CN115824220A - A robot traffic path planning method and device, electronic equipment - Google Patents

Abstract

The invention discloses a robot passage path planning scheme, which belongs to the technical field of intelligent hardware. The method includes: determining channel construction parameters corresponding to each road section in the traffic network and inputting it into a preset robot passage network planning model; determining The travel demand of the robot and the traffic cost parameters of the robot are input into the pre-built robot traffic equilibrium model; the travel needs of other traffic participants and the traffic cost parameters of other traffic participants determined are input into the pre-built traffic flow model of other traffic participants Equilibrium model; jointly solve the robot traffic network planning model, robot traffic equilibrium model and other traffic participant traffic equilibrium models, and obtain the robot traffic path planning results in the traffic network. The planning results not only meet the total traffic demand of the entire traffic network, but also It can save the cost of robot-specific road construction.

Description

A robot traffic path planning method and device, electronic equipment

technical field

The invention relates to the technical field of intelligent hardware, in particular to a method and device for planning a robot's passage path, and electronic equipment.

Background technique

In a complex traffic environment where robots and other traffic participants exist at the same time, in order to ensure the safe operation of robots, special passages for robots can be built in road sections, or road sections can be directly planned as road sections that only allow robots to pass.

Considering the high cost of building robot-only passages, in an existing traffic network, whether each road section needs to build robot-specific passages and whether it needs to be planned as a road section that only allows robots to pass is a key issue. How to make a reasonable plan for this problem, so that the planning can not only meet the traffic demand of the entire traffic network but also save the construction cost of the dedicated robot channel, is a technical problem to be solved urgently by those skilled in the art.

Contents of the invention

The purpose of the embodiment of the present invention is to provide a robot path planning method and device, and electronic equipment, which can solve the problem that the robot path cannot be reasonably planned at present.

In order to solve the above technical problems, the present invention provides the following technical solutions:

An embodiment of the present invention provides a method for planning a robot passage path, wherein the method includes: determining channel construction parameters corresponding to each road section in the traffic network, wherein the channel construction parameters include: the construction cost of the robot-specific channel, the total Passage cost, pass category; said pass category is used to indicate whether to build a new robot-specific passage for the road section;

Input the channel construction parameters corresponding to each road section into the preset robot traffic network planning model;

Determine the travel demand of the robot between each position pair in the travel position pair set, the travel demand of other traffic participants, and the traffic cost parameters corresponding to each road section, wherein the traffic cost parameters include: robot traffic cost parameters, other traffic Participant travel cost parameters;

Inputting the travel demand of the robot and the traffic cost parameters of the robot into a pre-built robot traffic equilibrium model;

Inputting the travel demand of the other traffic participants and the traffic cost parameters of the other traffic participants into the pre-built traffic equilibrium model of other traffic participants;

The robot traffic network planning model, the robot traffic equilibrium model, and the other traffic participant traffic equilibrium models are jointly solved to obtain a robot traffic path planning result in the traffic network.

Optionally, the robot passing cost parameters include: the number of passing robots per unit time and the average passing time of robots per unit time;

The passing cost parameters of other traffic participants include: the number of passing other traffic participants per unit time and the average passing time of other traffic participants per unit time.

Optionally, the robot traffic network planning model includes:

The first sub-model that represents the minimum sum of the construction cost and the total traffic cost of the robot-specific passage in each road section;

a second sub-model representing a connected robot travel path between travel position pairs;

The third sub-model representing the travel path between travel position pairs.

Optionally, the step of determining the travel demand of the robot and the travel demand of other traffic participants between each position pair in the travel position pair set includes:

For each position pair in the set of travel position pairs, based on the minimum passing time of the robot between the position pairs, the minimum passing time of other traffic participants and preset parameters, determine the probability that the user chooses to dispatch the robot;

Based on the probability and the total travel demand corresponding to the position pair, determine the travel demand of the robot between the position pair;

The difference between the total travel demand corresponding to the position pair and the travel demand of the robot between the position pairs is determined as the travel demand of other traffic participants between the position pairs.

Optionally, the robot traffic balance model includes:

Characterize the fourth sub-model with the minimum sum of the robot average transit time in all road sections;

A fifth sub-model that characterizes the traffic volume of the robot in the passing path set between the location pairs, and is equal to the travel demand of the robot between the location pairs.

