes: determining an execution path of an alternative robot according to a task route of the target task; determining the target robot from the alternative robots according to the execution path.

[0008] In some embodiments, the controlling the target robot to execute the cabin replacement task according to the missing resource type of the target robot includes: when the missing resource type is a lower cabin resource type, controlling the target robot to execute a lower cabin replacement task; when the missing resource type is an upper cabin resource type and the task type of the target task is a non-delivery type, controlling the target robot to execute an upper cabin replacement task; when the missing resource type is an upper cabin resource type and the task type of the target task is a delivery type, controlling the target robot to sequentially execute the target task and the upper cabin replacement task.

[0009] In some embodiments, the controlling the target robot to execute the lower cabin replacement task includes: controlling the target robot to go to an upper cabin resource replenishment area to perform placement processing on the original upper cabin; controlling a spare lower cabin to dock with the original upper cabin of the target robot to replace the original lower cabin of the target robot, generating a new target robot.

[0010] In some embodiments, the process of the upper cabin replacement task includes: controlling the target robot to go to an upper cabin resource replenishment area to perform placement processing on the original upper cabin; controlling the original lower cabin to dock with a spare upper cabin to replace the original upper cabin, generating a new target robot.

[0011] In some embodiments, a setting position of the upper cabin resource replenishment area is determined by a multi-objective optimization algorithm based on a guest room distribution density and a robot historical operation heat map.

[0012] In some embodiments, the resource consumption amount includes upper cabin power, lower cabin power, upper cabin cleaning resource usage amount, and lower cabin cleaning consumable usage duration.

[0013] Second aspect, the present application further provides a robot control device, including: a robot determination unit for determining a target robot corresponding to a target task; a status monitoring unit for detecting the resource satisfaction status of the target robot according to the resource consumption corresponding to the target task; a resource replenishment control unit for controlling the target robot to perform a cabin replacement task according to the missing resource type of the target robot when the resource satisfaction status is a resource shortage status; a task execution control unit for controlling the target robot to perform the target task when the resource satisfaction status is a resource sufficient status.

[0014] Third aspect, the present application further provides an electronic device, including: a memory and a processor, where the processor is configured to implement the steps of the robot control method described in the first aspect when executing a computer program stored in the memory.

[0015] Fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the steps of the robot control method described in the first aspect.

[0016] Fifth aspect, the present application further provides a computer program product, including a computer program or computer-executable instructions, where the computer program or computer-executable instructions, when executed by a processor, implement the robot control method provided by the embodiments of the present application.

[0017] In summary, through the detection of resource consumption before the target robot executes a task, the present application can predict in advance the resource satisfaction status of the robot, avoid task interruption or failure caused by resource shortage, and this pre-detection mechanism greatly improves the success rate of task execution; when the resources of the target robot are insufficient, a cabin replacement task mechanism for the missing resource type is provided to achieve dynamic resource replenishment. The target robot does not need to completely stop the task, but continues to complete the task after the resources are sufficient in an adaptive manner, improving the efficiency; through the resource detection and dynamic replenishment mechanism, the target robot can flexibly respond to various task scenarios. Whether it is a high-resource consumption task or a low-resource consumption task, it can ensure the dynamic matching of the resource status and the task requirements, improving the adaptability and flexibility of the robot task. In summary, through the resource consumption pre-detection and dynamic replenishment mechanism provided by the robot control method of the present application, the target robot can flexibly match the task requirements, adaptively replenish and continue to execute the task when the resources are insufficient, thereby improving the task success rate, execution efficiency and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiments. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of this specification. Also, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0019] Figure 1 It is a schematic flowchart of a robot control method provided by an embodiment of the present application;

[0020] Figure 2 It is a schematic structural diagram of a robot control device provided by an embodiment of the present application;

[0021] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0022] Terms in the specification, claims and drawings of the present application, such as "first", "second", "third", "fourth", etc. (if any), are used to distinguish similar objects and not to describe a specific order or sequence. Therefore, it is understood that under appropriate circumstances, these terms can be used interchangeably, so that the described embodiments can be implemented in a different order, unless there are special requirements in the drawings or descriptions. In addition, the terms "is" and "has" in the present application and any of their variants are intended to non-exclusively include all possible constituent elements. For example, a process, method, system, product or device including several steps or units does not necessarily have to be limited to the clearly listed steps or units, but may also include other steps or units not clearly listed, or steps or units inherent to the process, method, product or device.

