A robot path planning method and apparatus, electronic equipment

Abstract

This invention discloses a robot travel path planning scheme, belonging to the field of intelligent hardware technology. The method includes: determining the channel construction parameters corresponding to each road segment in the traffic network and inputting them into a preset robot traffic network planning model; inputting the determined robot travel demand and robot travel cost parameters into a pre-constructed robot traffic equilibrium model; inputting the determined travel demand and other traffic participant travel cost parameters into a pre-constructed other traffic participant traffic equilibrium model; and jointly solving the robot traffic network planning model, the robot traffic equilibrium model, and the other traffic participant traffic equilibrium models to obtain the robot travel path planning result in the traffic network. The obtained planning result satisfies the total traffic demand of the entire traffic network and saves the construction cost of dedicated robot roads.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent hardware, and in particular to a robot passage path planning method and device and electronic equipment. BACKGROUND

[0002] In a complex traffic environment where robots and other traffic participants exist at the same time, in order to ensure safe operation of the robots, a robot-only passage can be built in a road section, or the road section can be directly planned as a road section that only allows the robots to pass.

[0003] Considering that the construction cost of the robot-only passage is high, whether each road section needs to be built as a robot-only passage and whether the road section needs to be planned as a road section that only allows the robots to pass in an existing passage network is a key problem. How to reasonably plan this problem so that the planning meets the passage demand of the entire passage network and saves the construction cost of the robot-only passage is a technical problem that needs to be solved by the person skilled in the art. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a robot passage path planning method and device and electronic equipment, which can solve the problem that the robot path cannot be reasonably planned at present.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] The embodiments of the present application provide a robot passage path planning method, wherein the method comprises: determining passage construction parameters corresponding to each road section in a traffic network, wherein the passage construction parameters comprise: construction cost of a robot-only passage, total passage cost, and passage category; the passage category is used to indicate whether a new robot-only passage is built for the road section;

[0007] The passage construction parameters corresponding to each road section are input into a preset robot passage network planning model;

[0008] The passage demand of the robots, the passage demand of other traffic participants, and passage cost parameters corresponding to each road section between each position pair in a set of position pairs are determined, wherein the passage cost parameters comprise: robot passage cost parameters and other traffic participant passage cost parameters;

[0009] The passage demand of the robots and the robot passage cost parameters are input into a robot passage equilibrium model that is constructed in advance;

[0010] The passage demand of the other traffic participants and the other traffic participant passage cost parameters are input into an other traffic participant passage equilibrium model that is constructed in advance;

[0011] Solve the robot traffic network planning model, the robot traffic equilibrium model and the other traffic participant traffic equilibrium model jointly to obtain a robot traffic path planning result in the traffic network.

[0012] Optionally, the robot traffic cost parameter comprises: a number of robots passing through per unit time and an average travel time of the robots per unit time.

[0013] The other traffic participant traffic cost parameter comprises: a number of other traffic participants passing through per unit time and an average travel time of the other traffic participants per unit time.

[0014] Optionally, the robot traffic network planning model comprises:

[0015] a first sub-model representing that a sum of a construction cost and a total travel cost of a robot exclusive lane of each road section is minimum;

[0016] a second sub-model representing that a robot traffic path exists between a pair of travel locations;

[0017] a third sub-model representing that a robot traffic path exists between a pair of travel locations.

[0018] Optionally, the step of determining a travel demand of a robot and a travel demand of other traffic participants between each pair of locations in the set of pairs of travel locations comprises:

[0019] for each pair of locations in the set of pairs of travel locations, determining a probability that a user selects to dispatch a robot based on a minimum travel time of a robot, a minimum travel time of other traffic participants and a preset parameter between the pair of locations;

[0020] determining a travel demand of a robot between the pair of locations based on the probability and a total travel demand corresponding to the pair of locations;

[0021] determining a travel demand of other traffic participants between the pair of locations as a difference between the total travel demand corresponding to the pair of locations and the travel demand of the robot between the pair of locations.