Optionally, the traffic equilibrium models of other traffic participants include:

The sixth sub-model representing the minimum sum of the average passing time of other traffic participants in all road sections;

A seventh sub-model representing the traffic volume of other traffic participants in the passing path set between the location pairs, which is equal to the travel demand of other traffic participants between the location pairs.

An embodiment of the present invention provides a robot passage path planning device, wherein the device includes: a first determination module, configured to determine channel construction parameters corresponding to each road section in the traffic network, wherein the channel construction parameters include: a robot The construction cost, the total traffic cost, and the traffic category of the dedicated channel; the traffic category is used to indicate whether a road section is to build a new robot-specific channel; the second determination module is used to input the channel construction parameters corresponding to the road sections into the preset In the robot traffic network planning model; the third determination module is used to determine the travel demand of the robot between each position pair in the travel position pair set, the travel demand of other traffic participants, and the corresponding traffic cost parameters of each road section, wherein, The passage cost parameters include: robot passage cost parameters, and other traffic participant passage cost parameters; a first input module for inputting the travel demand of the robot and the robot passage cost parameters into a pre-built robot passage equilibrium model The second input module is used to input the travel demand of the other traffic participants and the traffic cost parameters of the other traffic participants into the pre-built traffic equilibrium model of other traffic participants; the solution module is used to calculate the robot The traffic network planning model, the robot traffic equilibrium model and the other traffic participant traffic equilibrium models are jointly solved to obtain the robot traffic path planning results in the traffic network.

Optionally, the robot passing cost parameters include: the number of passing robots per unit time and the average passing time of robots per unit time;

The passing cost parameters of other traffic participants include: the number of passing other traffic participants per unit time and the average passing time of other traffic participants per unit time.

Optionally, the robot traffic network planning model includes:

The first sub-model that represents the minimum sum of the construction cost and the total traffic cost of the robot-specific passage in each road section;

a second sub-model representing a connected robot travel path between travel position pairs;

The third sub-model representing the travel path between travel position pairs.

Optionally, the third determination module includes:

The first sub-module is used to determine the probability that the user chooses to dispatch a robot based on the minimum passing time of the robot between the pair of locations, the minimum passing time of other traffic participants, and preset parameters for each position pair in the travel position pair set ;

The second submodule is used to determine the travel demand of the robot between the position pair based on the probability and the total travel demand corresponding to the position pair;

The third sub-module is configured to determine the difference between the total travel demand corresponding to the position pair and the travel demand of the robot between the position pair as the travel demand of other traffic participants between the position pair.

Optionally, the robot traffic balance model includes:

Characterize the fourth sub-model with the minimum sum of the robot average transit time in all road sections;

A fifth sub-model that characterizes the traffic volume of the robot in the passing path set between the location pairs, and is equal to the travel demand of the robot between the location pairs.

Optionally, the traffic equilibrium models of other traffic participants include:

The sixth sub-model representing the minimum sum of the average passing time of other traffic participants in all road sections;

A seventh sub-model representing the traffic volume of other traffic participants in the passing path set between the location pairs, which is equal to the travel demand of other traffic participants between the location pairs.

An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, and the program or instruction is executed by the processor During execution, the steps of any one of the above-mentioned robot path planning methods are realized.

An embodiment of the present invention provides a readable storage medium, on which a program or an instruction is stored, and when the program or instruction is executed by a processor, the steps of any one of the above robot path planning methods are implemented.

The robot traffic path planning scheme provided by the embodiment of the present invention determines the channel construction parameters corresponding to each road section in the traffic network; inputs the channel construction parameters corresponding to each road section into the preset robot traffic network planning model; determines the travel position pair set The travel demand of the robot between each location pair, the travel demand of other traffic participants, and the corresponding traffic cost parameters of each road section; the travel demand of the robot and the traffic cost parameters of the robot are input into the pre-built robot traffic equilibrium model; the other traffic The travel demand of the participants and the traffic cost parameters of other traffic participants are input into the pre-built traffic equilibrium model of other traffic participants; the robot traffic network planning model, the robot traffic equilibrium model and the traffic equilibrium model of other traffic participants are jointly solved to obtain Path planning results for robots in traffic networks. The robot passage path planning method provided in the embodiment of the present application pre-constructs the robot passage network planning model for constraining the lowest construction cost of the robot-specific passage, and pre-constructs the robot passage equilibrium model and other traffic participation for constraining the robot's total passage cost to be the lowest. Through the joint solution of these three pre-built network planning models, the result of robot traffic path planning can not only meet the total traffic demand of the entire traffic network, ensure the connectivity of the traffic network, but also save the construction of dedicated roads for robots. cost.

