Automatic driving wheelchair charging path planning method and device and electronic equipment

Abstract

The invention discloses an automatic driving wheelchair charging path planning method and device and electronic equipment, and belongs to the technical field of intelligent hardware, and the method comprises the steps: determining a to-be-charged automatic driving wheelchair, and distributing a plurality of first charging stations for each automatic driving wheelchair according to the distance between the to-be-charged automatic driving wheelchair and the charging stations; planning a charging path for each automatic driving wheelchair to reach each first charging station, and generating a plurality of groups of charging path plans; for each group of charging paths, calculating a second optimization target value corresponding to the group of charging paths according to the first optimization target value of each charging path in the group; determining the charging path group with the minimum second optimization target value as a target charging path group; and according to each charging path included in the target charging path group, each automatic driving wheelchair is controlled to move to the charging station for charging, so that the safety and the stability of a power grid can be improved, and the problem of local traffic jam caused by bunching charging of the automatic driving wheelchairs can be avoided.

Description

Technical Field

[0001] This invention relates to the field of smart hardware technology, and in particular to a method and apparatus for planning charging routes for an autonomous wheelchair, as well as an electronic device. Background Technology

[0002] With the development of technology, robots are now generally equipped with automatic charging capabilities. When a robot needs charging, it navigates to a charging station using its location information to automatically recharge, thereby improving its ability to work autonomously for extended periods without human intervention. Examples of robots with this capability include robotic vacuum cleaners, self-driving wheelchairs, and industrial robots.

[0003] In transportation hubs where multiple autonomous wheelchairs are deployed, the clustering of these wheelchairs for charging during peak hours can cause localized traffic congestion near charging stations and lead to significant fluctuations in grid load. Therefore, improving grid security and avoiding traffic congestion caused by autonomous wheelchair charging are pressing technical challenges that need to be addressed. Summary of the Invention

[0004] The purpose of this invention is to provide an autonomous driving wheelchair charging path planning method, device, and electronic device that can solve at least one of the above-mentioned problems in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] This invention provides a method for planning charging routes for autonomous wheelchairs, wherein the method includes:

[0007] Identify the autonomous wheelchairs to be charged, and assign multiple first charging stations to each of the autonomous wheelchairs according to their distance from the charging stations;

[0008] For each of the autonomous driving wheelchairs, a charging path is planned to reach each of the first charging stations, generating multiple sets of charging path plans, where each charging path is different;

[0009] For each group of charging paths, a second optimization target value corresponding to the group of charging paths is calculated based on the first optimization target value of each charging path in the group.

[0010] The charging path group with the smallest second optimization objective value is determined as the target charging path group;

[0011] Based on each charging path included in the target charging path group, control each of the autonomous driving wheelchairs to be charged to move to the charging station for charging.

[0012] Optionally, for each group of charging paths, the step of calculating the second optimization target value corresponding to the group of charging paths based on the first optimization target value of each charging path in the group includes:

[0013] For each group of charging paths, a first optimization target value is determined for each charging path in that group;

[0014] The sum of the first optimization target values ​​of each charging path in the group is determined as the second optimization target value corresponding to the charging path in the group.

[0015] Optionally, the step of determining the first optimization target value for each charging path in the group includes:

[0016] For a charging route within the group, determine the traffic congestion evaluation value for that charging route;

[0017] Determine the congestion evaluation value of the charging station corresponding to the charging path;

[0018] Determine the grid load evaluation value corresponding to the charging path;

[0019] The weighted sum of the traffic congestion evaluation value, the charging station congestion evaluation value, and the power grid load evaluation value is determined as the first optimization target value of the charging path.

[0020] Optionally, the step of determining the traffic congestion assessment value of the charging route includes:

[0021] Determine the driving distance corresponding to the charging path and the moving speed of the autonomous wheelchair corresponding to the charging path;

[0022] The quotient of the travel distance and the travel speed is determined as the traffic congestion evaluation value of the charging route.