[0022] Optionally, the robot traffic equilibrium model comprises:

[0023] a fourth sub-model representing that a sum of average travel times of robots in all road sections is minimum;

[0024] a fifth sub-model representing that a traffic volume of a robot in a set of traffic paths between a pair of locations is equal to a travel demand of the robot between the pair of locations.

[0025] Optionally, the other traffic participant traffic equilibrium model comprises:

[0026] a sixth sub-model representing a sum of average travel times of other traffic participants in all road segments as minimum;

[0027] a seventh sub-model representing a traffic volume of other traffic participants in a set of travel paths between a pair of locations as equal to a travel demand of other traffic participants between the pair of locations.

[0028] The embodiment of the present application provides a robot travel path planning device, wherein the device comprises: a first determination module configured to determine channel construction parameters corresponding to each road segment in a traffic network, wherein the channel construction parameters comprise: construction cost of a robot-only channel, total travel cost, and travel category; the travel category is used to indicate whether a new robot-only channel is constructed on the road segment; a second determination module configured to input the channel construction parameters corresponding to each road segment into a preset robot travel network planning model; a third determination module configured to determine travel demand of a robot, travel demand of other traffic participants, and travel cost parameters corresponding to each road segment between each pair of locations in a set of pairs of locations, wherein the travel cost parameters comprise: robot travel cost parameters and other traffic participant travel cost parameters; a first input module configured to input the travel demand of the robot and the robot travel cost parameters into a robot travel equilibrium model constructed in advance; a second input module configured to input the travel demand of the other traffic participants and the other traffic participant travel cost parameters into an other traffic participant travel equilibrium model constructed in advance; and a solving module configured to jointly solve the robot travel network planning model, the robot travel equilibrium model and the other traffic participant travel equilibrium model to obtain a robot travel path planning result in the traffic network.

[0029] Optionally, the robot travel cost parameters comprise: number of robots passing through per unit time and average travel time of the robots per unit time.

[0030] The other traffic participant travel cost parameters comprise: number of other traffic participants passing through per unit time and average travel time of the other traffic participants per unit time.

[0031] Optionally, the robot travel network planning model comprises:

[0032] a first sub-model representing a sum of construction cost of a robot-only channel and total travel cost of each road segment as minimum;

[0033] a second sub-model representing a robot travel path between a pair of locations as existing;

[0034] a third sub-model representing a robot travel path between a pair of locations.

[0035] Optionally, the third determining module includes:

[0036] The first submodule is used to determine the probability that a user will choose to dispatch a robot for each location pair in the set of travel location pairs, based on the minimum robot passage time between the location pairs, the minimum passage time of other traffic participants, and preset parameters.

[0037] The second submodule is used to determine the robot's travel demand between the location pairs based on the probability and the total travel demand corresponding to the location pairs.

[0038] The third submodule is used to determine the difference between the total travel demand corresponding to the location pair and the travel demand of the robots between the location pairs as the travel demand of other traffic participants between the location pairs.

[0039] Optionally, the robot traffic balancing model includes:

[0040] The fourth sub-model represents the model that minimizes the sum of the average travel times of robots across all road segments;

[0041] A fifth sub-model represents the robot traffic volume in the set of travel paths between location pairs, which is equal to the robot's travel demand between the location pairs.

[0042] Optionally, the other traffic participant equilibrium model includes:

[0043] The sixth sub-model represents the model that minimizes the sum of the average travel times of all other traffic participants across all road segments;

[0044] The seventh sub-model represents the traffic volume of other traffic participants in the set of travel paths between location pairs, which is equal to the travel demand of other traffic participants between the location pairs.

[0045] 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 robot path planning methods described above.

[0046] 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 robot path planning methods described above.