Description of drawings

Fig. 1 is a flow chart showing the steps of a method for planning a robot travel path according to an embodiment of the present application;

Fig. 2 shows the schematic diagram of the traffic network planned in the existing way;

Fig. 3 is a schematic diagram showing the transportation network planned by the robot passage path planning method in the embodiment of the present application;

Fig. 4 is a structural block diagram showing a robot travel path planning device according to an embodiment of the present application;

FIG. 5 is a structural block diagram showing an electronic device according to an embodiment of the present application.

Detailed ways

In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

The following is a detailed description of the robot travel path planning solution provided by the embodiment of the present application through specific embodiments and application scenarios with reference to the accompanying drawings.

As shown in accompanying drawing 1, the robot passage path planning method of the embodiment of the present application comprises the following steps:

Step 101: Determine the channel construction parameters corresponding to each road section in the transportation network.

Among them, the channel construction parameters include: the construction cost of the robot-specific channel, the total traffic cost, and the traffic category. The traffic category is used to indicate whether a new robot-specific passage is built for the road segment. In addition, the channel construction parameters can also include the road segment type, which is used to indicate whether the road segment belongs to the robot traffic path between the travel position pair.

The robot path planning method in the embodiment of the present application is applied to an electronic device, and the electronic device may be a server, a computer, and other devices with analysis functions. The storage medium in the electronic device stores the robot passage path planning and recognition program, and the processor of the electronic device runs the program in the storage medium to execute the robot passage path planning process.

Step 102: Input the channel construction parameters corresponding to each road section into the preset robot traffic network planning model.

The robot traffic network planning model includes: the first sub-model representing the minimum sum of the construction cost and the total traffic cost of the robot-specific passages in each road section; the second sub-model representing the connected robot traffic path between the travel position pairs; representing the travel position Between the pair is the third submodel of the robot's travel path.

In the embodiment of the present application, a robot traffic network planning model is pre-built, and a transformation plan for the existing traffic network can be obtained by subsequently solving the model. The transformation plan specifically includes the transformation results of each road section. The transformation results include three types: first, build a special passage for robots in the road section, and second, directly plan the road section as a section that only allows robots to pass and prohibits other traffic participants from passing. Third, do not make transformations. The goal of the transformation is to plan a robot traffic network and ensure network connectivity under the premise of minimizing the total system cost. Among them, the total cost of the system = the construction cost of the robot-specific channel + the total traffic cost.

Step 103: Determine the travel demand of the robot, the travel demand of other traffic participants, and the corresponding travel cost parameters of each road segment between each position pair in the travel position pair set.

Among them, the passage cost parameters include: the passage cost parameters of the robot, and the passage cost parameters of other traffic participants.

An optional way to determine the travel demand of the robot and the travel demand of other traffic participants between each position pair in the set of travel position pairs can be as follows:

First, for each location pair in the travel location pair set, determine the probability that the user chooses to dispatch the robot based on the minimum passing time of the robot between the location pairs, the minimum passing time of other traffic participants, and preset parameters;

Secondly, based on the probability and the total travel demand corresponding to the position pair, the travel demand of the robot between the position pairs is determined;

Thirdly, the difference between the total travel demand corresponding to the location pair and the travel demand of the robot between the location pairs is determined as the travel demand of other traffic participants between the location pairs.

In the actual implementation process, the following travel demand division model can be used to determine the travel demand of the robot between the entry location point and the exit location point of each location pair, and determine other traffic between the entry location point and the exit location point of each location pair The travel needs of the participants.

Step 104: Input the robot's travel demand and the robot's traffic cost parameters into the pre-built robot traffic equilibrium model.

The robot passing cost parameters include: the number of passing robots per unit time and the average passing time of robots per unit time.