[0023] Optionally, the step of determining the congestion evaluation value of the charging station corresponding to the charging path includes:

[0024] Determine the target charging station corresponding to the charging path;

[0025] Determine the first number of autonomous wheelchairs in this group that are to be scheduled to be charged at the target charging station;

[0026] Based on the first quantity and the second quantity of ideal autonomous driving wheelchairs preset in the system for the target charging station, the congestion evaluation value of the charging station corresponding to the charging path is determined.

[0027] Optionally, the step of determining the grid load evaluation value corresponding to the charging path includes:

[0028] Determine the estimated power load of the target charging station corresponding to the charging path;

[0029] Determine the ideal power load value preset for the target charging station in the system;

[0030] Based on the estimated power load and the ideal power load, the power grid load evaluation value corresponding to the charging path is determined.

[0031] This invention also provides an autonomous driving wheelchair charging route planning device, wherein the device includes:

[0032] The first determining module is used to determine the autonomous driving wheelchair to be charged and assign multiple first charging stations to each of the autonomous driving wheelchairs according to the distance between them and the charging stations.

[0033] The path generation module is used to plan a charging path to each of the first charging stations for each of the autonomous driving wheelchairs, and generate multiple sets of charging path plans, wherein each charging path is different.

[0034] The calculation module is used to calculate the second optimization target value corresponding to the charging path in the group based on the first optimization target value of each charging path in the group.

[0035] The second determining module is used to determine the charging path group with the smallest second optimization target value as the target charging path group;

[0036] The charging control module is used to control each of the autonomous driving wheelchairs to be charged to move to the charging station for charging according to each charging path included in the target charging path group.

[0037] Optionally, the computing module includes:

[0038] The first submodule is used to determine the first optimization target value for each charging path in each group of charging paths.

[0039] The second submodule is used to determine the sum of the first optimization target values ​​of each charging path under the group as the second optimization target value corresponding to the charging path of the group.

[0040] Optionally, the first submodule includes:

[0041] The first unit is used to determine the traffic congestion evaluation value of a charging route for a given charging route within the group.

[0042] The second unit is used to determine the congestion evaluation value of the charging station corresponding to the charging path;

[0043] The third unit is used to determine the grid load evaluation value corresponding to the charging path;

[0044] The fourth unit is used to determine the first optimization target value of the charging path by weighted summing of the traffic congestion evaluation value, the charging station congestion evaluation value, and the power grid load evaluation value.

[0045] Optionally, the first unit is specifically used for:

[0046] Determine the driving distance corresponding to the charging path and the moving speed of the autonomous wheelchair corresponding to the charging path;

[0047] The quotient of the travel distance and the travel speed is determined as the traffic congestion evaluation value of the charging route.

[0048] Optionally, the second unit is specifically used for:

[0049] Determine the target charging station corresponding to the charging path;

[0050] Determine the first number of autonomous wheelchairs in this group that are to be scheduled to be charged at the target charging station;

[0051] Based on the first quantity and the second quantity of ideal autonomous driving wheelchairs preset in the system for the target charging station, the congestion evaluation value of the charging station corresponding to the charging path is determined.

[0052] Optionally, the third unit is specifically used for:

[0053] Determine the estimated power load of the target charging station corresponding to the charging path;

[0054] Determine the ideal power load value preset for the target charging station in the system;

[0055] Based on the estimated power load and the ideal power load, the power grid load evaluation value corresponding to the charging path is determined.

[0056] This invention provides an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of any of the above-described autonomous driving wheelchair charging path planning methods.

[0057] This invention provides a readable storage medium storing a program or instructions, which, when executed by a processor, implement the steps of any of the above-described autonomous driving wheelchair charging path planning methods.

