Charging robot layout method based on environmental perception

Abstract

The invention belongs to the technical field of shared charging, and particularly relates to a charging robot layout method based on environmental perception, which comprises the following steps of: acquiring crowd distribution information in a region by introducing an infrared sensing module, planning an advancing path of a robot in combination with information of a position module, and identifying an optimal staying position by using an image acquisition module; and the robot can intelligently avoid the crowded area and select the most suitable place to provide service. And meanwhile, the electric quantity state of the robot is continuously monitored, the recharging process is automatically started when the electric quantity is lower than a preset threshold value, and seamless switching between the robot about to run out of power and the standby robot is achieved in combination with dispatching of the central control system. The method not only improves service efficiency and user experience, but also effectively avoids service interruption caused by insufficient power of a single robot, and ensures continuity and stability of service.

Description

Technical Field

[0001] The present invention belongs to the technical field of shared charging, and in particular relates to a charging robot layout method based on environmental perception. Background Art

[0002] With the popularity of mobile devices, people's demand for charging services is growing. Especially in public places such as shopping malls, airports, and stations, people often need to temporarily charge their mobile phones or other electronic devices. Although traditional fixed charging stations can provide basic charging services, they have the problems of fixed locations and limited coverage, and cannot flexibly respond to changes in traffic at different time periods and locations. In recent years, charging robots based on environmental perception have gradually become a new solution, aiming to meet users' charging needs more efficiently in a mobile way.

[0003] In existing solutions, charging robots are usually equipped with a simple navigation system, which can move within a certain area according to a preset path and provide charging services at designated locations. However, these robots often lack the ability to effectively perceive the surrounding environment, resulting in low service efficiency in areas with high traffic density, and it is easy for multiple robots to gather in the same area, thus affecting the overall service quality and user experience. In addition, when the robot is low on power, how to quickly and effectively replace it to ensure service continuity is also an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a charging robot layout method based on environmental perception, which not only improves service efficiency and user experience, but also effectively avoids service interruption caused by insufficient power of a single robot, ensures the continuity and stability of the service, so as to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention adopts the following technical solution: a charging robot layout method based on environmental perception, comprising:

[0006] The charging robot obtains the crowd distribution information in the area through the infrared sensing module, and determines the relatively densely populated area based on the obtained crowd distribution information;

[0007] The charging robots plan the paths of each charging robot based on the information from the location module to ensure that the different robots maintain a reasonable distance from each other, and use the image acquisition module to identify and select a suitable location for stopping and providing service;

[0008] The robot pauses at the selected location and waits for user operation. The user completes identity authentication by scanning the QR code on the robot and then takes out the required power bank.

[0009] The charging robot continuously monitors its own power status and starts the recharging process when the power level is lower than the preset threshold. The charging robot sends the current coordinates and status information to the central control system, requesting the dispatch of a backup robot to take over the work;

[0010] After the charging robot receives instructions from the system, the robot that is about to run out of power automatically navigates to the nearest charging station for charging, while the other robot moves to the original location to continue providing service.

[0011] Preferably, the charging robot obtains the population distribution information in the area through the infrared sensing module, including:

[0012] Based on the obtained crowd distribution information, a crowd density function D(x,y) is defined to represent the density of people in different areas, where x and y represent the coordinates within the area;

[0013] The average crowd density value D_avg is calculated from the entire monitoring range using the formula D_avg = (1 / N)*Σ[D(x,y)], where N is the total number of selected sample points. The deployment strategy of the charging robot is adjusted based on the comparison result between D_avg and the preset threshold T. When D_avg>T, the number of robots is increased to cope with high demand, and vice versa.

[0014] After determining the optimal deployment strategy, the optimized path planning formula P_new=P_old+α(D(x,y)-D_avg) is used to update the route of each robot, where P_old is the current path and α is the adjustment coefficient.

[0015] Preferably, determining the relatively densely populated area based on the obtained crowd distribution information includes:

[0016] Based on the determined relatively densely populated areas, define the density distribution function F(u,v) to represent the crowd density at different positions u and v; calculate the standard deviation of the crowd density in each area S = sqrt[(1 / M)*Σ(F(u,v)-F_mean)^2], where M is the total number of selected sample points and F_mean is the average value of the crowd density in the area;

[0017] According to the relationship between the standard deviation S and the set threshold L, the robot's stay time T_stay in the area is adjusted. When S>L, the stay time is increased to meet more needs, that is, T_stay=T_base+β(SL), where T_base is the basic stay time and β is the adjustment factor;

[0018] The service plan of the charging robot is optimized according to the updated stay time T_stay to ensure longer service time during high-demand periods.