[0047] The robot travel path planning scheme provided in this embodiment of the invention determines the channel construction parameters corresponding to each road segment in the traffic network; inputs the channel construction parameters corresponding to each road segment into a preset robot travel network planning model; determines the robot's travel demand, the travel demand of other traffic participants, and the travel cost parameters corresponding to each road segment for each location pair in the travel location pair set; inputs the robot's travel demand and robot travel cost parameters into a pre-constructed robot travel equilibrium model; inputs the travel demand and other traffic participant travel cost parameters into a pre-constructed other traffic participant travel equilibrium model; and jointly solves the robot travel network planning model, the robot travel equilibrium model, and the other traffic participant travel equilibrium models to obtain the robot travel path planning result in the traffic network. The robot path planning method provided in this application pre-constructs a robot traffic network planning model to constrain the lowest construction cost of dedicated robot lanes, and pre-constructs a robot traffic equilibrium model and other traffic participant traffic equilibrium models to constrain the lowest total robot traffic cost. By jointly solving these three pre-constructed network planning models, the resulting robot path planning result not only meets the total traffic demand of the entire traffic network and ensures the connectivity of the traffic network, but also saves the construction cost of dedicated robot roads. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the steps of a robot path planning method according to an embodiment of this application;

[0049] Figure 2 This is a schematic diagram representing a transportation network planned using existing methods;

[0050] Figure 3 This is a schematic diagram of the traffic network planned by the robot path planning method in the embodiments of this application;

[0051] Figure 4 This is a structural block diagram illustrating a robot path planning device according to an embodiment of this application;

[0052] Figure 5 This is a structural block diagram illustrating an embodiment of an electronic device according to this application. Detailed Implementation

[0053] 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.

[0054] The robot path planning scheme provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0055] As attachedFigure 1 As shown, the robot path planning method in this application includes the following steps:

[0056] Step 101: Determine the corridor construction parameters corresponding to each road segment in the transportation network.

[0057] The corridor construction parameters include: the construction cost of the dedicated robot corridor, the total passage cost, and the passage category. The passage category indicates whether a new dedicated robot corridor should be built on a road segment. In addition, the corridor construction parameters may also include the road segment type, which indicates whether the road segment belongs to the robot passage path between travel location pairs.

[0058] The robot path planning method of this application embodiment is applied to an electronic device, which can be a server, computer, or other device with analysis capabilities. The storage medium in the electronic device stores a robot path planning and recognition program, and the processor of the electronic device executes the robot path planning process by running the program in the storage medium.

[0059] Step 102: Input the corresponding channel construction parameters for each road segment into the preset robot traffic network planning model.

[0060] The robot traffic network planning model includes: a first sub-model that represents minimizing the sum of the construction cost and the total traffic cost of dedicated robot lanes in each road segment; a second sub-model that represents robot traffic paths connecting pairs of travel locations; and a third sub-model that represents robot traffic paths between pairs of travel locations.

[0061] In this embodiment, a robot traffic network planning model is pre-built. By solving this model, a modification scheme for the existing traffic network can be obtained. The modification scheme specifically includes the modification results for each road segment, which can be categorized into three types: first, constructing dedicated robot lanes within the road segment; second, directly planning the road segment as a route that only allows robot passage and prohibits other traffic participants; and third, no modification. The modification objective is to plan a robot traffic network while minimizing the total system cost and ensuring network connectivity. The total system cost = cost of constructing dedicated robot lanes + total traffic cost.

[0062] Step 103: Determine the travel demand of the robot between each location pair in the travel location pair set, the travel demand of other traffic participants, and the corresponding toll cost parameters for each road segment.

[0063] The toll cost parameters include: robot toll cost parameters and other toll cost parameters.

[0064] An optional way to determine the robot's travel demand and the travel demand of other traffic participants between each pair of locations in the set of travel location pairs may be as follows:

[0065] First, for each location pair in the set of travel location pairs, the probability of a user choosing to dispatch a robot is determined based on the minimum travel time of the robot between the location pairs, the minimum travel time of other traffic participants, and preset parameters.

[0066] Secondly, based on the probability and the total travel demand corresponding to the location pair, the travel demand of the robot between the location pairs is determined;

[0067] Next, the difference between the total travel demand corresponding to a location pair and the travel demand of the robots between the location pairs is determined as the travel demand of other traffic participants between the location pairs.