[0023] In the present application, a "module" or "unit" refers to a computer program or a part of a computer program with a specific function, and works in cooperation with other relevant parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as a processing circuit or a memory), or a combination of both. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be a part of a larger module or unit.

[0024] The technical solutions in the present application will be described in detail below in conjunction with the drawings in the embodiments. It should be noted that the described embodiments are only a part of the present application, not all embodiments. In the following description, the "some embodiments" mentioned are only subsets of all possible embodiments, which can be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1It is a schematic flowchart of a robot control method provided by an embodiment of the present application. Exemplarily, refer to Figure 1 , the robot control method provided by the embodiment of the present application may include the following steps 101 to 104:

[0026] Step 101, determine the target robot corresponding to the target task;

[0027] In some examples, the target task is the specific work content that the robot needs to execute, which may include the task type (such as delivering items, cleaning, patrolling, etc.) and task parameters (such as location, time, execution priority, etc.). The target task may be issued by the hotel management system or the staff through the scheduling platform, or triggered by sensors or system events (such as a guest sending a request for delivering items); for example, a certain guest sends a request through the hotel App, and the task is "deliver a towel to Room 201". The target robot is the robot selected to execute the target task, and the robot can be specified according to the task type and fixed scheduling rules, or dynamically screened from the alternative robots by comprehensively considering factors such as the current location, remaining resources, and task priority of the robot; for example, robot A, which is the closest to Room 201 and has sufficient power, can be screened out as the target robot.

[0028] By implementing step 101, the most suitable robot is selected according to the task requirements, which can optimize the resource utilization efficiency; ensure that the task is assigned to the robot with the best execution ability, avoid the disorderly competition and mismatch of robot resources, and thus can avoid resource waste or insufficient performance.

[0029] Step 102, detect the resource satisfaction status of the target robot according to the resource consumption corresponding to the target task;

[0030] In some examples, the resource consumption is the amount of resources expected to be used during the execution of the target task, which may include power, cleaning consumable usage duration, etc. The resource consumption can be calculated according to the type, route, and execution requirements of the target task. For example, the task distance affects the power consumption, or the resource consumption can be predicted based on the execution records of similar tasks; for example, the task "deliver an item to Room 201 on the 5th floor" requires 10% power, 2 minutes, and 1 storage unit. The resource satisfaction status is the judgment result of whether the existing resources of the target robot can support the completion of the target task, which can be divided into "resource sufficient status" and "resource insufficient status". The current resource status, such as the remaining power, can be obtained through the built-in sensors or monitoring modules of the robot; for example, the current power of the target robot is 50%, and the task is expected to consume 10%, so the resource satisfaction status is "resource sufficient status".

[0031] By implementing step 102, the matching degree between the resources required for the task estimated in advance and the current resource reserve of the target robot can be effectively prevented from suddenly stopping work due to problems such as power exhaustion and insufficient materials during the execution of the robot, reducing the risk of task failure; the problem of insufficient resources can be detected in time, providing a basis for the subsequent cabin replacement task, and avoiding unnecessary waiting and repeated operations.

[0032] Step 103, when the resource satisfaction status is the insufficient resource status, control the target robot to perform the cabin replacement task according to the missing resource type of the target robot;

[0033] In some examples, the missing resource type refers to the specific resource category for which the target robot cannot complete the task due to insufficient resources, such as insufficient power or depletion of cleaning consumables; the current resource status of the target robot can be detected to identify which resources are insufficient; for example, if the target task consumes 20% of the power, but the current power of the robot is only 15%, the missing resource type is "power". The cabin replacement task means that the robot goes to the designated replenishment area to replace resources according to the missing resource type, such as replacing the battery module or replenishing cleaning consumables.