[0019] Preferably, the planning of the travel paths of the charging robots according to the position module information to ensure that a reasonable distance is maintained between different robots includes:

[0020] Based on the position module information, the relative distance function G(p,q) between each charging robot is defined, where p and q represent the current positions of any two robots, respectively. The minimum safe distance D_min=min{G(p,q)} between all robot pairs is calculated to ensure that the robots maintain a reasonable distance.

[0021] Adjust the travel speed V_adj of each robot according to the minimum safe distance D_min, using the formula V_adj = V_base-γ(D_min-D_safe), where V_base is the base speed, γ is the adjustment coefficient, and D_safe is the preset safe distance threshold;

[0022] The travel path of each charging robot is replanned according to the adjusted speed V_adj.

[0023] Preferably, the step of using the image acquisition module to identify and select a location suitable for staying for service includes:

[0024] The charging robot analyzes environmental features through the image acquisition module, defines the suitability scoring function H(w,x) for each potential stop point, where w and x represent the coordinates of different locations, and calculates the comprehensive score S_total = Σ[H(w,x)]*W_i for each location, where W_i is the weight factor corresponding to each feature;

[0025] According to the comprehensive score S_total, the most suitable location P_opt is selected as the service stop point, ensuring that P_opt satisfies S_total>S_threshold, where S_threshold is the preset minimum suitability threshold;

[0026] After determining P_opt, the formula D_stay=D_base+δ(S_total-S_avg) is used to adjust the robot's expected stay time D_stay at the location, where D_base is the base stay time, δ is the adjustment coefficient, and S_avg is the average of the comprehensive scores of all candidate locations.

[0027] Preferably, pausing at the selected position and waiting for user operation includes:

[0028] After pausing at the selected position, define the user proximity function J(m,n) to evaluate the distance between the user and the robot, where m and n represent the coordinates of the user and the robot, respectively, and calculate the user proximity index A_index=1 / (1+J(m,n)) to quantify the user proximity;

[0029] Adjust the robot's waiting strategy based on A_index. When A_index>A_threshold, activate the interactive interface to prepare for service. A_threshold is the preset proximity threshold.

[0030] The formula T_wait=T_min+η(A_index-A_threshold) is used to dynamically adjust the robot's minimum waiting time T_wait, where T_min is the basic waiting time and η is the adjustment factor.

[0031] Preferably, the user completes identity authentication by scanning the QR code on the robot and then takes out the required power bank, including:

[0032] After the user scans the QR code on the robot, the identity authentication function K(z) is defined, where z represents the verification information provided by the user; the verification score V_score = Σ[K(z)]*V_i is calculated, where V_i is the weight factor corresponding to each verification information;

[0033] When V_score reaches the preset verification threshold V_threshold, the user's identity is confirmed and the power bank is unlocked;

[0034] According to the user identity authentication result and request, the formula C_release=C_base+θ(V_score-V_threshold) is used to determine the power bank release quantity C_release, where C_base is the basic release quantity and θ is the adjustment coefficient.

[0035] Preferably, the charging robot continuously monitors its own power state, and starts the recharging process when the power is lower than a preset threshold, including: the charging robot continuously monitors its own power state E, and defines a power state function L(t), where t represents time;

[0036] Calculate the percentage of current power to full power state P = L(t) / L_max*100%, L_max is the power value in full power state;

[0037] When P is lower than the preset threshold value P_threshold, the recharge process is started, and the formula T_charge = T_base + ρ (P_threshold-P) is used to determine the estimated charging time T_charge, where T_base is the basic charging time and ρ is the adjustment coefficient;

[0038] Plan the best path back to the nearest charging station based on T_charge.

[0039] Preferably, the charging robot sends the current coordinates and status information to the central control system, requesting the dispatch of a backup robot to take over the work, including:

[0040] The charging robot sends the current coordinates B and state information S to the central control system, and defines a replacement efficiency function M(B,S) to evaluate the efficiency of dispatching the backup robot;

[0041] Calculate the replacement efficiency index E_index = M(B, S) / M_max*100%, where M_max is the maximum replacement efficiency under ideal conditions;

[0042] The most suitable backup robot U_opt is determined based on E_index, and the dispatch time T_dispatch of the backup robot is adjusted using the formula T_dispatch=T_base+σ(E_index-E_threshold), where T_base is the base dispatch time, σ is the adjustment coefficient, and E_threshold is the preset replacement efficiency threshold.