The robot traffic network planning model is to model the robot traffic network planning problem from the perspective of cost saving. It is understandable that due to the planning of the robot traffic network, the existing traffic network has changed, so the user travel needs to consider the following issues : The problem of travel mode selection, whether to choose a robot to travel or choose other traffic participants to travel, and the problem of travel route selection after the travel mode is determined. In order to solve the above problems, in the embodiment of the present application, the total travel demand between the travel position pair w is set to be unchanged, that is, the travel demand of the robot and the travel demand of other traffic participants between the travel position pair w remain unchanged. On this basis, the travel demand division model is constructed in advance. According to the travel demand partition model, the travel demand of the robot and the travel demand of other traffic participants between each travel position pair w can be determined. It is also necessary to pre-build the traffic equilibrium model of the robot and other traffic participants. By substituting the travel demands of the robot and other traffic participants into the traffic equilibrium model of the robot and the traffic equilibrium model of other traffic participants, the robot The joint solution of the traffic network planning model can finally determine the robot traffic path planning scheme that not only meets the total traffic demand of the entire traffic network, ensures the connectivity of the traffic network, but also saves the cost of robot-specific road construction.

The robot traffic balance model includes: the fourth sub-model that represents the minimum sum of the average passing time of robots in all road sections; represents the robot traffic volume in the path set between the position pairs, and is equal to the travel demand of the robot between the position pairs Fifth submodel.

Step 105: Input the travel demands of other traffic participants and the traffic cost parameters of other traffic participants into the pre-built traffic equilibrium model of other traffic participants.

Wherein, the parameters of the passing cost of other traffic participants include: the number of passing other traffic participants per unit time and the average passing time of other traffic participants per unit time.

The balance model of other traffic participants includes but not limited to: the sixth sub-model that represents the minimum sum of the average transit time of other traffic participants in all road sections; represents the traffic volume of other traffic participants in the traffic path set between pairs of locations, and The seventh sub-model with equal travel demand of other traffic participants between pairs of locations.

Step 106: jointly solve the robot traffic network planning model, the robot traffic equilibrium model, and other traffic participant traffic equilibrium models, and obtain the robot traffic path planning results in the traffic network.

The purpose of this scheme is to plan a robot traffic network that satisfies the connectivity constraints on the premise of minimizing the total system cost.

The method for planning the robot's travel path shown in the embodiment of the present application will be described below with reference to FIGS. 2-3 and Table 1 below.

Fig. 2 is a schematic diagram of a traffic network planned by an existing method. Assuming that the total traffic demand between the travel location pairs is a known quantity, the specific values are shown in Table 1, and the traffic network changes will not change its value. On this basis, the embodiment of the application uses this method in combination with Table 1. The robot passage path planning method provided in the embodiment of the application optimizes the robot passage path, and the specific optimization process is as follows:

Table 1

serial number starting point end Total demand serial number starting point end Total demand 1 1 10 1300 11 12 10 2000 2 1 19 3000 12 13 4 1600 3 2 twenty two 1000 13 13 twenty two 1300 4 5 16 1800 14 14 11 1600 5 6 19 2000 15 16 9 1400 6 8 1 1800 16 16 10 4400 7 10 15 4000 17 16 11 1400 8 10 19 1800 18 17 3 1000 9 11 twenty three 1300 19 twenty three 13 1800 10 11 15 1400 20 twenty three twenty four 1700

S1: Construct a robot traffic network planning model.

The purpose of this step is to build a robot traffic network planning model. Afterwards, by solving the model, a transformation plan for the existing traffic network can be obtained. The transformation plan specifically includes the transformation results of each road section. The transformation results include three types: first, build a special passage for robots in the road section, and second, directly plan the road section as a section that only allows robots to pass and prohibits other traffic participants from passing. Third, do not make transformations.

The goal of the transformation is to plan a robot traffic network and ensure network connectivity under the premise of minimizing the total system cost. Among them, the total cost of the system = the construction cost of the robot-specific channel + the total traffic cost.

The robot traffic network planning model is as follows:

The meaning of each parameter in the robot traffic network planning model is as follows:

A represents the collection of all road segments, and a represents any road segment;

N represents the set of all location points;

I represents the set of all travel position pairs, the travel position pair includes the entry point and the exit point, I∈N;

表示机器人专用通道的建设成本。x _a indicates whether a new robot-only channel is built in section a; c _a indicates the cost of building a new robot-only channel in section a;

Indicates the construction cost of the dedicated channel for robots.