[0058] The autonomous driving wheelchair charging path planning scheme provided in this invention identifies the autonomous driving wheelchairs to be charged and assigns multiple first charging stations to each wheelchair based on their distance from the charging stations. It plans charging paths to each first charging station for each wheelchair, generating multiple sets of charging path plans. For each set of charging paths, it calculates a second optimization target value based on the first optimization target value of each charging path within the set. The charging path set with the smallest second optimization target value is determined as the target charging path set. Based on the charging paths included in the target charging path set, it controls each autonomous driving wheelchair to move to a charging station for charging. This effectively reduces the impact of multiple autonomous driving wheelchairs charging simultaneously on the power grid, ensuring grid safety and stability, and avoiding localized traffic congestion caused by clustered charging of autonomous driving wheelchairs. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of an autonomous driving wheelchair charging path planning method according to an embodiment of this application;

[0060] Figure 2 This is a structural block diagram illustrating an autonomous driving wheelchair charging path planning device according to an embodiment of this application;

[0061] Figure 3 This is a structural block diagram illustrating an embodiment of an electronic device according to this application. Detailed Implementation

[0062] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0063] The charging path planning scheme for autonomous wheelchairs provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0064] The autonomous driving wheelchair charging path planning method in this application includes the following steps:

[0065] Step 101: Identify the autonomous wheelchairs to be charged and assign multiple first charging stations to each autonomous wheelchair according to their distance from the charging stations.

[0066] For x autonomous wheelchairs requiring charging in a transportation hub scenario, an autonomous wheelchair charging path planning system (hereinafter referred to as the system) is activated. The system matches each autonomous wheelchair with y nearest first charging stations, and the number of first charging stations matched with each autonomous wheelchair, y, is a preset value in the system. The specific number of Y can be flexibly set by those skilled in the art, and this embodiment does not impose specific limitations on it.

[0067] The autonomous driving wheelchair charging path planning method of this application embodiment is applied to a multi-autonomous driving wheelchair system, which includes multiple autonomous driving wheelchairs and electronic devices. The electronic devices can be servers, computers, or other devices with analytical functions. The storage medium in the electronic devices stores an autonomous driving wheelchair charging path planning program. The processor of the electronic devices runs the program in the storage medium to execute the autonomous driving wheelchair charging path planning process, planning charging paths for each autonomous driving wheelchair to be charged in the system, and controlling each autonomous driving wheelchair to be charged to charge according to the corresponding charging path.

[0068] Step 102: Plan the charging path to each first charging station for each autonomous wheelchair, and generate multiple sets of charging path plans.

[0069] The autonomous driving wheelchair charging path planning system utilizes pre-stored heuristic path planning algorithms and deep reinforcement learning algorithms to plan z distinct charging paths to y charging stations for each autonomous driving wheelchair. The number of charging paths, z, is a preset value in the autonomous driving wheelchair charging path planning system, and its specific value can be flexibly set by those skilled in the art; this embodiment does not impose any restrictions on its specific value. Each charging path is unique.

[0070] For x autonomous wheelchairs, each wheelchair has z planned charging paths, which, when combined, can generate z charging points. x There are multiple charging path planning schemes for autonomous driving wheelchairs, each corresponding to a set of charging paths. In this embodiment, a target charging path needs to be selected from multiple sets of charging paths as the charging path planning for this autonomous driving wheelchair.

[0071] In practical implementation, the autonomous wheelchair charging route planning system can calculate the optimization target value (i.e., the second optimization target value) for each autonomous wheelchair charging route planning scheme, and then select the autonomous wheelchair charging route planning scheme with the smallest optimization target value as the final optimized scheme. For specific calculation methods, please refer to the relevant explanation in step 103 below.

[0072] Step 103: For each group of charging paths, calculate the second optimization target value corresponding to the group of charging paths based on the first optimization target value of each charging path in that group.

[0073] In one optional embodiment, for each group of charging paths, the second optimization target value corresponding to that group of charging paths can be calculated based on the first optimization target value of each charging path in that group as follows:

[0074] First, for each group of charging paths, determine the first optimization target value for each charging path in that group;

[0075] Secondly, the sum of the first optimization target values ​​of each charging path in the group is determined as the second optimization target value corresponding to the charging path in the group.