[0043] Preferably, after the charging robot receives the instruction from the system, the robot that is about to run out of power automatically navigates to the nearest charging station for charging, while the other robot moves to the original position to continue to provide services, including:

[0044] After receiving the instruction from the system, the charging robot defines the shortest path function N(a,b) to calculate the shortest path from the current position a to the nearest charging station b;

[0045] Determine the optimal navigation route R_opt based on N(a,b), and use the formula D_path=D_min+τ(N(a,b)-N_avg) to adjust the actual travel distance D_path, where D_min is the theoretical shortest distance, N_avg is the average path length, and τ is the adjustment coefficient;

[0046] The robot that is about to run out of power automatically navigates along R_opt to the nearest charging station for charging. At the same time, another backup robot moves to that location based on the original position coordinates to continue providing service.

[0047] Technical effects and advantages of the present invention: Compared with the prior art, the charging robot layout method based on environment perception proposed by the present invention has the following advantages:

[0048] The present invention introduces an infrared sensing module to obtain information about the distribution of people in the area, plans the robot's path in combination with the information from the location module, and uses an image acquisition module to identify the best stop position, ensuring that the robot can intelligently avoid crowded areas and choose the most suitable location to provide services. At the same time, by continuously monitoring its own power status and automatically starting the recharging process when the power is lower than the preset threshold, combined with the scheduling of the central control system, seamless switching between a robot that is about to run out of power and a backup robot is achieved. This method not only improves service efficiency and user experience, but also effectively avoids service interruptions caused by insufficient power of a single robot, ensuring the continuity and stability of the service. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a charging robot layout method based on environmental perception of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] The present invention provides Figure 1 A charging robot layout method based on environment perception is shown, comprising:

[0052] Step 1: The charging robot obtains the population distribution information in the area through the infrared sensing module; specifically includes:

[0053] The charging robot is equipped with an infrared sensing module that can scan and collect data on the distribution of people in the area in real time. Based on the obtained crowd distribution information, a crowd density function D(x,y) is defined to represent the density of people in different areas, where x and y represent the coordinates in the area; this function is used to represent the density of people at different locations.

[0054] In order to evaluate the average crowd density within the entire monitoring range, the system will calculate the average crowd density value from all sample points, and use the formula D_avg = (1 / N)*Σ[D(x,y)] to calculate the average crowd density value D_avg from the entire monitoring range, where N is the total number of selected sample points; this process ensures an accurate quantitative indicator for the overall population distribution.

[0055] The deployment strategy of the charging robot is adjusted according to the comparison result of D_avg and the preset threshold T. When D_avg>T, the number of robots is increased to cope with high demand, otherwise it is reduced. That is, if D_avg is greater than the preset threshold T, it indicates that the population in the current area is relatively dense, and the system will increase the number of charging robots to meet higher service needs; conversely, if D_avg is less than or equal to T, the number of robots will be reduced to avoid waste of resources.

[0056] After determining the optimal deployment strategy, the system will use the optimized path planning formula P_new = P_old + α (D (x, y) - D_avg) to update the route of each robot, where P_old is the current path and α is the adjustment coefficient. Specifically, the system will adjust the route of each robot based on the difference between the current path P_old and the crowd density function D (x, y) and the average density D_avg. This adjustment enables the robot to avoid high-density areas more intelligently, thereby improving the overall service efficiency and service quality.

[0057] Step 2: Determine the relatively densely populated areas based on the obtained population distribution information; specifically include:

[0058] Based on the determined relatively densely populated areas, the system will define a density distribution function F(u,v) to represent the density of people at different locations u and v; where u and v represent the coordinates of different locations. This function is used to represent the density of people at each specific location, thereby helping the system identify which areas are relatively densely populated.

[0059] In order to evaluate the fluctuation of the population density in each area, the system will calculate the standard deviation of the population density in each area S = sqrt[(1 / M)*Σ(F(u,v)-F_mean)^2], where M is the total number of selected sample points and F_mean is the average population density in the area; the standard deviation S reflects the degree of dispersion of the population density value in the area relative to the average value F_mean. By calculating the standard deviation, the system can more accurately determine which areas are not only densely populated, but also have large density changes and may require more service time.