表示单位时间内路段a上的通过机器人的数量；

表示单位时间内路段a上有

台机器人通过时机器人平均通行时间；

表示单位时间内路段a上的通过的其他交通参与者的数量；

表示单位时间内路段a上有

个其他交通参与者通过时其他交通参与者平均通行时间；

表示总通行成本。公式(1)即第一子模型。σ represents the travel time conversion coefficient;

Indicates the number of passing robots on road section a per unit time;

Indicates that there is a road segment a in unit time

The average passing time of the robot when a robot passes;

Indicates the number of other traffic participants passing on road section a per unit time;

Indicates that there is a road segment a in unit time

The average passing time of other traffic participants when other traffic participants pass;

Indicates the total travel cost. Formula (1) is the first sub-model.

y _a indicates whether road section a prohibits other traffic participants from passing; formula (2) indicates that if the robot can pass through a road section, either the road section has a dedicated robot passageway, or the road section adopts a scheme that prohibits other traffic participants from passing. Only one of the planning options can be selected.

表示路段a是否属于出行位置点r到e之间的机器人通行路径上；i为路段的入口位置点，j路段的出口位置点；a＝(i,j)∈A。

Indicates whether road segment a belongs to the path of the robot between travel position point r and e; i is the entry point of road segment, and the exit point of road segment j; a=(i,j)∈A.

Equation (3) and Equation (4) indicate that if two points r to e are selected from the travel position pair, there is a connected robot path between r and e. Formula (3) and formula (4) are the second sub-model.

Formula (4) indicates that any two points r to e are selected from the travel position pair, and any road section in the robot-specific path connecting r to e is a robot-specific road section. Formula (4) is the third sub-model.

均为0-1变量。Formula (5) shows that x _a , y _a ,

Both are 0-1 variables.

S2: Build a travel demand partition model.

The previous step is to model the robot traffic network planning problem from the perspective of cost saving. It is understandable that due to the planning of the robot traffic network, the existing traffic network has changed, so the following issues need to be considered for the travel of users: travel The mode selection problem is whether to choose the robot to travel or choose other traffic participants to travel, and the travel route selection problem after the travel mode is determined. The purpose of this step is to model the problem described above.

Before and after planning the robot traffic network, this scheme considers that the total travel demand between the travel position pair w remains unchanged, that is, the travel demand of the robot and the travel demand of other traffic participants between the travel position pair w remain unchanged. On this basis, this step builds a travel demand division model. According to the travel demand partition model, the travel demand of the robot and the travel demand of other traffic participants between each travel position pair w can be determined.

The travel demand division model is as follows:

The meaning of each parameter is as follows:

W represents a set of travel position pairs;

w represents the travel location pair;

表示出行位置对w之间机器人的出行需求；

Indicates the travel demand of the robot between the travel position and w;

表示出行位置对w之间其他交通参与者的出行需求；

Indicates the travel demand of other traffic participants between the travel location and w;

d ^w represents the total travel demand between the travel location pair w;

表示出行位置对w之间用户选择派出机器人去的概率；

Indicates the probability that the user chooses to send the robot between the travel location pair w;

θSystem preset parameters;

表示出行位置对w之间的机器人最小通行时间；

Indicates the minimum travel time of the robot between the travel position pair w;

表示出行位置对w之间的其他交通参与者最小通行时间。

Indicates the minimum travel time of other traffic participants between the travel location pair w.

In the actual implementation process, the following travel demand division model can be used to determine the travel demand of the robot between the entry location point and the exit location point of each location pair, and determine other traffic between the entry location point and the exit location point of each location pair The travel needs of the participants.

之后，构建机器人通行均衡模型如下：The travel demand of the robot between the obtained travel position pair w

After that, the robot traffic balance model is constructed as follows:

Wherein, formula (9) is the fourth sub-model, and formula (10) is the fifth sub-model.

表示单位时间内路段a上的通过机器人的数量；

表示单位时间内路段a上有

台机器人通过时机器人的平均通行时间；

Indicates the number of passing robots on road section a per unit time;

Indicates that there is a road segment a in unit time

The average passing time of the robot when the robot passes;

表示出行位置对w之间机器人通行路径p上的机器人交通量；

表示出行位置对w之间机器人通行路径集合；p表示一个通行路径；

表示出行位置对w之间机器人的出行需求；

Indicates the traffic volume of the robot on the path p between the travel position pair w;

Indicates the set of travel paths of the robot between the travel position pair w; p represents a passage path;

Indicates the travel demand of the robot between the travel position and w;

表示路段a是否属于出行位置对w之间的机器人通行路径p上；W表示出行位置对集合；

Indicates whether the road section a belongs to the path p of the robot between the travel position pair w; W represents the set of travel position pairs;

表示机器人通行时长增长系数；

表示各个机器人通行路段上机器人平均通行时间；α,β表示预设系统第二系数、第三系数；Q^b表示机器人单位时间内通过的最大数量；M表示一个无穷大的数。

Indicates the growth coefficient of the robot's transit time;

Indicates the average passing time of robots on each robot passage section; α, β represent the second and third coefficients of the preset system; ^Qb represents the maximum number of robots passing per unit time; M represents an infinite number.

