Robot charging control device and method thereof

Abstract

This invention discloses a robot charging control device and method, relating to the field of robotics. The device includes: a central control module, a path planning module, a status monitoring module, a collaborative scheduling module, and a communication module. The central control module coordinates communication and data processing between the various modules. The path planning module utilizes machine learning and computer vision technologies to plan the optimal path for the robot from its current location to the charging station. The status monitoring module monitors the status of the charging station in real time via the Internet of Things (IoT) and provides data to the central control module. The collaborative scheduling module coordinates the charging of multiple robots under limited charging resources, preventing charging station overload. This invention acquires the robot's current position and environmental information through environmental perception sensors, uses an intelligent path planning algorithm to calculate the optimal charging path, and can dynamically adjust the charging path according to actual conditions, improving charging efficiency and task completion rate.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a robot charging control device and method. Background Technology

[0002] With the continuous development of robotics technology, robots are being applied more and more widely in various fields such as industry, agriculture, and services. Robots can effectively perform various tasks, including production line operation, logistics transportation, cleaning services, and medical assistance. However, robots inevitably face a critical problem during task execution: insufficient power supply. When a robot's battery is low, it needs to be recharged promptly to ensure its continuous and reliable task completion. Therefore, designing an efficient robot charging control device and method has become an important research topic.

[0003] In existing technologies, the following methods are commonly used to implement robot charging management:

[0004] Preset charging point method: When the robot's battery is low, it charges at preset charging points. This method is simple and easy to implement, but it lacks flexibility. The charging point positions are fixed and cannot be dynamically adjusted according to the actual situation, resulting in low charging efficiency.

[0005] Centralized charging management: This method uses a central control system to manage the charging tasks of multiple robots, assigning robots to charge at fixed times and locations. While this approach can optimize charging resources to some extent, it still has some drawbacks, such as uneven distribution of charging resources, low utilization of charging stations, and slow response times. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the problems existing in the current robot charging control device and method, the present invention is proposed.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] A robot charging control device, characterized in that the device comprises: a central control module, a path planning module, a status monitoring module, a collaborative scheduling module, and a communication module;

[0010] The central control module is used to coordinate communication and data processing between various modules; the path planning module uses machine learning and computer vision technology to plan the optimal path for the robot from its current location to the charging station.

[0011] The status monitoring module monitors the status of the charging station in real time through the Internet of Things and provides data to the central control module; the collaborative scheduling module is used to coordinate the charging of multiple robots under limited charging resources to avoid overloading of the charging station.

[0012] The communication module is responsible for data transmission and communication between modules, ensuring real-time updates and delivery of information.

[0013] As a preferred embodiment of the robot charging control device of the present invention, it further includes: a charging management module and a user interface module;

[0014] The charging management module controls the current and voltage regulation during the charging process to ensure the safety and efficiency of the charging process. The user interface module provides an interactive interface between the user and the system, allowing the user to view the charging status and scheduling.

[0015] A robot charging control method, the method comprising the following steps:

[0016] Step 1: First, the path planning module obtains the robot's current position and environmental information through the environmental perception sensor, calculates the optimal path using the path planning algorithm, and then the status monitoring module collects the status data of the charging station in real time and transmits it to the central control module.

[0017] Step 2: Based on the path planning results and the charging station status, the central control module instructs the robot to move to a suitable charging station, and the collaborative scheduling module manages the charging sequence and time of multiple robots to ensure the reasonable allocation of charging resources.

[0018] Step 3: The charging management module controls the current and voltage during the charging process to ensure safe and efficient charging. Finally, the user interface module provides an interactive interface between the user and the system, displaying the charging status and scheduling information.

[0019] As a preferred embodiment of the robot charging control method of the present invention, the path planning module obtains the robot's current position and environmental information through environmental perception sensors, and calculates the optimal path using a path planning algorithm, including the following steps:

[0020] The robot's current position and environmental information are obtained using environmental perception sensors, thus determining the robot's current position as follows: Real-time monitoring of the current at each charging station ,Voltage Idle state ;

[0021] The robot calculates its distance from its current location to each charging station. European distance :

[0022] = ;

[0023] For each charging station Calculate the state function:

[0024] in, , , These are all weighting coefficients, representing the importance of current, voltage, and idle state. Indicates whether the charging station is available. =1 indicates that the space is available. =0 indicates that the space is occupied;

[0025] Then construct the objective function. The utility value of each charging station is calculated by combining the path length and the charging station status. ,

[0026] Where n represents the number of charging stations. It is the attenuation coefficient; through The format allows charging stations with shorter distances to have a greater weight in the utility value calculation; through An exponential decay factor is introduced, which causes the utility value to decrease exponentially with increasing distance, ensuring that charging stations further away will not be easily selected even if they are in good condition.