[0060] According to the relationship between the standard deviation S and the set threshold L, the robot's stay time T_stay in the area is adjusted. When S>L, the stay time is increased to meet more needs, that is, T_stay=T_base+β(SL), where T_base is the basic stay time and β is the adjustment factor; specifically, if the standard deviation S is greater than the preset threshold L, it indicates that the population density in the area changes greatly and there may be more temporary needs, so the system will increase the robot's stay time to meet the needs of more users. On the contrary, if S is less than or equal to L, the basic stay time T_base is kept unchanged to avoid wasting resources.

[0061] The service plan of the charging robot is optimized according to the updated stay time T_stay to ensure longer service during high-demand periods. This means that during high-demand periods, the robot will provide longer service in areas with dense populations and large density changes, thereby improving service efficiency and user experience.

[0062] Using the density distribution function F(u,v) and standard deviation S, the system can more accurately identify areas with relatively dense populations and large density changes, providing a scientific basis for subsequent deployment and service strategies. By dynamically adjusting the robot's residence time in a specific area, the system can better adapt to changes in the flow of people in different time periods, ensuring sufficient service time during peak demand periods, thereby improving user satisfaction. Based on the real-time crowd distribution and density changes, the system can intelligently allocate robot resources to ensure the longest service time in the most needed places, thereby improving overall service efficiency. By providing longer service time during high-demand periods, user waiting time is reduced, the user experience is improved, and user satisfaction and trust in the service are enhanced.

[0063] Step 3: The charging robots plan the paths of each charging robot based on the location module information to ensure that the different robots maintain a reasonable distance from each other; specifically, the following steps are included:

[0064] Each charging robot is equipped with a location module. Based on the location module information, the relative distance function G(p,q) between each charging robot is defined, where p and q represent the current positions of any two robots respectively.

[0065] To ensure that the robots maintain a reasonable distance, the system calculates the minimum safe distance D_min=min{G(p,q)} between all robot pairs to ensure that the robots maintain a reasonable distance; specifically, the system traverses all robot pairs and finds the minimum distance between them. In this way, the system can determine the shortest distance between robots in the current environment, thereby avoiding clustering or collision of robots.

[0066] Adjust the travel speed V_adj of each robot according to the minimum safety distance D_min, using the formula V_adj = V_base-γ(D_min-D_safe), where V_base is the reference speed, γ is the adjustment coefficient, and D_safe is the preset safety distance threshold; if D_min is less than the preset safety distance threshold D_safe, it indicates that the distance between the robots is too close and there is a potential risk of collision. At this time, the system will reduce the travel speed of the robots to increase the distance between them. On the contrary, if D_min is greater than or equal to D_safe, keep the reference speed V_base unchanged to ensure service efficiency.

[0067] The travel path of each charging robot is replanned according to the adjusted speed V_adj. This ensures that the robot can reach the destination efficiently while maintaining a safe distance and avoiding conflicts with other robots during the movement.

[0068] Step 4: The charging robot uses the image acquisition module to identify and select a suitable location for staying and providing service; specifically, it includes:

[0069] The charging robot is equipped with an image acquisition module that can capture images of the surrounding environment in real time. The charging robot analyzes environmental features through the image acquisition module, which may include but are not limited to: crowd density, obstacle distribution, lighting conditions, space size, etc.

[0070] Based on the extracted environmental features, the system defines the suitability scoring function H(w,x) for each potential stop point, where w and x represent the coordinates of different locations. This function is used to evaluate the comprehensive suitability of each potential stop point. Different environmental features are assigned different weight factors W_i, which reflect the importance of each feature to the stop point selection.

[0071] Calculate the comprehensive score of each location S_total = Σ[H(w,x)]*W_i, where W_i is the weight factor corresponding to each feature; the higher the comprehensive score, the more suitable the location is as a service stop. In this way, the system can quantify the suitability of each location and make a scientific selection.

[0072] According to the comprehensive score S_total, the most suitable location P_opt is selected as the service stop point, ensuring that P_opt satisfies S_total>S_threshold, where S_threshold is the preset minimum suitability threshold;

[0073] After determining P_opt, the formula D_stay=D_base+δ(S_total-S_avg) is used to adjust the robot's expected stay time D_stay at the location, where D_base is the base stay time, δ is the adjustment coefficient, and S_avg is the average of the comprehensive scores of all candidate locations. If S_total is higher than the average score S_avg of all candidate locations, the system will increase the stay time; otherwise, it will reduce the stay time. This ensures that the robot provides longer service at the most suitable location, thereby improving service efficiency and user experience.