[0068] In practical implementation, the travel demand of robots between the entrance and exit points of each location pair can be determined through the following travel demand segmentation model, and the travel demand of other traffic participants between the entrance and exit points of each location pair can also be determined.

[0069] Step 104: Input the robot's travel demand and robot travel cost parameters into the pre-built robot travel equilibrium model.

[0070] The parameters for robot passage cost include: the number of robots passing through per unit time and the average passage time of robots per unit time.

[0071] The robot traffic network planning model models the robot traffic network planning problem from a cost-saving perspective. It's understood that planning a robot traffic network changes the existing network, requiring users to consider the following issues: mode of transport selection (choosing between robots and other traffic participants), and route selection after determining the mode of transport. To address these issues, this embodiment assumes the total travel demand between travel locations w remains constant, meaning the travel demand for robots and other traffic participants between travel locations w remains unchanged. Based on this, a travel demand partitioning model is pre-built. According to the travel demand partitioning model, the travel demand for robots and other traffic participants between each travel location pair w can be determined. Furthermore, robot traffic equilibrium models and other traffic participant traffic equilibrium models need to be pre-built. By substituting the travel demand of robots and other traffic participants into these models respectively, and then jointly solving them with the robot traffic network planning model, a robot traffic path planning scheme can be finally determined that satisfies the total traffic demand of the entire traffic network, ensuring network connectivity, while also saving on the construction costs of dedicated robot roads.

[0072] The robot traffic equilibrium model includes: a fourth sub-model that represents minimizing the sum of average robot travel times across all road segments; and a fifth sub-model that represents the robot traffic volume in the set of travel paths between location pairs being equal to the travel demand of robots between the location pairs.

[0073] Step 105: Input the travel demand and travel cost parameters of other traffic participants into the pre-built traffic equilibrium model of other traffic participants.

[0074] Among them, the parameters for the passage cost of other traffic participants include: the number of other traffic participants passing through per unit time and the average passage time of other traffic participants per unit time.

[0075] Other traffic participant traffic equilibrium models include, but are not limited to: a sixth sub-model that represents minimizing the sum of the average travel times of other traffic participants across all road segments; and a seventh sub-model that represents that the traffic volume of other traffic participants in the set of travel paths between location pairs is equal to the travel demand of other traffic participants between location pairs.

[0076] Step 106: Jointly solve the robot traffic network planning model, the robot traffic equilibrium model, and the traffic equilibrium models of other traffic participants to obtain the robot traffic path planning results in the traffic network.

[0077] The purpose of this scheme is to plan a robot access network that satisfies connectivity constraints while minimizing the total system cost.

[0078] The following is combined Figures 2-3 The following table 1 illustrates the robot path planning method shown in the embodiments of this application.

[0079] Figure 2 This is a schematic diagram of a traffic network planned using existing methods. It is assumed that the total travel demand between travel location pairs is a known quantity, as shown in Table 1, and that changes in the travel network will not alter this value. Based on this, in this embodiment, the robot travel path is optimized using the robot path planning method provided in this embodiment, in conjunction with Table 1. The specific optimization process is as follows:

[0080] Table 1

[0081] Serial number Starting point End point Total demand Serial number Starting point End point Total demand 1 1 10 1300 11 12 10 2000 2 1 19 3000 12 13 4 1600 3 2 22 1000 13 13 22 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 23 1300 19 23 13 1800 10 11 15 1400 20 23 24 1700

[0082] S1: Construct a robot access network planning model.

[0083] The purpose of this step is to construct a robot traffic network planning model. By solving this model, modification schemes for the existing traffic network can be obtained. These modification schemes specifically include the modification results for each road segment, which can be categorized into three types: first, constructing dedicated robot lanes within the road segment; second, directly planning the road segment as a route that only allows robot traffic and prohibits other traffic participants; and third, no modification.