之后，构建其他交通参与者通行均衡模型如下：The travel demand of other traffic participants between the travel location pair w is obtained

Afterwards, the traffic equilibrium model of other traffic participants is constructed as follows:

Wherein, formula (14) is the sixth sub-model, and formula (15) is the seventh sub-model.

The meaning of each parameter in the traffic equilibrium model of other traffic participants is as follows:

表示单位时间内路段a上的通过的其他交通参与者的数量；

表示单位时间内路段a上有

个其他交通参与者通过时其他交通参与者的平均通行时间；

Indicates the number of other traffic participants passing on road section a per unit time;

Indicates that there is a road segment a in unit time

The average passing time of other traffic participants when other traffic participants pass;

表示出行位置对w之间其他交通参与者通行路径p上的其他交通参与者的交通量；

表示出行位置对w之间其他交通参与者的通行路径集合；p表示一个通行路径；

表示其他交通参与者通行出行位置对w之间的总需求数量；

Indicates the traffic volume of other traffic participants on the path p of other traffic participants between the travel position pair w;

Indicates the set of passing paths of other traffic participants between the travel position pair w; p represents a passing path;

Indicates the total demand quantity between the travel positions of other traffic participants and w;

表示路段a是否属于出行位置对w之间的其他交通参与者的通行路径p上；

Indicates whether the road section a belongs to the passing path p of other traffic participants between the travel position pair w;

表示各个其他交通参与者的通行路段上其他交通参与者平均通行时间；α,β表示预设系统第二系数、第三系数；Q^v表示其他交通参与者在单位时间内通过的最大数量；M表示一个无穷大的数。

Indicates the average passing time of other traffic participants on the passing section of each other traffic participant; α, β represent the second coefficient and the third coefficient of the preset system; ^Qv represents the maximum number of other traffic participants passing in a unit time; M represents an infinite number.

S3: Jointly solve the models of S1 and S2 to obtain the planning results of the existing traffic network, that is, which road sections are newly built with robot-specific passages, and which road sections are prohibited from passing by other traffic participants.

The purpose of this specific example is to plan a robot traffic network that satisfies the connectivity constraints on the premise of minimizing the total system cost. The overall model is shown in S1, where the total system cost includes the construction cost of the robot-specific channel and the total traffic cost, as shown in As shown in the schematic diagram of the traffic network planned by the existing way shown in Figure 2, after the robot traffic network is planned in the existing traffic network, users will face the problem of choosing travel modes and travel routes, and the total traffic cost will also be affected. The specific model is shown in S2. In this step, the models of S1 and S2 are jointly solved to obtain a robot traffic network that guarantees the minimum robot-specific channel construction cost and total traffic cost and satisfies the constraint conditions. The traffic network schematic diagram obtained after optimization is shown in Figure 3 shown. Among them, the black filled road section shown in Figure 3 is the new construction robot passage section, and the white and black filled road section is the road section where other traffic participants are prohibited from passing.

The robot passage path planning method provided in the embodiment of the present application pre-constructs the robot passage network planning model for constraining the lowest construction cost of the robot-specific passage, and pre-constructs the robot passage equilibrium model and other traffic participation for constraining the robot's total passage cost to be the lowest. Through the joint solution of these three pre-built network planning models, the result of robot traffic path planning can not only meet the total traffic demand of the entire traffic network, ensure the connectivity of the traffic network, but also save the construction of dedicated roads for robots. cost.

Fig. 4 is a structural block diagram of a robot travel path planning device implementing an embodiment of the present application.