[0027] As a preferred embodiment of the robot charging control device and method described in this invention, wherein: The availability of a charging station is indicated by a natural numerical value, where... =1 indicates that the space is empty. =0 indicates that the space is occupied.

[0028] In a preferred embodiment of the robot charging control device and method of the present invention, the utility value is set with three thresholds, namely: a high utility value threshold. Utility threshold and inefficiency threshold ;

[0029] The utility value of charging stations Greater than If so, the charging station is classified as a high-efficiency charging station;

[0030] The utility value of charging stations Between and If the value is between 0 and 1, the charging station is classified as a medium-utility charging station.

[0031] The utility value of charging stations Between and If the charging station is between these two options, then this charging station will be considered as an alternative.

[0032] The utility value of charging stations Less than If the value is low, it is considered an inefficient charging station and will not be selected.

[0033] As a preferred embodiment of the robot charging control device and method described in this invention, in step two, for each robot, a comprehensive decision value for charging stations with high and medium utilization values ​​is calculated, which comprehensively considers the state and path distance of each charging station; then:

[0034] ;

[0035] Where B represents the robot's current battery level, the integration time is T, and the larger the D value, the more likely the charging station with the largest comprehensive decision value D will be selected as the initial target charging station.

[0036] In a preferred embodiment of the robot charging control device and method of the present invention, in step two, the charging sequence and time of multiple robots are managed to ensure the reasonable allocation of charging resources. After all robots select a preliminary target charging station, the comprehensive scheduling value C is calculated.

[0037] ;

[0038] in, Indicates that it is being evaluated and dispatched. The path planning results of the robot Indicates the first The current battery level of the robot. Indicates the first The charging time requirement of each robot, where m represents the total number of robots and u represents the adjustment parameters. Based on the comprehensive scheduling value, the final target charging station and charging order of each robot are adjusted. The larger the C value, the higher the rationality of the scheduling and the higher the priority.

[0039] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the robot charging control method described above.

[0040] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described robot charging control method.

[0041] The beneficial effects of this invention are:

[0042] 1. This invention obtains the robot's current position and environmental information through environmental perception sensors, and uses an intelligent path planning algorithm to calculate the optimal charging path. It can dynamically adjust the charging path according to the actual situation, thereby improving charging efficiency and task completion rate.

[0043] 2. This invention designs a method for calculating comprehensive decision value and comprehensive scheduling value. By evaluating the utility value of charging stations, it achieves global optimization of charging decisions, enabling each robot to select the optimal charging scheme and ensuring the optimal allocation of charging resources globally. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0045] Figure 1 This is a schematic flowchart of a robot charging control method proposed in this invention;

[0046] Figure 2 This is a schematic diagram of the experimental simulation distribution of the robot and charging station for the robot charging control device and method proposed in this invention. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figure 1 As an embodiment of the present invention, a robot charging control device and method are provided, the device comprising:

[0051] It includes: a central control module, a path planning module, a status monitoring module, a collaborative scheduling module, and a communication module;

[0052] The central control module is used to coordinate communication and data processing between various modules; the path planning module uses machine learning and computer vision technology to plan the optimal path for the robot from its current location to the charging station.

[0053] The status monitoring module monitors the status of the charging stations in real time through the Internet of Things and provides data to the central control module, enabling the robot to understand the usage of each charging station in real time and avoid charging failures due to charging station overload or malfunction. The collaborative scheduling module is used to coordinate the charging of multiple robots under limited charging resources to avoid charging station overload. The communication module is responsible for data transmission and communication between the modules to ensure real-time updates and transmission of information.

[0054] The device also includes: a charging management module and a user interface module;

[0055] The charging management module controls the current and voltage regulation during the charging process to ensure the safety and efficiency of the charging process. The user interface module provides an interactive interface between the user and the system, allowing the user to view the charging status and scheduling.

[0056] The charging control method applied to the above-mentioned robot charging control device includes the following steps:

[0057] Step 1: First, the path planning module obtains the robot's current position and environmental information through the environmental perception sensor, calculates the optimal path using the path planning algorithm, and then the status monitoring module collects the status data of the charging station in real time and transmits it to the central control module.