[0034] By implementing step 103, the target robot can continue the task after the resources are replenished through the cabin replacement task, avoiding stopping work due to resource exhaustion, realizing the robot's autonomous processing ability for the problem of insufficient resources, and reducing the maintenance and management costs; moreover, according to the specific missing resource type (such as power, tools, etc.), performing a targeted cabin replacement task can improve the replenishment efficiency.

[0035] Step 104, when the resource satisfaction status is the sufficient resource status, control the target robot to perform the target task;

[0036] Exemplarily, when the target robot detects that its current resources are sufficient to complete the target task, send an execution instruction to it, such as "deliver the beverage to Room 201", and the target robot plans a path according to the target task, moves to the target position and completes the task delivery, realizing a seamless service process.

[0037] By implementing step 104, it is ensured that the task is executed when the resources are sufficient, avoiding the risk of resource interruption during the task.

[0038] In summary, through the detection of the resource consumption of the target robot before performing a task, the embodiments of the present application can predict in advance the satisfaction status of the robot's resources, avoid task interruption or failure caused by insufficient resources, and this pre-detection mechanism greatly improves the success rate of task execution; when the resources of the target robot are insufficient, a cabin replacement task mechanism for the missing resource type is provided to achieve dynamic resource replenishment. The target robot does not need to completely stop the task, but continues to complete the task in an adaptive manner after the resources are sufficient, improving the efficiency; through the resource detection and dynamic replenishment mechanism, the target robot can flexibly cope with various task scenarios. Whether it is a high-resource consumption task or a low-resource consumption task, it can ensure the dynamic matching of the resource status and the task requirements, improving the adaptability and flexibility of the robot task. In summary, through the resource consumption pre-detection and dynamic replenishment mechanism provided by the embodiments of the present application, the target robot can flexibly match the task requirements, adaptively replenish and continue to execute the task when the resources are insufficient, thereby improving the task success rate, execution efficiency and adaptability.

[0039] In some embodiments, the foregoing step 101 may include: determining the execution path of the alternative robot according to the task route of the target task; and determining the target robot from the alternative robots according to the execution path.

[0040] In some examples, the task route is the traveling trajectory planned for the target task from the starting point to the ending point, which depends on the nature and destination of the target task and can be generated by the task scheduling system based on the environmental map and the position information of the task requirement points. The execution path is the complete movement trajectory for the alternative robot to complete the target task, from its current position to the task starting point and then along the task route. It can be calculated by using a path planning algorithm in combination with the real-time position of the alternative robot and the surrounding environmental obstacle information. The alternative robots are multiple robot individuals initially screened out, having certain conditions and being likely to undertake the target task, and can be obtained by considering and screening multiple aspects such as the basic attributes, performance indicators, and current states of the robot cluster. The target robot is the optimal robot individual finally selected to execute the target task.

[0041] Exemplarily, when the hotel front desk receives a guest's request of "need a set of toiletries to be delivered to Room 508", a delivery target task is generated. First, based on the hotel map and the location of Room 508, a task route can be planned starting from the storage room, going through the elevator to the 5th floor and then to the door of Room 508. Then, among all the deployable robots, several robots with storage functions and sufficient power are selected as alternative robots. Then, for each alternative robot, considering their current locations, such as some in the 2nd floor corridor and some in the lobby, the respective execution paths for them to go to the storage room and then deliver items along the task route are calculated. Finally, by comparing these execution paths, the robot with the shortest distance and the least need to avoid too many obstacles on the way is selected as the target robot to efficiently complete the delivery task and bring a convenient experience to the guest.