[0074] By selecting the most suitable stop point and adjusting the stop duration, the robot can provide services at the most appropriate time and place, improving overall service efficiency and service quality. By providing services for a longer time at the best location, the user's waiting time is reduced, and the user's experience and service satisfaction are improved. The system can dynamically adjust the stop point and stop duration according to environmental changes. It has strong adaptability and flexibility and is suitable for various complex and changing environments.

[0075] Step 5: Pause at the selected position and wait for user operation; specifically including:

[0076] When the charging robot pauses at the selected location, the system defines a user proximity function J(m,n) to evaluate the distance between the user and the robot, where m and n represent the coordinates of the user and the robot, respectively. This function is used to evaluate the distance between the user and the robot. By monitoring the user's movement in real time, the system can dynamically calculate the proximity between the user and the robot.

[0077] In order to quantify the user's proximity, the system calculates the user proximity index A_index = 1 / (1+J(m,n)) based on the user proximity function J(m,n) to quantify the user's proximity; the larger the value of A_index, the closer the user is to the robot. Specifically, A_index is calculated by dividing 1 by 1 plus the distance between the user and the robot, so that a value between 0 and 1 can be obtained, which is convenient for subsequent processing.

[0078] According to the calculated user proximity index A_index, the system will dynamically adjust the robot's waiting strategy. When A_index>A_threshold, the interactive interface will be activated to prepare for service. A_threshold is the preset proximity threshold. If A_index is greater than the preset proximity threshold A_threshold, it indicates that the user is close enough to the robot. At this time, the system will activate the interactive interface and prepare to provide services. This ensures that the robot can respond in time when the user approaches, improving the user experience.

[0079] To further optimize the waiting time, the system uses A_index and A_threshold to dynamically adjust the robot's minimum waiting time T_wait. The formula T_wait = T_min + η (A_index-A_threshold) is used to dynamically adjust the robot's minimum waiting time T_wait, where T_min is the basic waiting time and η is the adjustment factor. If A_index is higher than A_threshold, the system will increase T_wait to ensure that the robot has enough time to wait for the user to complete the operation; conversely, if A_index is lower than A_threshold, the system will reduce T_wait to avoid unnecessary waiting time. This ensures that the robot can respond quickly when the user approaches, and does not waste too much time when the user is far away.

[0080] Step 6: The user completes identity verification by scanning the QR code on the robot and takes out the required power bank; specifically:

[0081] After the user scans the QR code on the robot at the selected stop point, the system will start the identity verification process. The system defines an identity verification function K(z), where z represents the verification information provided by the user; this verification information may include but is not limited to the user's mobile phone number, ID number, facial recognition information, etc.

[0082] The system will calculate a verification score V_score based on the verification information provided by the user and the corresponding weight factor V_i. The verification score V_score = Σ[K(z)]*V_i, where V_i is the weight factor corresponding to each verification information. Different verification information will be assigned different weights to reflect its importance to identity authentication. For example, mobile phone numbers and ID card numbers may have higher weights, while other auxiliary information may have lower weights. By weighted summation, the system can obtain a comprehensive verification score.

[0083] When the calculated verification score V_score reaches the preset verification threshold V_threshold, the system will confirm the user's identity. This means that the verification information provided by the user is reliable enough to unlock the power bank for the user to use. If V_score is lower than V_threshold, the system will prompt the user to re-provide or supplement the verification information until the requirement is met;

[0084] Once the user's identity is confirmed, the system will use the formula C_release=C_base+θ(V_score-V_threshold) to determine the number of power banks released C_release based on the user's identity verification results and requests, where C_base is the basic release number and θ is the adjustment coefficient. If V_score is higher than V_threshold, the system will increase the number of power banks released; otherwise, the basic release number C_base will remain unchanged. This ensures that users with high credit can obtain more power banks, improving service flexibility and user experience.

[0085] Step 7: The charging robot continuously monitors its own power status and starts the recharging process when the power is lower than the preset threshold; specifically, it includes:

[0086] The charging robot is equipped with a battery management system that can continuously monitor its own power state. The charging robot continuously monitors its own power state E and defines a power state function L(t), where t represents time;

[0087] In order to quantify the relationship between the current power and the full power state, the system will calculate the percentage of the current power to the full power state P = L(t) / L_max*100%, where L_max is the power value in the full power state; specifically, the system will divide the current power L(t) by the power value in the full power state L_max, and then multiply by 100% to get the current power percentage P. This can intuitively represent the remaining power of the robot.