[0084] The goal of the transformation is to plan a robot access network while minimizing the total system cost and ensuring network connectivity. The total system cost equals the construction cost of dedicated robot lanes plus the total access cost.

[0085] The robot access network planning model is as follows:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] The meanings of the parameters in the robot traffic network planning model are as follows:

[0092] A represents the set of all road segments, and a represents any road segment;

[0093] N represents the set of all location points;

[0094] I represents the set of all travel location pairs, which include the entrance location and the exit location, and I∈N;

[0095] x a Indicate whether a new dedicated robot lane will be built on road segment a; c a This indicates the cost of constructing a new dedicated robot lane on road segment a; This indicates the construction cost of a dedicated robot walkway.

[0096] σ represents the travel time conversion factor; This represents the number of robots passing through road segment a per unit time. This indicates that there are sections a on the road within a unit of time. The average passage time of the robot when the robot passes by; This represents the number of other traffic participants passing through road segment a per unit of time; This indicates that there are sections a on the road within a unit of time. Average travel time of other traffic participants when they pass by; This represents the total toll cost. Formula (1) is the first sub-model.

[0097] y a It indicates whether road segment a prohibits other traffic participants from passing through; Formula (2) indicates that if a robot can pass through a road segment, either a robot-only lane has been built on the road segment, or the road segment has adopted a plan that prohibits other traffic participants from passing through. Only one of the two planning schemes can be selected.

[0098] Indicates whether segment a belongs to the robot's travel path between the starting point r and e; i is the entry point of the segment, and j is the exit point of the segment; a = (i,j) ∈ A.

[0099] Equations (3) and (4) indicate that, for any two points r from the travel location pair, there exists a connected robot travel path between r and e. Equations (3) and (4) constitute the second sub-model.

[0100] Formula (4) means that any two points r from the travel location pair can be selected to travel to e. Any segment of the robot-dedicated path connecting r and e is a robot-dedicated segment. Formula (4) is the third sub-model.

[0101] Formula (5) means that x a ,y a , All are 0-1 variables.

[0102] S2: Construct a travel demand segmentation model.

[0103] The previous step modeled the robot traffic network planning problem from a cost-saving perspective. It's understandable that planning the robot traffic network alters the existing network, requiring users to consider the following issues: mode of transport selection (whether to choose a robot or other road users), and route selection after determining the mode of transport. This step aims to model these issues.

[0104] Before and after planning the robot traffic network, this scheme assumes that the total travel demand between travel locations w remains unchanged, meaning that the travel demand of robots between travel locations w and the travel demand of other traffic participants remains unchanged. Based on this, this step constructs a travel demand partitioning model. According to the travel demand partitioning model, the travel demand of robots between each travel location w and the travel demand of other traffic participants can be determined.

[0105] The travel demand segmentation model is as follows:

[0106]

[0107]

[0108]

[0109] The meanings of each parameter are as follows:

[0110] W represents the set of travel location pairs;

[0111] w indicates the travel location pair;

[0112] This represents the travel demand of robots between travel locations w;

[0113] This indicates the travel demand of other traffic participants between travel locations w;

[0114] d w This represents the total travel demand between travel locations w;

[0115] This represents the probability that a user will choose to send a robot to a location between travel locations w;

[0116] θ system preset parameters;

[0117] This represents the minimum travel time for robots between travel location pairs w;

[0118] This represents the minimum travel time for other traffic participants between travel locations w.

[0119] In practical implementation, the travel demand of robots between the entrance and exit points of each location pair can be determined through the following travel demand segmentation model, and the travel demand of other traffic participants between the entrance and exit points of each location pair can also be determined.

[0120] The travel demand of robots between travel locations w is obtained. Next, the robot traffic equilibrium model is constructed as follows:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] Among them, formula (9) is the fourth sub-model, and formula (10) is the fifth sub-model.