[0076] From another perspective, the method for calculating the optimization objective value of the charging path planning scheme for autonomous wheelchairs can be as follows:

[0077] The system identifies the autonomous wheelchairs and their corresponding charging paths involved in the autonomous wheelchair charging path planning scheme. The system calculates the optimization target value (i.e., the first optimization target value) for the charging path of each autonomous wheelchair involved in the scheme. The system then sums the optimization target values ​​of all the charging paths involved in the scheme and denotes this sum as the scheme's overall optimization target value (i.e., the second optimization target value).

[0078] In actual implementation, the second optimization target value corresponding to each group of charging paths can be calculated using the above method.

[0079] In one alternative embodiment, determining the first optimization target value for each charging path in a set may include the following sub-steps:

[0080] Sub-step 1: For a charging route under the group, determine the traffic congestion evaluation value of the charging route;

[0081] An optional method for determining the traffic congestion evaluation value of a charging route is to: determine the driving distance corresponding to the charging route and the moving speed of the autonomous wheelchair corresponding to the charging route; and determine the quotient of the driving distance and the moving speed as the traffic congestion evaluation value of the charging route.

[0082] Specifically, the traffic congestion evaluation value c of the i-th charging route planned for the r-th autonomous driving wheelchair. i，r，v The formula for calculating it is as follows:

[0083]

[0084] in, S is the distance of the i-th charging path planned for the r-th autonomous wheelchair. r Let be the speed of the r-th autonomous wheelchair.

[0085] Sub-step 2: Determine the congestion evaluation value of the charging station corresponding to the charging path;

[0086] An optional method for determining the congestion evaluation value of the charging station corresponding to the charging path may be as follows: determine the target charging station corresponding to the charging path; determine the first number of autonomous driving wheelchairs in the group that are to be planned to the target charging station for charging; and determine the congestion evaluation value of the charging station corresponding to the charging path based on the first number and the second number of autonomous driving wheelchairs that are preset in the system for the target charging station to accommodate an ideal number of autonomous driving wheelchairs.

[0087] For example, the quotient of the first quantity and the second quantity can be determined as the charging station congestion evaluation value; the quotient of the two can be multiplied by a certain coefficient to determine the charging station congestion evaluation value; or the difference between the two can be multiplied by a certain coefficient to determine the charging station congestion evaluation value.

[0088] An exemplary evaluation value c of the charging station congestion along the i-th charging route planned for the r-th autonomous driving wheelchair. i，r，n The calculation formula is as follows:

[0089]

[0090] In this context, the endpoint of the i-th charging path planned for the r-th autonomous driving wheelchair is set to the target charging station e. i，r e i，r This is the charging station number. Num(e i，r For all autonomous wheelchairs involved in the autonomous wheelchair charging path planning scheme for optimizing the target value of the calculation scheme, the destination of the planned charging path is e. i，r This refers to the number of autonomous wheelchairs at the charging station (i.e., the first number). Q(e) i，r (e) for charging stations i，r The ideal number of autonomous wheelchairs that can be accommodated at each charging station (i.e., the second number) is a preset value in the system.

[0091] Sub-step 3: Determine the grid load evaluation value corresponding to the charging path;

[0092] An optional method for determining the grid load evaluation value corresponding to a charging path is as follows: determine the estimated power load of the target charging station corresponding to the charging path; determine the ideal power load value preset for the target charging station in the system; and determine the grid load evaluation value corresponding to the charging path based on the estimated power load value and the ideal power load value.

[0093] For example, the quotient of the estimated power load of the target charging station and the ideal power load can be determined as the power grid load evaluation value, or the quotient can be multiplied by a certain coefficient to determine the power grid load evaluation value.

[0094] An exemplary grid load evaluation value c for the i-th charging path planned for the r-th autonomous driving wheelchairi，r，p The calculation formula is as follows:

[0095]

[0096] Among them, Load(e i，r (e) for charging stations i，r The estimated power load, wherein the estimated power load is determined by the autonomous wheelchair charging path planning system based on the planned charging path, with the destination being the target charging station e. i，r The remaining battery power, expected charge level after charging, and battery power information of the autonomous wheelchair are used to calculate the charging station's e i，r The estimated electricity load.