[0088] When the calculated P is lower than the preset threshold value P_threshold, it means that the robot's power is not enough to continue to provide services and needs to return to the charging station for charging in time. Start the recharging process and use the formula T_charge = T_base + ρ (P_threshold-P) to determine the estimated charging time T_charge, where T_base is the basic charging time and ρ is the adjustment factor; this formula takes into account the difference between the basic charging time T_base and the power percentage P and the threshold P_threshold. In this way, the system can dynamically adjust the charging time to ensure that the robot can recover to a sufficient power state in the shortest time.

[0089] Once the estimated charging time T_charge is determined, the system will plan the best path back to the nearest charging station based on this time. Path planning will take into account multiple factors, such as distance, road conditions, and the availability of charging stations, to ensure that the robot can return to the charging station efficiently and safely. The system may use navigation algorithms to find the optimal path and generate specific travel instructions.

[0090] Step 8: The charging robot sends the current coordinates and status information to the central control system, requesting the dispatch of a backup robot to take over the work; specifically including:

[0091] When the charging robot starts the recharging process, it sends its current coordinates B and status information S to the central control system. This information includes the robot's current position, power status, service status, etc. In this way, the central control system can grasp the specific status of each robot in real time. After receiving the information, the central control system will define a replacement efficiency function M(B,S) to evaluate the efficiency of dispatching backup robots; this function comprehensively considers the robot's current position B and status information S, as well as other factors that may affect the replacement efficiency, such as the backup robot's position, power status, task load, etc.

[0092] In order to quantify the efficiency of dispatching backup robots, the system will calculate the replacement efficiency index E_index = M(B,S) / M_max*100%, where M_max is the maximum replacement efficiency under ideal conditions; specifically, the system will divide the value of the replacement efficiency function M(B,S) by the maximum replacement efficiency M_max under ideal conditions, and then multiply it by 100% to obtain E_index. This can intuitively represent the efficiency level of dispatching backup robots.

[0093] According to the calculated replacement efficiency index E_index, the system will select the most suitable backup robot U_opt to replace the robot that is about to return to the charging station. During the selection process, the system will give priority to those backup robots with higher replacement efficiency indexes to ensure efficient service continuity, and use the formula T_dispatch=T_base+σ(E_index-E_threshold) to adjust the dispatch time T_dispatch of the backup robot, where T_base is the basic dispatch time, σ is the adjustment coefficient, and E_threshold is the preset replacement efficiency threshold.

[0094] Specifically, once the most suitable backup robot U_opt is determined, the system will adjust the dispatch time T_dispatch of the backup robot according to the replacement efficiency index E_index. If E_index is higher than the preset replacement efficiency threshold E_threshold, the system will reduce the dispatch time; otherwise, it will increase the dispatch time. This ensures that the backup robot can arrive at the designated location at the most appropriate time and provide seamless service replacement.

[0095] Step 9: After the charging robot receives the command from the system, the robot that is about to run out of power automatically navigates to the nearest charging station for charging, while the other robot moves to the original location to continue providing services; specifically, it includes:

[0096] When the charging robot receives the command from the central control system, the system will define a shortest path function N(a,b) to calculate the shortest path from the current location a to the nearest charging station b. This function takes into account the current location information, the location information of the charging station, and possible path selection;

[0097] Based on the shortest path function N(a,b), the system determines the optimal navigation route R_opt. This route is not only the shortest path, but also takes into account factors such as obstacles and traffic conditions in the actual environment to ensure that the robot can reach the charging station safely and efficiently. The actual travel distance D_path is adjusted using the formula D_path=D_min+τ(N(a,b)-N_avg), where D_min is the theoretical shortest distance, N_avg is the average path length, and τ is the adjustment coefficient;

[0098] By adjusting the actual travel distance D_path, the system can ensure that the robot can avoid congested or obstacle-dense areas during navigation and improve navigation efficiency. Specifically, the system dynamically adjusts the actual travel distance based on the difference between the theoretical shortest distance D_min and the average path length N_avg, thereby ensuring that the robot can choose the optimal path.

[0099] When the robot is about to run out of power, it will automatically navigate along the determined optimal navigation route R_opt to the nearest charging station for charging. The robot will move according to the generated path instructions and monitor the situation on the path in real time to ensure that it arrives at the charging station safely. Once it arrives at the charging station, the robot will automatically connect to the charging device and start charging.

[0100] At the same time, the central control system will dispatch another backup robot to the original location of the robot that is about to run out of power. The backup robot moves to that location based on the coordinates of the original location and continues to provide charging services. The backup robot will follow the same path planning method to ensure that it reaches the designated location quickly and safely.