[0127] This represents the number of robots passing through road segment a per unit time. This indicates that there are sections a on the road within a unit of time. The average travel time of the robot when the robot passes by;

[0128] This represents the robot traffic volume along the robot travel path p between travel locations w; p represents the set of robot travel paths between travel location pairs w; p represents a single travel path. This represents the travel demand of robots between travel locations w;

[0129] Indicate whether road segment a belongs to the robot travel path p between travel location pairs w; W represents the set of travel location pairs;

[0130] This represents the growth factor of robot travel time; The average travel time of the robot on each robot's route segment is represented by α and β, respectively, which represent the preset system's second and third coefficients. b This represents the maximum number of times the robot can pass per unit of time; M represents an infinite number.

[0131] After obtaining the travel demand of other traffic participants between travel locations w Next, the traffic equilibrium model for other traffic participants is constructed as follows:

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Among them, formula (14) is the sixth sub-model, and formula (15) is the seventh sub-model.

[0138] The meanings of the parameters in the traffic equilibrium model for other traffic participants are as follows:

[0139] This represents the number of other traffic participants passing through road segment a per unit of time; This indicates that there are sections a on the road within a unit of time. The average travel time of other traffic participants when one other traffic participant passes by;

[0140] This represents the traffic volume of other traffic participants on the travel path p between travel locations w; p represents the set of travel paths for other traffic participants between travel locations w; p represents a travel path. This represents the total demand for travel between w locations by other traffic participants;

[0141] Indicates whether road segment a belongs to the travel path p of other traffic participants between travel location pairs w;

[0142] The average travel time of other traffic participants on the same road segment represents the average travel time of other traffic participants; α and β represent the second and third coefficients of the preset system; Q represents the average travel time of other traffic participants on the same road segment. v This represents the maximum number of other traffic participants that can pass through per unit of time; M represents an infinite number.

[0143] S3: Solve the models of S1 and S2 together to obtain the planning results of the existing traffic network, namely, which road segments have newly built robot-only lanes and which road segments prohibit other traffic participants from passing through.

[0144] The purpose of this specific example is to plan a robot access network that satisfies connectivity constraints while minimizing the total system cost. The overall model is shown in S1, where the total system cost includes the construction cost of dedicated robot lanes and the total access cost, as follows: Figure 2 The diagram shows a traffic network planned using the existing method. After a robot traffic network is planned within the existing network, users will face the problem of choosing travel modes and routes, and the total travel cost will also be affected. The specific model is shown in S2. In this step, by jointly solving models S1 and S2, we can obtain a robot traffic network that minimizes the construction cost of dedicated robot lanes and the total travel cost while satisfying the constraints. The optimized traffic network diagram is shown below. Figure 3 As shown. Among them, Figure 3 The black-filled sections shown are newly constructed robot access routes, while the white and black-filled sections are sections where other road users are prohibited from passing.

[0145] The robot path planning method provided in this application pre-constructs a robot traffic network planning model to constrain the lowest construction cost of dedicated robot lanes, and pre-constructs a robot traffic equilibrium model and other traffic participant traffic equilibrium models to constrain the lowest total robot traffic cost. By jointly solving these three pre-constructed network planning models, the resulting robot path planning result not only meets the total traffic demand of the entire traffic network and ensures the connectivity of the traffic network, but also saves the construction cost of dedicated robot roads.

[0146] Figure 4 The structural block diagram of a robot path planning device according to an embodiment of this application is shown.

[0147] The robot path planning device provided in this application includes the following functional modules:

[0148] The first determining module 401 is used to determine the channel construction parameters corresponding to each road segment in the traffic network. The channel construction parameters include: the construction cost of the robot-dedicated channel, the total passage cost, and the passage category. The passage category is used to indicate whether a new robot-dedicated channel should be built on the road segment.

[0149] The second determining module 402 is used to input the channel construction parameters corresponding to each road segment into a preset robot traffic network planning model;

[0150] The third determining module 403 is used to determine the robot's travel demand, the travel demand of other traffic participants, and the passage cost parameters corresponding to each road segment between each location pair in the travel location pair set. The passage cost parameters include: robot passage cost parameters and other traffic participant passage cost parameters.