[0097] L(e i，r ) is the target charging station e i，r The ideal power load value is the preset value in the charging path planning system for autonomous driving wheelchairs.

[0098] Sub-step 4: The weighted sum of the traffic congestion evaluation value, the charging station congestion evaluation value, and the power grid load evaluation value is determined as the first optimization target value for the charging route.

[0099] The formula for calculating the first optimization objective value of the i-th charging path in the r-th autonomous driving wheelchair planning can be as follows:

[0100] C i，r =a v ·c i，r，v +a n ·c i，r，n +a p ·c i，r，p

[0101] Among them, C i，r The optimization objective value (i.e., the first optimization objective value) for the i-th charging path planned for the r-th autonomous wheelchair is c. i，r，v The traffic congestion evaluation value of the i-th charging route planned for the r-th autonomous wheelchair, c. i，r，n The congestion evaluation value of the charging station for the i-th charging route planned for the r-th autonomous driving wheelchair, c. i，r，p The grid load assessment value for the i-th charging path planned for the r-th autonomous driving wheelchair. v a n and a p Preset weighting coefficients for the autonomous wheelchair path planning system.

[0102] The first optimization target value corresponding to a single planned path can be calculated by using sub-steps 1-4 above. In actual implementation, the above process can be repeated to calculate the first optimization target value corresponding to each planned path in a group.

[0103] Step 104: Determine the charging path group with the smallest second optimization target value as the target charging path group.

[0104] Step 103 can be used to calculate the second optimization target value corresponding to each group of charging paths. By comparing the second optimization target values ​​of each group, the minimum second optimization target value can be determined. The group of charging paths corresponding to the minimum second optimization target value is determined as the target charging path.

[0105] Step 105: Based on the charging paths included in the target charging path group, control each autonomous wheelchair to be charged to move to the charging station for charging.

[0106] After determining each target charging path group through steps 101-104, the system controls each autonomous wheelchair to be charged to the target charging station for charging according to each charging path contained in the target charging path group.

[0107] Because the determination of the target charging route group takes into account the congestion of charging stations and traffic congestion along the charging route, it can effectively avoid the problem of local traffic congestion caused by autonomous wheelchairs crowding together for charging. Furthermore, because the determination of the target charging route group takes into account the grid load of each target charging station, it can improve grid security and stability.

[0108] The autonomous driving wheelchair charging path planning method provided in this application identifies autonomous driving wheelchairs to be charged and assigns multiple first charging stations to each wheelchair based on their distance from charging stations. It plans charging paths to each first charging station for each wheelchair, generating multiple sets of charging path plans. For each set of charging paths, it calculates a second optimization target value based on the first optimization target value of each charging path within the set. The charging path set with the smallest second optimization target value is determined as the target charging path set. Based on the charging paths included in the target charging path set, it charges each autonomous driving wheelchair to be charged. This method effectively reduces the impact of multiple autonomous driving wheelchairs charging simultaneously on the power grid, ensuring power grid safety and stability, and avoiding localized traffic congestion caused by clustered charging of autonomous driving wheelchairs.

[0109] Figure 2 The structural block diagram of an autonomous driving wheelchair charging path planning device according to an embodiment of this application is shown.

[0110] The autonomous driving wheelchair charging path planning device provided in this application includes the following functional modules:

[0111] The first determining module 201 is used to determine the autonomous driving wheelchair to be charged and assign multiple first charging stations to each of the autonomous driving wheelchairs according to the distance between them and the charging stations.

[0112] The path generation module 202 is used to plan a charging path to each of the first charging stations for each of the autonomous driving wheelchairs, and generate multiple sets of charging path plans, wherein each charging path is different.

[0113] The calculation module 203 is used to calculate the second optimization target value corresponding to the charging path of the group based on the first optimization target value of each charging path in the group for each group of charging paths.