[0042] Through the implementation of the above embodiments, by combining the route of the target task and the execution paths of the alternative robots, the optimal target robot is selected, which can effectively reduce resource waste and path redundancy during task execution, improve the accuracy and efficiency of task allocation, and at the same time support the dynamic scheduling of multi-robot cooperation scenarios, thereby improving the overall operation efficiency of target task execution.

[0043] In some embodiments, the foregoing controlling the target robot to execute the cabin replacement task according to the missing resource type of the target robot may include: when the missing resource type is the lower cabin resource type, controlling the target robot to execute the lower cabin replacement task; when the missing resource type is the upper cabin resource type and the task type of the target task is a non-delivery type, controlling the target robot to execute the upper cabin replacement task; when the missing resource type is the upper cabin resource type and the task type of the target task is a delivery type, controlling the target robot to execute the target task and the upper cabin replacement task in sequence.

[0044] In some examples, the upper cabin refers to the module on the top of the robot, which is mainly used to store items, place cleaning tools or carry other functional modules, for example, to place items to be delivered, such as drinks, towels, takeaways, etc.; or to prevent disinfectant spraying devices, lighting, cameras, screens, voice interaction modules, etc. The lower cabin refers to the module at the bottom of the robot, which may include a drive system, a core power part and a cleaning consumables module. The lower cabin resource type refers to the resource type related to the storage or operation of the bottom of the robot, such as battery modules, cleaning consumables, etc. The chassis power or cleaning consumables remaining can be monitored by sensors. For example, the current chassis power of the target robot is insufficient to complete the task, and the missing resource type is determined to be "lower cabin resource type"; the upper cabin resource type refers to the resource type related to the storage on the top of the robot, including the upper cabin power, etc. The upper cabin resources can be judged based on the storage and power required for the target task. Whether the upper cabin resources are sufficient. The task of replacing the lower cabin refers to the operation of replacing the lower resource cabin to supplement the scarce resources performed by the target robot in response to insufficient resources in the lower cabin. Task type is the classification of target tasks, which can be divided according to the key actions and goals of the task instructions, and can be divided into delivery tasks (such as item delivery) and non-delivery tasks (such as cleaning or patrolling); for example, the non-delivery type can be a hotel robot performing a room cleaning task, the focus of the task is to use cleaning tools to clean the room, and does not involve the handling of items; for example, the robot inspects the lighting facilities and fire-fighting facilities in the public areas of each floor of the hotel according to the preset route; the delivery type is as mentioned above. When a hotel guest places an order by phone or system to have a charger delivered to the room, the core of the instruction received by the robot is delivery, that is, to deliver specific items from the storage room, front desk and other starting points to the guest room, which is the target task of the delivery type.

[0045] For example, when the battery level in the upper cabin is low, the target robot will be controlled to perform the delivery task first, but will not provide unnecessary additional charging services, such as display screen interaction, automatic door opening and closing, etc. After delivering the items to the target location, it will go to the nearest charging area or resource replenishment area to perform the power replenishment task. Through this dynamic scheduling mechanism, the timeliness of the delivery task can be guaranteed, and the target robot can complete the next assigned task.

[0046] Through the implementation of the above-mentioned embodiments, cabin change strategies are formulated according to the types of missing resources and task types (such as delivery and non-delivery tasks), which can solve the problems of insufficient different resources in a targeted manner, ensure the accuracy and timeliness of resource replenishment, optimize the task execution process, and further improve the adaptability and flexibility of the robot in complex scenarios.

[0047] In some embodiments, for the foregoing control target robot to perform the lower cabin replacement task, it may include: controlling the target robot to go to the upper cabin resource replenishment area and perform placement processing on the original upper cabin; controlling the spare lower cabin to dock with the original upper cabin of the target robot, replacing the original lower cabin of the target robot, and generating a new target robot.