The robot passage path planning device provided in the embodiment of the present application, the device includes the following functional modules:

The first determination module 401 is used to determine the channel construction parameters corresponding to each road section in the transportation network, wherein the channel construction parameters include: the construction cost of the robot-specific channel, the total traffic cost, and the traffic category; the traffic category is used to indicate Whether to build a new robot-only passage on the road section;

The second determination module 402 is used to input the channel construction parameters corresponding to the road sections into the preset robot traffic network planning model;

The third determination module 403 is used to determine the travel demand of the robot between each position pair in the travel position pair set, the travel demand of other traffic participants, and the traffic cost parameters corresponding to each road section, wherein the traffic cost parameters include : robot traffic cost parameters, other traffic participants traffic cost parameters;

The first input module 404 is configured to input the travel demand of the robot and the travel cost parameters of the robot into a pre-built robot travel equilibrium model;

The second input module 405 is used to input the travel demand of the other traffic participants and the travel cost parameters of the other traffic participants into the pre-built traffic balance model of other traffic participants;

The solution module 406 is configured to jointly solve the robot traffic network planning model, the robot traffic equilibrium model, and the other traffic participant traffic equilibrium models to obtain robot traffic path planning results in the traffic network.

Optionally, the robot passing cost parameters include: the number of passing robots per unit time and the average passing time of robots per unit time;

The passing cost parameters of other traffic participants include: the number of passing other traffic participants per unit time and the average passing time of other traffic participants per unit time.

Optionally, the robot traffic network planning model includes:

The first sub-model that represents the minimum sum of the construction cost and the total traffic cost of the robot-specific passage in each road section;

a second sub-model representing a connected robot travel path between travel position pairs;

The third sub-model representing the travel path between travel position pairs.

Optionally, the third determination module includes:

The first sub-module is used to determine the probability that the user chooses to dispatch a robot based on the minimum passing time of the robot between the pair of locations, the minimum passing time of other traffic participants, and preset parameters for each position pair in the travel position pair set ;

The second submodule is used to determine the travel demand of the robot between the position pair based on the probability and the total travel demand corresponding to the position pair;

The third sub-module is configured to determine the difference between the total travel demand corresponding to the position pair and the travel demand of the robot between the position pair as the travel demand of other traffic participants between the position pair.

Optionally, the robot traffic balance model includes:

Characterize the fourth sub-model with the minimum sum of the robot average transit time in all road sections;

A fifth sub-model that characterizes the traffic volume of the robot in the passing path set between the location pairs, and is equal to the travel demand of the robot between the location pairs.

Optionally, the traffic equilibrium models of other traffic participants include:

The sixth sub-model representing the minimum sum of the average passing time of other traffic participants in all road sections;

A seventh sub-model representing the traffic volume of other traffic participants in the passing path set between the location pairs, which is equal to the travel demand of other traffic participants between the location pairs.

The robot passage path planning device provided by the embodiment of the present application pre-constructs the robot passage network planning model for constraining the lowest construction cost of the robot-specific passage, and pre-constructs the robot passage equilibrium model and other traffic participation for constraining the robot's total passage cost to be the lowest. Through the joint solution of these three pre-built network planning models, the result of robot traffic path planning can not only meet the total traffic demand of the entire traffic network, ensure the connectivity of the traffic network, but also save the construction of dedicated roads for robots. cost.

The robot path planning device shown in FIG. 4 in the embodiment of the present application may be a device, or a component, an integrated circuit, or a chip in a server. The robot path planning device shown in FIG. 4 in the embodiment of the present application may be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.

The robot passage path planning device shown in FIG. 4 provided by the embodiment of the present application can realize various processes realized by the method embodiment in FIG. 1 , and details are not repeated here to avoid repetition.

Optionally, as shown in FIG. 5 , the embodiment of the present application further provides an electronic device 500, including a processor 501, a memory 502, and programs or instructions stored in the memory 502 and operable on the processor 501, When the program or instruction is executed by the processor 501, each process of the above-mentioned embodiment of the robot path planning method can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

It should be noted that the electronic device in the embodiment of the present application includes the above-mentioned server.

The embodiment of the present application also provides a readable storage medium. The readable storage medium stores programs or instructions. When the program or instructions are executed by the processor, the various processes of the above-mentioned embodiments of the robot path planning method are implemented, and can To achieve the same technical effect, in order to avoid repetition, no more details are given here.

Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.

The embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to realize the implementation of the above robot path planning method Each process of the example, and can achieve the same technical effect, in order to avoid repetition, will not repeat them here.