[0058] Step 2: Based on the path planning results and the charging station status, the central control module instructs the robot to move to a suitable charging station, and the collaborative scheduling module manages the charging sequence and time of multiple robots to ensure the reasonable allocation of charging resources.

[0059] Step 3: The charging management module controls the current and voltage during the charging process to ensure safe and efficient charging. Finally, the user interface module provides an interactive interface between the user and the system, displaying the charging status and scheduling information.

[0060] Specifically, the path planning module acquires the robot's current position and environmental information through environmental perception sensors, and calculates the optimal path using a path planning algorithm, including the following steps:

[0061] The robot's current position and environmental information are obtained using environmental perception sensors, thus determining the robot's current position as follows: Real-time monitoring of the current at each charging station ,Voltage Idle state ;

[0062] The robot calculates its distance from its current location to each charging station. European distance :

[0063] = ;

[0064] For each charging station Calculate the state function:

[0065] in, , , These are all weighting coefficients, representing the importance of current, voltage, and idle state. Indicates whether the charging station is available. =1 indicates that the space is empty. =0 indicates that the space is occupied;

[0066] Then construct the objective function. The utility value of each charging station is calculated by combining the path length and the charging station status. ,

[0067] Where n represents the number of charging stations. It is the attenuation coefficient; through The format allows charging stations with shorter distances to have a greater weight in the utility value calculation; through An exponential decay factor is introduced, causing the utility value to decrease exponentially with increasing distance. This ensures that even if a charging station is in good condition, it will not be easily selected at a greater distance. The availability of a charging station is indicated by a natural numerical value, where... =1 indicates that the space is empty. =0 indicates that the space is occupied.

[0068] In addition, the utility value is set with three thresholds, namely: high utility value threshold. Utility threshold and inefficiency threshold ;

[0069] The utility value of charging stations Greater than If so, the charging station is classified as a high-efficiency charging station;

[0070] The utility value of charging stations Between and If the value is between 0 and 1, the charging station is classified as a medium-utility charging station.

[0071] The utility value of charging stations Between and If the charging station is between these two options, then this charging station will be considered as an alternative.

[0072] The utility value of charging stations Less than If the value is low, it is considered an inefficient charging station and will not be selected.

[0073] In step two, for each robot, the comprehensive decision value for charging stations with high and medium utility values ​​is calculated. This value takes into account the state and path distance of each charging station. Therefore:

[0074] ;

[0075] Where B represents the robot's current battery level, the integration time is T, and T is the charging station's response time, which is the time from when the robot sends a charging request to when the charging station is ready to accept the robot. Depending on the response speed of different charging stations, a suitable T value can be set. For example, if the average response time of a charging station is 5 minutes, then T can be set to 5-10 minutes so that the charging effect can be evaluated after the charging station responds. The larger the D value, the larger the comprehensive decision value D is selected as the initial target charging station.

[0076] In addition, in step two, the charging sequence and time of multiple robots are managed to ensure the reasonable allocation of charging resources. After all robots have selected the initial target charging station, the comprehensive scheduling value C is calculated.

[0077] ;

[0078] in, Indicates that it is being evaluated and dispatched. The path planning results of the robot Indicates the first The current battery level of the robot. Indicates the first The charging time requirement of each robot is calculated, where m represents the total number of robots and u represents the adjustment parameters. Based on the comprehensive scheduling value, the final target charging station and charging order of each robot are adjusted. The larger the C value, the higher the rationality of the scheduling and the higher the priority. The numerator combines the robot demand and the charging station resource situation, taking into account the remaining power of the charging station, the power demand of the robot, distance attenuation, and time factors. The denominator is the normalization processing of the charging demand of all robots.

[0079] To further verify the invention, the following simulation was conducted.

[0080] Reference Figure 2 Environment settings: 100x100 work area.

[0081] Ten charging stations are randomly distributed; 50 robots have their initial battery levels randomly distributed between 50% and 100%.

[0082] Task points are randomly distributed within the work area. Charging station parameters: Each charging station can accommodate 5 robots for charging simultaneously. The charging rate at each charging station is 10% per minute. Robot tasks: Each robot needs to execute 5 task points, and after completing each task point, it needs to return to the charging station to recharge. Evaluation metrics are as follows: Average charging efficiency: the average time for all robots to complete charging; Charging station utilization rate: the frequency of use of each charging station.