[0048] In some examples, the upper cabin resource replenishment area is a specific area set specifically for replenishing the upper cabin resources of the robot, which can be set at some key positions in the hotel for the robot to reach quickly. The original upper cabin is the resource cabin originally installed on the upper part of the target robot before performing the upper cabin replacement task, which may contain upper cabin resources such as power, clean pure water, detergent, etc.; the original lower cabin is the resource cabin originally installed on the lower part of the target robot before performing the lower cabin replacement task, which is responsible for carrying consumable resources directly related to the task, such as cleaning rags, cleaning side brushes, cleaning main brushes, etc.; the spare lower cabin is a pre-reserved lower resource cabin filled with the required resources, used to replace the original lower cabin with insufficient resources of the robot. The new target robot is a robot that, after completing the lower cabin replacement task, has a new lower cabin resource configuration and can continue to efficiently execute subsequent tasks.

[0049] Through the implementation of the above embodiments, the specific steps of the lower cabin replacement task of the target robot are clarified to ensure the smooth completion of resource replenishment. With the modular design of the docking and replacement of the spare lower cabin and the original lower cabin, the standardization and high efficiency of the cabin replacement process can be achieved. After generating the new target robot, the target task execution can be immediately resumed, significantly improving the continuity and stability of the target task execution.

[0050] In some embodiments, the process of the foregoing upper cabin replacement task may include: controlling the target robot to go to the upper cabin resource replenishment area and perform placement processing on the original upper cabin; controlling the original lower cabin to dock with the spare upper cabin, replacing the original upper cabin, and generating a new target robot.

[0051] In some examples, the spare upper cabin is a pre-reserved upper cabin body equipped with complete key resources, used to replace the original upper cabin with resource shortages to ensure the continuous and stable operation of the target robot.

[0052] Through the implementation of the above embodiments, the upper cabin resource replenishment becomes more standardized and efficient. The dynamic interchange design of the upper and lower cabin resources can flexibly meet diverse task requirements, further improving the flexibility of resource management while ensuring the continuity of target task execution.

[0053] In some embodiments, the setting location of the upper cabin resource replenishment area is determined by a multi-objective optimization algorithm based on the guest room distribution density and the robot historical operation heat map.

[0054] In some examples, the guest room distribution density refers to the distribution of the number of guest rooms in different areas of a hotel, which can be used to evaluate the demand for robot services in a certain area. Statistical analysis can be carried out based on the floor layout and the number of rooms in the hotel, and combined with the occupancy rate of the guest rooms, the regional density weight can be dynamically adjusted. Taking a hotel as an example, if the hotel has three floors of guest rooms, with 20 rooms on the first floor, 50 rooms on the second floor, and 30 rooms on the third floor, through statistical analysis, the guest room distribution densities of each floor are 20%, 50%, and 30% respectively. At a certain time period, the occupancy rate of the first floor is 80%, the second floor is 60%, and the third floor is 90%. After dynamically adjusting the density weight in combination with the occupancy rate, the comprehensive density weight of the first floor is 16% (20% × 80%), the second floor is 30% (50% × 60%), and the third floor is 27% (30% × 90%). According to the adjusted density weight, the resource demand on the second floor is the largest. The upper cabin resource replenishment area can be preferentially arranged on the second floor, and a sub-optimal replenishment point can be set on the third floor to better meet the real-time service demand distribution.

[0055] The robot historical operation heat map is a visual chart generated based on the robot operation data, which is used to display the frequency of robot activities and the task intensity in different areas of the hotel. The time and location of task execution can be recorded through the robot operation log, and the areas with high operation frequency are displayed as high-heat areas, and the areas with low frequency are displayed as low-heat areas. For example, the corridors and areas near the elevators on the first floor are displayed as high-heat areas due to frequent tasks, and other areas are low-heat areas.

[0056] Exemplarily, the guest room distribution density data and the robot historical operation heat map can be input into a multi-objective optimization algorithm. The multi-objective optimization algorithm then comprehensively considers factors such as the construction cost of the upper cabin resource replenishment area, the distance from the elevator, and the availability of surrounding space, and performs complex calculations and simulations to obtain the final setting position of the upper cabin resource replenishment area. The multi-objective optimization algorithm can be a genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing algorithm (SA), ant colony optimization (ACO), or deep reinforcement learning, etc.