[0151] The first input module 404 is used to input the robot's travel demand and the robot's travel cost parameters into a pre-built robot travel equilibrium model;

[0152] The second input module 405 is used to input the travel needs of the other traffic participants and the travel cost parameters of the other traffic participants into a pre-built traffic equilibrium model of other traffic participants.

[0153] The solver module 406 is used to jointly solve the robot traffic network planning model, the robot traffic equilibrium model, and the other traffic participant traffic equilibrium model to obtain the robot traffic path planning results in the traffic network.

[0154] Optionally, the robot passage cost parameters include: the number of robots passing through per unit time and the average passage time of the robots per unit time;

[0155] The other traffic participant travel cost parameters include: the number of other traffic participants passing through per unit time and the average travel time of other traffic participants per unit time.

[0156] Optionally, the robot access network planning model includes:

[0157] The first sub-model represents the one that minimizes the sum of the construction cost and the total passage cost of the dedicated robot lanes on each road segment;

[0158] A second sub-model representing robot travel paths that connect pairs of travel locations;

[0159] The third sub-model represents the robot's travel path between pairs of travel locations.

[0160] Optionally, the third determining module includes:

[0161] The first submodule is used to determine the probability that a user will choose to dispatch a robot for each location pair in the set of travel location pairs, based on the minimum robot passage time between the location pairs, the minimum passage time of other traffic participants, and preset parameters.

[0162] The second submodule is used to determine the robot's travel demand between the location pairs based on the probability and the total travel demand corresponding to the location pairs.

[0163] The third submodule is used to determine the difference between the total travel demand corresponding to the location pair and the travel demand of the robots between the location pairs as the travel demand of other traffic participants between the location pairs.

[0164] Optionally, the robot traffic balancing model includes:

[0165] The fourth sub-model represents the model that minimizes the sum of the average travel times of robots across all road segments;

[0166] A fifth sub-model represents the robot traffic volume in the set of travel paths between location pairs, which is equal to the robot's travel demand between the location pairs.

[0167] Optionally, the other traffic participant equilibrium model includes:

[0168] The sixth sub-model represents the model that minimizes the sum of the average travel times of all other traffic participants across all road segments;

[0169] The seventh sub-model represents the traffic volume of other traffic participants in the set of travel paths between location pairs, which is equal to the travel demand of other traffic participants between the location pairs.

[0170] The robot path planning device provided in this application pre-constructs a robot traffic network planning model to constrain the lowest construction cost of robot-dedicated lanes, and pre-constructs a robot traffic equilibrium model and other traffic participant traffic equilibrium models to constrain the lowest total robot traffic cost. By jointly solving these three pre-constructed network planning models, the resulting robot traffic path planning result not only meets the total traffic demand of the entire traffic network and ensures the connectivity of the traffic network, but also saves the construction cost of robot-dedicated roads.

[0171] In the embodiments of this application Figure 4 The robot 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 4 The robot path 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 embodiment does not specifically limit it.

[0172] The embodiments provided in this application Figure 4 The robot 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.

[0173] Optionally, such as Figure 5 As shown, this application embodiment also provides an electronic device 500, including a processor 501, a memory 502, and a program or instructions stored in the memory 502 and executable on the processor 501. When the program or instructions are executed by the processor 501, they implement the various processes of the above-described robot path planning method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0174] It should be noted that the electronic device in this application embodiment includes the server described above.

[0175] 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 robot path planning method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0176] 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.

[0177] 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 the various processes of the above-described robot path planning method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0178] 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.

[0179] 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.