[0083] index Existing technology value Technical value of the invention Average charging efficiency (per minute) 490 450 Charging station utilization rate 0.95 0.85 Task completion rate 0.60 0.80

[0084] As can be seen, the average charging efficiency is significantly higher at 450 minutes compared to the 490 minutes of existing technologies, demonstrating a clear advantage in improving charging efficiency. Regarding charging station utilization, the existing technology achieves a utilization rate of 0.95, while the present invention achieves 0.85, indicating a superior ability to rationally allocate charging resources and avoid overuse of individual charging stations. Finally, the present invention achieves a task completion rate of 0.80, higher than the 0.60 of existing technologies, further demonstrating a significant advantage in improving task completion rates.

[0085] This embodiment also provides a computer device applicable to a robot charging control device and method thereof, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a robot charging control device and method thereof as proposed in the above embodiment.

[0086] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0087] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a robot charging control device and method as described in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A robot charging control method, characterized in that: The method includes the following steps: Step 1: First, the path planning module obtains the robot's current position and environmental information through the environmental perception sensor, calculates the optimal path using the path planning algorithm, and then the status monitoring module collects the status data of the charging station in real time and transmits it to the central control module. Step 2: Based on the path planning results and the charging station status, the central control module instructs the robot to move to a suitable charging station, and the collaborative scheduling module manages the charging sequence and time of multiple robots to ensure the reasonable allocation of charging resources. Step 3: The charging management module controls the current and voltage during the charging process to ensure safe and efficient charging. Finally, the user interface module provides an interactive interface between the user and the system, displaying the charging status and scheduling information. For each robot, a comprehensive decision value is calculated for charging stations with high and medium utility values. This value takes into account the state and path distance of each charging station. Therefore: ； Where B represents the robot's current battery level, the integration time is T, and the charging station with the largest comprehensive decision value D is selected as the initial target charging station. This represents the Euclidean distance from the robot's current location to the charging station. Manage the charging sequence and time of multiple robots to ensure the reasonable allocation of charging resources. After all robots select the initial target charging station, calculate the comprehensive scheduling value C. ； in, Indicates that it is being evaluated and dispatched. The path planning results of the robot Indicates the first The current battery level of the robot. Indicates the first The charging time requirement of each robot, where m represents the total number of robots and u represents the adjustment parameters. Based on the comprehensive scheduling value, the final target charging station and charging order of each robot are adjusted. The larger the C value, the higher the rationality of the scheduling and the higher the priority.

2. The robot charging control method according to claim 1, characterized in that: The path planning module acquires the robot's current position and environmental information through environmental perception sensors, and calculates the optimal path using a path planning algorithm, including the following steps: The robot's current position and environmental information are obtained using environmental perception sensors, thus determining the robot's current position as follows: Real-time monitoring of the current at each charging station ,Voltage Idle state ; The calculation robot travels from its current location to each charging station. European distance : = ； For each charging station Calculate the state function: in, , , These are all weighting coefficients, representing the importance of current, voltage, and idle state. Indicates whether the charging station is available. =1 indicates that the space is available. =0 indicates that the space is occupied; Then construct the objective function. The utility value of each charging station is calculated by combining the path length and the charging station status. , Where n represents the number of charging stations. It is the attenuation coefficient; through The format allows charging stations with shorter distances to have a greater weight in the utility value calculation; through An exponential decay factor is introduced, which causes the utility value to decrease exponentially with increasing distance, ensuring that charging stations further away will not be easily selected even if they are in good condition.

3. The robot charging control method according to claim 2, characterized in that: The The availability of a charging station is indicated by a natural numerical value, where... =1 indicates that the space is available. =0 indicates that the space is occupied.

4. The robot charging control method according to claim 3, characterized in that: The utility value is set with three thresholds, namely: high utility value threshold. Utility threshold and inefficiency threshold ; The utility value of charging stations Greater than If so, the charging station is classified as a high-efficiency charging station; The utility value of charging stations Between and If the value is between 0 and 1, the charging station is classified as a medium-utility charging station. The utility value of charging stations Between and If the charging station is between these two options, then this charging station will be considered as an alternative. The utility value of charging stations Less than If the value is low, it is considered an inefficient charging station and will not be selected.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the robot charging control method according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the robot charging control method according to any one of claims 1 to 4.

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