[0114] The second determining module 204 is used to determine the charging path group with the smallest second optimization target value as the target charging path group;

[0115] The charging control module 205 is used to control each of the autonomous driving wheelchairs to be charged to move to the charging station for charging according to each charging path included in the target charging path group.

[0116] Optionally, the computing module includes:

[0117] The first submodule is used to determine the first optimization target value for each charging path in each group of charging paths.

[0118] The second submodule is used to determine the sum of the first optimization target values ​​of each charging path under the group as the second optimization target value corresponding to the charging path of the group.

[0119] Optionally, the first submodule includes:

[0120] The first unit is used to determine the traffic congestion evaluation value of a charging route for a given charging route within the group.

[0121] The second unit is used to determine the congestion evaluation value of the charging station corresponding to the charging path;

[0122] The third unit is used to determine the grid load evaluation value corresponding to the charging path;

[0123] The fourth unit is used to determine the first optimization target value of the charging path by weighted summation of the traffic congestion evaluation value, the charging station congestion evaluation value, and the power grid load evaluation value.

[0124] Optionally, the first unit is specifically used for:

[0125] Determine the driving distance corresponding to the charging path and the moving speed of the autonomous wheelchair corresponding to the charging path;

[0126] The quotient of the travel distance and the travel speed is determined as the traffic congestion evaluation value of the charging route.

[0127] Optionally, the second unit is specifically used for:

[0128] Determine the target charging station corresponding to the charging path;

[0129] Determine the first number of autonomous wheelchairs in this group that are to be scheduled to be charged at the target charging station;

[0130] Based on the first quantity and the second quantity of ideal autonomous driving wheelchairs preset in the system for the target charging station, the congestion evaluation value of the charging station corresponding to the charging path is determined.

[0131] Optionally, the third unit is specifically used for:

[0132] Determine the estimated power load of the target charging station corresponding to the charging path;

[0133] Determine the ideal power load value preset for the target charging station in the system;

[0134] Based on the estimated power load and the ideal power load, the power grid load evaluation value corresponding to the charging path is determined.

[0135] The autonomous driving wheelchair charging path planning device provided in this application identifies autonomous driving wheelchairs to be charged and assigns multiple first charging stations to each wheelchair according to their distance from charging stations. It plans charging paths to each first charging station for each wheelchair, generating multiple sets of charging path plans. For each set of charging paths, it calculates a second optimization target value based on the first optimization target value of each charging path within the set. The charging path set with the smallest second optimization target value is determined as the target charging path set. Based on the charging paths included in the target charging path set, it controls each autonomous driving wheelchair to move to a charging station for charging. This effectively reduces the impact of multiple autonomous driving wheelchairs charging simultaneously on the power grid, ensuring grid safety and stability, and avoiding localized traffic congestion caused by clustered charging of autonomous driving wheelchairs.

[0136] In the embodiments of this application Figure 2 The autonomous wheelchair charging path planning device shown can be a physical device, or it can be a component, integrated circuit, or chip in a server. (This is from an embodiment of the present application.) Figure 2 The autonomous driving wheelchair charging route planning device shown can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application does not specifically limit the specific operating system used.

[0137] The embodiments provided in this application Figure 2 The autonomous wheelchair charging path planning device shown can achieve Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0138] Optionally, such as Figure 3 As shown, this application embodiment also provides an electronic device 300, including a processor 301, a memory 302, and a program or instructions stored in the memory 302 and executable on the processor 301. When the program or instructions are executed by the processor 301, they implement the various processes of the above-described autonomous driving wheelchair charging path planning method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0139] It should be noted that the electronic device in this application embodiment includes the server described above.