[0180] 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 robot passage route planning method characterized by comprising: The method comprises: determining channel construction parameters corresponding to each road section in a traffic network, wherein the channel construction parameters comprise: construction cost of a robot-only channel, total travel cost, and travel category, and the travel category is used to indicate whether a new robot-only channel is constructed on the road section; inputting the channel construction parameters corresponding to each road section into a preset robot travel network planning model; determining robot travel demand between each location pair in a set of travel location pairs, other traffic participant travel demand, and travel cost parameters corresponding to each road section, wherein the travel cost parameters comprise: robot travel cost parameters and other traffic participant travel cost parameters; inputting the robot travel demand and the robot travel cost parameters into a robot travel equilibrium model constructed in advance; inputting the other traffic participant travel demand and the other traffic participant travel cost parameters into an other traffic participant travel equilibrium model constructed in advance; jointly solving the robot travel network planning model, the robot travel equilibrium model, and the other traffic participant travel equilibrium model to obtain robot travel path planning results in the traffic network; the robot travel network planning model comprises: a first sub-model representing that a sum of construction cost and total travel cost of a robot-only channel of each road section is minimum; a second sub-model representing that a robot travel path exists between the travel location pairs; a third sub-model representing that a robot travel path exists between the travel location pairs.

2. The method of claim 1, wherein: the robot travel cost parameters comprise: number of robots passing through per unit time and average travel time of robots per unit time; the other traffic participant travel cost parameters comprise: number of other traffic participants passing through per unit time and average travel time of other traffic participants per unit time.

3. The method of claim 1, wherein, The step of determining robot travel demand between each location pair in a set of travel location pairs and other traffic participant travel demand comprises: for each location pair in the set of travel location pairs, determining a probability that a user selects to dispatch a robot based on robot minimum travel time, other traffic participant minimum travel time, and a preset parameter between the location pair; determining robot travel demand between the location pair based on the probability and total travel demand corresponding to the location pair; determining other traffic participant travel demand between the location pair as a difference between the total travel demand corresponding to the location pair and the robot travel demand between the location pair.

4. The method of claim 1, wherein, The robot travel equilibrium model comprises: a fourth sub-model representing that a sum of average travel time of robots in all road sections is minimum; a fifth sub-model representing that robot traffic volume in a set of travel paths between a location pair is equal to robot travel demand between the location pair.

5. The method of claim 1, wherein, The other traffic participant travel equilibrium model comprises: a sixth sub-model representing that a sum of average travel time of other traffic participants in all road sections is minimum; A seventh sub-model representing traffic volume of other traffic participants in a set of travel paths between a pair of locations, equal to travel demand of other traffic participants between the pair of locations.

6. A robot passage route planning device characterized by comprising: The apparatus comprises: A first determining module configured to determine channel construction parameters corresponding to each road segment in a traffic network, wherein the channel construction parameters comprise construction cost of a robot-only channel, total travel cost, and travel category, the travel category being used to indicate whether a new robot-only channel is constructed on the road segment; A second determining module configured to input the channel construction parameters corresponding to each road segment into a preset robot travel network planning model; A third determining module configured to determine travel demand of robots, travel demand of other traffic participants, and travel cost parameters corresponding to each road segment between each pair of locations in a set of pairs of locations, wherein the travel cost parameters comprise robot travel cost parameters and other traffic participant travel cost parameters; A first inputting module configured to input the travel demand of robots and the robot travel cost parameters into a robot travel equilibrium model constructed in advance; A second inputting module configured to input the travel demand of other traffic participants and the other traffic participant travel cost parameters into an other traffic participant travel equilibrium model constructed in advance; A solving module configured to jointly solve the robot travel network planning model, the robot travel equilibrium model, and the other traffic participant travel equilibrium model to obtain a robot travel path planning result in the traffic network. The robot travel network planning model comprises: A first sub-model representing that a sum of construction cost and total travel cost of a robot-only channel of each road segment is minimum; A second sub-model representing that there is a connected robot travel path between a pair of locations; A third sub-model representing that a path between a pair of locations is a robot travel path.

7. The apparatus according to claim 6, wherein: The robot travel cost parameters comprise number of robots passing through per unit time and average travel time of robots per unit time; The other traffic participant travel cost parameters comprise number of other traffic participants passing through per unit time and average travel time of other traffic participants per unit time.

8. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions being executed by the processor to implement steps of the robot travel path planning method according to any one of claims 1-5.

Images