[0101] Through the above process, the system can ensure that when one robot returns to the charging station, another robot can seamlessly take over its work, thus ensuring the continuity and stability of the service. The backup robot will continue to provide services at the original location to ensure that users will not be affected by insufficient power of a single robot.

[0102] By defining the shortest path function and determining the optimal navigation route, the system can intelligently plan the path of the robot from its current location to the charging station, ensuring that the robot can reach the charging station efficiently and safely. The system can dynamically adjust the actual travel distance according to actual conditions, avoid congestion and obstacles, and improve navigation efficiency and safety.

[0103] By timely replacing the backup robot, the system can ensure service continuity and avoid service interruptions caused by insufficient power of a single robot. Users do not need to worry about not being able to obtain a power bank due to insufficient power of the robot, which improves the user experience and service satisfaction. Through intelligent scheduling and path planning, the system can ensure that each robot can return to the charging station in time when needed, and the backup robot will take over the work, improving the reliability and stability of the overall service.

[0104] In summary, the present invention realizes efficient and reliable charging services through intelligent planning of the charging robot's path and timely scheduling of the backup robot, thereby significantly improving the service quality and user experience.

[0105] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging robot layout method based on environment perception, characterized in that: include: The charging robot obtains the crowd distribution information in the area through the infrared sensing module, and determines the relatively densely populated area based on the obtained crowd distribution information; The charging robots plan the paths of each charging robot based on the information from the location module to ensure that the different robots maintain a reasonable distance from each other, and use the image acquisition module to identify and select a suitable location for stopping and providing service; The robot pauses at the selected location and waits for user operation. The user completes identity authentication by scanning the QR code on the robot and then takes out the required power bank. The charging robot continuously monitors its own power status and starts the recharging process when the power level is lower than the preset threshold. The charging robot sends the current coordinates and status information to the central control system, requesting the dispatch of a backup robot to take over the work; After the charging robot receives instructions from the system, the robot that is about to run out of power automatically navigates to the nearest charging station for charging, while the other robot moves to the original location to continue providing service.

2. The method for charging robot layout based on environment perception according to claim 1 is characterized in that: The charging robot obtains the population distribution information in the area through the infrared sensing module, including: Based on the obtained crowd distribution information, a crowd density function D(x,y) is defined to represent the density of people in different areas, where x and y represent the coordinates within the area; The average crowd density value D_avg is calculated from the entire monitoring range using the formula D_avg = (1 / N)*Σ[D(x,y)], where N is the total number of selected sample points. The deployment strategy of the charging robot is adjusted based on the comparison result between D_avg and the preset threshold T. When D_avg>T, the number of robots is increased to cope with high demand, and vice versa. After determining the optimal deployment strategy, the optimized path planning formula P_new=P_old+α(D(x,y)-D_avg) is used to update the route of each robot, where P_old is the current path and α is the adjustment coefficient.

3. The method for charging robot layout based on environment perception according to claim 1, characterized in that: Determining a relatively densely populated area based on the obtained crowd distribution information includes: Based on the determined relatively densely populated areas, define the density distribution function F(u,v) to represent the crowd density at different positions u and v; calculate the standard deviation of the crowd density in each area S = sqrt[(1 / M)*Σ(F(u,v)-F_mean)^2], where M is the total number of selected sample points and F_mean is the average value of the crowd density in the area; According to the relationship between the standard deviation S and the set threshold L, the robot's stay time T_stay in the area is adjusted. When S>L, the stay time is increased to meet more needs, that is, T_stay=T_base+β(SL), where T_base is the basic stay time and β is the adjustment factor; The service plan of the charging robot is optimized according to the updated stay time T_stay to ensure longer service time during high-demand periods.

4. The method for charging robot layout based on environment perception according to claim 1 is characterized in that: The planning of the travel paths of each charging robot according to the position module information to ensure that a reasonable distance is maintained between different robots includes: Based on the position module information, the relative distance function G(p,q) between each charging robot is defined, where p and q represent the current positions of any two robots, respectively. The minimum safe distance D_min=min{G(p,q)} between all robot pairs is calculated to ensure that the robots maintain a reasonable distance. Adjust the travel speed V_adj of each robot according to the minimum safe distance D_min, using the formula V_adj = V_base-γ(D_min-D_safe), where V_base is the base speed, γ is the adjustment coefficient, and D_safe is the preset safe distance threshold; The travel path of each charging robot is replanned according to the adjusted speed V_adj.