[0140] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described autonomous driving wheelchair charging path planning method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0141] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0142] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement various processes of the above-mentioned autonomous driving wheelchair charging path planning method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0143] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0145] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for planning a charging path for an autonomous driving wheelchair, characterized in that: The method comprises: Determine an autonomous driving wheelchair to be charged, and assign a plurality of first charging stations to each autonomous driving wheelchair according to the distance from the charging station; Planning a charging path for each of the autonomous driving wheelchairs to reach each of the first charging stations, generating multiple groups of charging path plans, wherein each charging path is different; For each group of charging paths, calculating a second optimization target value corresponding to the group of charging paths according to the first optimization target value of each charging path in the group; Determine the charging path group with the smallest second optimization target value as the target charging path group; According to each charging path included in the target charging path group, each of the autonomous driving wheelchairs to be charged is controlled to move to a charging station for charging.

2. The method according to claim 1, characterized in that For each group of charging paths, the step of calculating the second optimization target value corresponding to the group of charging paths according to the first optimization target value of each charging path in the group includes: For each group of charging paths, determining a first optimization target value of each charging path in the group; The sum of the first optimization target values ​​of the charging paths in the group is determined as the second optimization target value corresponding to the charging paths in the group.

3. The method according to claim 2, characterized in that The step of determining the first optimization target value of each charging path in the group comprises: For a charging path in the group, determining a traffic congestion evaluation value of the charging path; Determining a charging station congestion evaluation value corresponding to the charging path; Determining a grid load evaluation value corresponding to the charging path; A weighted sum of the traffic congestion evaluation value, the charging station congestion evaluation value, and the power grid load evaluation value is determined as a first optimization target value of the charging path.

4. The method according to claim 3, characterized in that The step of determining the traffic congestion evaluation value of the charging path includes: Determine a driving distance corresponding to the charging path and a moving speed of the autonomous driving wheelchair corresponding to the charging path; A quotient of the travel distance and the moving speed is determined as a traffic congestion evaluation value of the charging path.

5. The method according to claim 3, characterized in that: The step of determining the charging station congestion evaluation value corresponding to the charging path includes: Determining a target charging station corresponding to the charging path; Determining a first number of autonomous driving wheelchairs in the group to be planned to be charged at the target charging station; The charging station congestion evaluation value corresponding to the charging path is determined based on the first number and a second number of ideal autonomous driving wheelchairs preset for the target charging station in the system.

6. The method according to claim 3, characterized in that The step of determining the grid load evaluation value corresponding to the charging path comprises: Determining an estimated power load value of a target charging station corresponding to the charging path; Determining an ideal power load value preset for the target charging station in the system; The grid load evaluation value corresponding to the charging path is determined according to the power load estimation value and the power load ideal value.

7. A charging path planning device for an autonomous driving wheelchair, characterized in that: The device comprises: A first determination module is used to determine the autonomous driving wheelchair to be charged, and allocate a plurality of first charging stations to each autonomous driving wheelchair according to the distance between the wheelchair and the charging station; a path generation module, configured to plan a charging path for each of the autonomous driving wheelchairs to reach each of the first charging stations, and generate multiple groups of charging path plans, wherein each charging path is different; A calculation module, configured to calculate, for each group of charging paths, a second optimization target value corresponding to the group of charging paths according to the first optimization target value of each charging path in the group; A second determining module, configured to determine the charging path group with the smallest second optimization target value as the target charging path group; The charging control module is used to control each of the autonomous driving wheelchairs to be charged to move to a charging station for charging according to each charging path included in the target charging path group.

8. The device according to claim 7, characterized in that The calculation module comprises: A first submodule is used to determine, for each group of charging paths, a first optimization target value of each charging path in the group; The second submodule is used to determine the sum of the first optimization target values ​​of the charging paths in the group as the second optimization target value corresponding to the charging paths in the group.

9. The device according to claim 8, characterized in that The first submodule includes: A first unit is used to determine a traffic congestion evaluation value of a charging path under the group; A second unit is used to determine a charging station congestion evaluation value corresponding to the charging path; A third unit is used to determine a grid load evaluation value corresponding to the charging path; The fourth unit is used to determine the weighted sum of the traffic congestion evaluation value, the charging station congestion evaluation value and the power grid load evaluation value as the first optimization target value of the charging path.

10. An electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method for planning a charging path for an autonomous wheelchair as described in any one of claims 1 to 6.

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