It should be understood that the chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

The above description is a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, these improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (10)

1. A robot passing path planning method is characterized by comprising the following steps:

determining channel construction parameters corresponding to each road section in a traffic network, wherein the channel construction parameters comprise: the construction cost, the total passing cost and the passing category of the special channel for the robot; the passage category is used for indicating whether a new special robot passage is built on a road section or not;

inputting the channel construction parameters corresponding to each road section into a preset robot passing network planning model;

determining travel demands of the robot between each position pair in the row position pair set, travel demands of other traffic participants and corresponding traffic cost parameters of each road section, wherein the traffic cost parameters comprise: the robot passage cost parameter and the passage cost parameters of other traffic participants;

inputting the travel demand of the robot and the robot passing cost parameter into a pre-constructed robot passing balance model;

inputting the travel demands of the other traffic participants and the passing cost parameters of the other traffic participants into a pre-constructed passing balance model of the other traffic participants;

and jointly solving the robot passing network planning model, the robot passing balance model and the other traffic participant passing balance models to obtain a robot passing path planning result in the traffic network.

2. The method of claim 1, wherein:

the robot passage cost parameters include: the number of the passing robots in unit time and the average passing time of the robots in unit time;

the other traffic participant passage cost parameters include: the number of other traffic participants passing in the unit time and the average transit time of the other traffic participants in the unit time.

3. The method of claim 1, wherein the robotic traffic network planning model comprises:

the method comprises the steps that a first sub-model with the minimum sum of construction cost and total passing cost of a robot special channel of each road section is represented;

a second submodel for representing a robot passing path communicated between the travel position pairs;

and characterizing a third submodel of the robot passing path between the travel position pairs.

4. The method of claim 1, wherein the step of determining the travel needs of the robot and the travel needs of other transportation participants between each position pair in the set of row position pairs comprises:

for each position pair in the travel position pair set, determining the probability of the user selecting the dispatching robot based on the minimum passing time of the robot between the position pairs, the minimum passing time of other traffic participants and preset parameters;

determining travel demands of the robot between the position pairs based on the probability and total travel demands corresponding to the position pairs;

and determining the difference between the total travel demand corresponding to the position pair and the travel demand of the robot between the position pair as the travel demands of other traffic participants between the position pair.

5. The method of claim 1, wherein the robotic traffic balancing model comprises:

the fourth submodel with the minimum sum of the average passing time of the robots in all road sections is represented;

and a fifth submodel which represents the traffic volume of the robot in the traffic path set between the position pairs and is equal to the travel demand of the robot between the position pairs.

6. The method of claim 1, wherein the other traffic participant traffic balancing model comprises:

the sixth submodel is used for representing the smallest sum of the average passing times of other traffic participants in all road sections;

and the seventh submodel is used for representing the traffic volume of other traffic participants in the traffic path set between the position pairs and is equal to the travel demands of the other traffic participants between the position pairs.

7. A robot passage path planning apparatus, characterized in that the apparatus comprises:

the first determining module is configured to determine a channel construction parameter corresponding to each road segment in a traffic network, where the channel construction parameter includes: the construction cost, the total passing cost and the passing category of the special channel for the robot; the traffic category is used for indicating whether a new special channel for the robot is built on a road section or not;

the second determining module is used for inputting the channel construction parameters corresponding to the road sections into a preset robot passing network planning model;

a third determining module, configured to determine travel demands of the robot between each position pair in the row position pair set, travel demands of other transportation participants, and a passing cost parameter corresponding to each road segment, where the passing cost parameter includes: the robot passage cost parameter and the passage cost parameters of other traffic participants;

the first input module is used for inputting the travel requirement of the robot and the robot passing cost parameter into a robot passing balance model which is constructed in advance;

the second input module is used for inputting the travel demands of the other traffic participants and the passing cost parameters of the other traffic participants into a pre-constructed passing balance model of the other traffic participants;

and the solving module is used for jointly solving the robot passing network planning model, the robot passing balance model and the other traffic participant passing balance models to obtain a robot passing path planning result in the traffic network.

8. The apparatus of claim 7, wherein:

the robot passage cost parameters include: the number of the passing robots in unit time and the average passing time of the robots in unit time;

the other traffic participant passage cost parameters include: the number of other traffic participants passing in the unit of time and the average transit time of the other traffic participants in the unit of time.

9. The apparatus of claim 7, wherein the robotic traffic network planning model comprises:

the method comprises the steps that a first sub-model with the minimum sum of construction cost and total passing cost of a robot special channel of each road section is represented;

a second submodel for representing a robot passing path communicated between the travel position pairs;

and characterizing a third submodel of the robot passing path between the travel position pairs.

10. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, which when executed by the processor, implement the steps of the robot transit path planning method of any of claims 1-6.

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