5. The method for charging robot layout based on environment perception according to claim 1, characterized in that: The method of using the image acquisition module to identify and select a location suitable for staying for providing service includes: The charging robot analyzes environmental features through the image acquisition module, defines the suitability scoring function H(w,x) for each potential stop point, where w and x represent the coordinates of different locations, and calculates the comprehensive score S_total = Σ[H(w,x)]*W_i for each location, where W_i is the weight factor corresponding to each feature; According to the comprehensive score S_total, the most suitable location P_opt is selected as the service stop point, ensuring that P_opt satisfies S_total>S_threshold, where S_threshold is the preset minimum suitability threshold; After determining P_opt, the formula D_stay=D_base+δ(S_total-S_avg) is used to adjust the robot's expected stay time D_stay at the location, where D_base is the base stay time, δ is the adjustment coefficient, and S_avg is the average of the comprehensive scores of all candidate locations.

6. The method for charging robot layout based on environment perception according to claim 1, characterized in that: The step of pausing at the selected position and waiting for user operation includes: After pausing at the selected position, define the user proximity function J(m,n) to evaluate the distance between the user and the robot, where m and n represent the coordinates of the user and the robot, respectively, and calculate the user proximity index A_index=1 / (1+J(m,n)) to quantify the user proximity; Adjust the robot's waiting strategy based on A_index. When A_index>A_threshold, activate the interactive interface to prepare for service. A_threshold is the preset proximity threshold. The formula T_wait=T_min+η(A_index-A_threshold) is used to dynamically adjust the robot's minimum waiting time T_wait, where T_min is the basic waiting time and η is the adjustment factor.

7. The method for charging robot layout based on environment perception according to claim 1, characterized in that: The user completes identity authentication by scanning the QR code on the robot and then takes out the required power bank, including: After the user scans the QR code on the robot, the identity authentication function K(z) is defined, where z represents the verification information provided by the user; the verification score V_score = Σ[K(z)]*V_i is calculated, where V_i is the weight factor corresponding to each verification information; When V_score reaches the preset verification threshold V_threshold, the user's identity is confirmed and the power bank is unlocked; According to the user identity authentication result and request, the formula C_release=C_base+θ(V_score-V_threshold) is used to determine the power bank release quantity C_release, where C_base is the basic release quantity and θ is the adjustment coefficient.

8. The method for charging robot layout based on environment perception according to claim 1, characterized in that: The charging robot continuously monitors its own power state, and starts a recharging process when the power is lower than a preset threshold, including: the charging robot continuously monitors its own power state E, and defines a power state function L(t), where t represents time; Calculate the percentage of current power to full power state P = L(t) / L_max*100%, L_max is the power value in full power state; When P is lower than the preset threshold value P_threshold, the recharge process is started, and the formula T_charge = T_base + ρ (P_threshold-P) is used to determine the estimated charging time T_charge, where T_base is the basic charging time and ρ is the adjustment coefficient; Plan the best path back to the nearest charging station based on T_charge.

9. The method for charging robot layout based on environment perception according to claim 1, characterized in that: The charging robot sends the current coordinates and status information to the central control system, requesting the dispatch of a backup robot to take over the work, including: The charging robot sends the current coordinates B and state information S to the central control system, and defines a replacement efficiency function M(B,S) to evaluate the efficiency of dispatching the backup robot; Calculate the replacement efficiency index E_index = M(B, S) / M_max*100%, where M_max is the maximum replacement efficiency under ideal conditions; The most suitable backup robot U_opt is determined based on E_index, and the dispatch time T_dispatch of the backup robot is adjusted using the formula T_dispatch=T_base+σ(E_index-E_threshold), where T_base is the base dispatch time, σ is the adjustment coefficient, and E_threshold is the preset replacement efficiency threshold.

10. The method for charging robot layout based on environment perception according to claim 1, characterized in that: After the charging robot receives the command from the system, the robot that is about to run out of power automatically navigates to the nearest charging station for charging, while the other robot moves to the original position to continue providing services, including: After receiving the instruction from the system, the charging robot defines the shortest path function N(a,b) to calculate the shortest path from the current position a to the nearest charging station b; Determine the optimal navigation route R_opt based on N(a,b), and use the formula D_path=D_min+τ(N(a,b)-N_avg) to adjust the actual travel distance D_path, where D_min is the theoretical shortest distance, N_avg is the average path length, and τ is the adjustment coefficient; The robot that is about to run out of power automatically navigates along R_opt to the nearest charging station for charging. At the same time, another backup robot moves to that location based on the original position coordinates to continue providing service.

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