[0057] Through the implementation of the above embodiments, based on the multi-objective optimization algorithm of the guest room distribution density and the robot historical operation heat map, reasonably setting the position of the upper cabin resource replenishment area can significantly reduce the running distance and time of the target robot during the resource replenishment process, improve the resource replenishment efficiency, and provide support for the efficiency and scalability of the target task execution.

[0058] In some embodiments, the foregoing resource consumption may include the upper cabin power, the lower cabin power, the upper cabin cleaning resource usage, and the lower cabin cleaning consumable usage duration.

[0059] Exemplarily, the upper cabin power refers to the power independently supplied to the upper cabin module at the top of the robot, which is usually used to drive devices or tools related to the upper cabin functions, such as a cleaning liquid sprayer or a lighting device; the lower cabin power refers to the power of the driving module at the bottom of the robot (such as walking, elevator docking), which is directly related to the robot's moving ability and mission endurance; the upper cabin cleaning resource usage refers to the amount of resources consumed in the cleaning module at the top of the robot, such as clean water, disinfectant, or cleaning agent, etc.; the lower cabin cleaning consumable usage duration refers to the cumulative usage time of the consumables (such as cleaning cloth or variable cleaning side brush) in the cleaning module at the bottom of the robot, which is used to determine whether replacement or maintenance is required.

[0060] Through the implementation of the above embodiments, it is possible to comprehensively and accurately reflect the resource status during the execution of the target task, provide data support for resource detection, cabin replacement tasks, and task scheduling, thereby significantly improving the refinement level and reliability of the target task execution.

[0061] Furthermore, as an implementation of the foregoing method embodiments, the present application also provides a robot control device for implementing the foregoing method embodiments. This device embodiment corresponds to the foregoing method embodiments. For ease of reading, the details of the foregoing method embodiments will not be described one by one in this robot control device embodiment. However, it should be clear that the device in the embodiments of the present application can correspondingly implement all the contents of the foregoing method embodiments. As Figure 2 shown, the robot control device 20 includes: a robot determination unit 201, a status monitoring unit 202, a resource replenishment control unit 203, and a task execution control unit 204. Among them, the robot determination unit 201 is used to determine the target robot corresponding to the target task; the status monitoring unit 202 is used to detect the resource satisfaction status of the target robot according to the resource consumption corresponding to the target task; the resource replenishment control unit 203 is used to control the target robot to perform a cabin replacement task according to the missing resource type of the target robot when the resource satisfaction status is a resource shortage status; the task execution control unit 204 is used to control the target robot to perform the target task when the resource satisfaction status is a resource sufficient status.

[0062] In some embodiments, the robot determination unit 201 is further used to determine the execution path of the alternative robot according to the task route of the target task; and determine the target robot from the alternative robots according to the execution path.

[0063] In some embodiments, the resource replenishment control unit 203 is further configured to control the target robot to perform the task of replacing the lower cabin when the missing resource type is the lower cabin resource type; when the missing resource type is the upper cabin resource type and the task type of the target task is a non-delivery type, control the target robot to perform the task of replacing the upper cabin; when the missing resource type is the upper cabin resource type and the task type of the target task is a delivery type, control the target robot to perform the target task and the task of replacing the upper cabin in sequence.

[0064] In some embodiments, the resource replenishment control unit 203 is further configured to control the target robot to go to the upper cabin resource replenishment area to perform placement processing on the original upper cabin; control the spare lower cabin to dock with the original upper cabin of the target robot to replace the original lower cabin of the target robot, and generate a new target robot.

[0065] In some embodiments, the resource replenishment control unit 203 is further configured to control the target robot to go to the upper cabin resource replenishment area to perform placement processing on the original upper cabin; control the original lower cabin to dock with the spare upper cabin to replace the original upper cabin, and generate a new target robot.

[0066] In some embodiments, the setting position of the upper cabin resource replenishment area is determined by a multi-objective optimization algorithm based on the guest room distribution density and the robot historical operation heat map.

[0067] In some embodiments, the resource consumption includes the upper cabin power, the lower cabin power, the upper cabin cleaning resource usage amount, and the lower cabin cleaning consumable usage duration.

[0068] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will be caused to execute any step of the robot control method provided by the present application.

[0069] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it may also be various devices including one or any combination of the above memories.

[0070] In some embodiments, the computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0071] In some embodiments, the computer-executable instructions may, but do not necessarily, correspond to a file in a file system, and may be stored as part of a file that holds other programs or data, e.g., in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program being discussed, or in multiple cooperating files (such as files that hold one or more modules, subroutines, or portions of code).

[0072] In some embodiments, the computer-executable instructions may be deployed to execute on one electronic device, or on multiple electronic devices located at one site, or, on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0073] As Figure 3 shown, the present application also provides an electronic device 30, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned robot control method is implemented.

[0074] The present application also provides a computer program product, which includes a computer program or computer-executable instructions. The computer program or computer-executable instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, so that the electronic device executes any step of the robot control method described above in the present application.

[0075] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot control method, characterized in that: include: Determine the target robot corresponding to the target task; Detecting the resource satisfaction state of the target robot according to the resource consumption corresponding to the target task; When the resource satisfaction state is a resource shortage state, controlling the target robot to perform a cabin change task according to the missing resource type of the target robot; When the resource satisfaction state is a resource sufficient state, the target robot is controlled to execute the target task.

2. The robot control method according to claim 1, characterized in that: The step of determining a target robot corresponding to a target task comprises: Determining an execution path of the candidate robot according to the task route of the target task; The target robot is determined from the candidate robots according to the execution path.

3. The robot control method according to claim 1, characterized in that: The step of controlling the target robot to perform a cabin-changing task according to the missing resource type of the target robot comprises: When the missing resource type is a lower cabin resource type, controlling the target robot to perform a lower cabin replacement task; When the missing resource type is an upper cabin resource type, and the task type of the target task is a non-delivery type, controlling the target robot to perform an upper cabin replacement task; When the missing resource type is an upper cabin resource type, and the task type of the target task is a delivery type, the target robot is controlled to execute the target task and the upper cabin replacement task in sequence.

4. The robot control method according to claim 3, characterized in that: The controlling the target robot to perform the task of replacing the lower cabin includes: Controlling the target robot to go to the upper cabin resource replenishment area to place the original upper cabin; The spare lower cabin is controlled to dock with the original upper cabin of the target robot, replacing the original lower cabin of the target robot to generate a new target robot.

5. The robot control method according to claim 4, characterized in that: The process of replacing the upper cabin task includes: Controlling the target robot to go to the upper cabin resource replenishment area to place the original upper cabin; The original lower cabin is controlled to dock with the spare upper cabin, and the original upper cabin is replaced to generate a new target robot.

6. The robot control method according to any one of claims 1 to 5, characterized in that: The location of the upper cabin resource replenishment area is determined by a multi-objective optimization algorithm based on the guest room distribution density and the robot's historical operation heat map.

7. The robot control method according to any one of claims 1 to 5, characterized in that: The resource consumption includes the upper cabin power consumption, the lower cabin power consumption, the upper cabin cleaning resource usage and the lower cabin cleaning consumables usage time.

8. A robot control device, characterized in that: include: A robot determination unit, used to determine a target robot corresponding to a target task; A state monitoring unit, configured to detect a resource satisfaction state of the target robot according to a resource consumption amount corresponding to the target task; A resource supplement control unit, configured to control the target robot to perform a cabin change task according to the type of missing resources of the target robot when the resource satisfaction state is a resource shortage state; The task execution control unit is used to control the target robot to execute the target task when the resource satisfaction state is a resource sufficient state.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the robot control method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the robot control method according to any one of claims 1 to 7 are implemented.

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