Unmanned vehicle path planning method based on three-dimensional modeling

Abstract

The invention discloses an unmanned vehicle path planning method based on three-dimensional modeling, and the method comprises the steps: 1, a control terminal constructs a plurality of predetermined navigation paths of an unmanned vehicle based on a target position, and the control terminal transmits a motion instruction to the unmanned vehicle based on the predetermined navigation paths; 2, the unmanned vehicle end receives the motion instruction of the control terminal and the predetermined navigation path, the unmanned vehicle plans virtual parameters of the predetermined navigation path based on unmanned vehicle state monitoring and unmanned vehicle road condition monitoring, and then the unmanned vehicle end corrects the predetermined navigation path in real time. According to the method, the running state of the unmanned vehicle and the running cardinal number of the cruise road condition are mapped, and the cruise road condition is selected according to the running state of the unmanned vehicle in the path planning process, so that the unmanned vehicle with the poor running state can plan the path with the good running cardinal number of the cruise road condition, and the running stability of the unmanned vehicle along the planned path is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and particularly to an unmanned vehicle path planning method based on three-dimensional modeling. Background Art

[0002] Autonomous vehicles (also known as driverless vehicles, computer-driven cars, or wheeled mobile robots) are intelligent vehicles that achieve driverless operation through a computer system. They have a history of several decades in the 20th century and showed a trend towards practicality in the early 21st century.

[0003] Autonomous vehicles rely on the collaborative cooperation of artificial intelligence, visual computing, radar, monitoring devices, and the global positioning system, enabling the computer to automatically and safely operate a motor vehicle without any active operation by a human.

[0004] In the process of path planning for existing unmanned vehicles, path planning is usually carried out based on the set initial position and end position. During the planning process, the operating state of the vehicle and the vehicle operating environment are not comprehensively considered, resulting in certain limitations in path planning. Summary of the Invention

[0005] The purpose of the present invention is to provide an unmanned vehicle path planning method based on three-dimensional modeling. During the path planning process of the unmanned vehicle, the operating state of the unmanned vehicle is mapped to the cruise road condition operation base number. During the path planning process, the cruise road condition is selected according to the operating state of the unmanned vehicle, so that an unmanned vehicle with a poor operating state can plan a path with a good cruise road condition operation base number, thereby effectively ensuring the running stability of the unmanned vehicle along the planned path.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An unmanned vehicle path planning method based on three-dimensional modeling, comprising:

[0008] Step 1: The control terminal constructs multiple predetermined navigation paths for the unmanned vehicle based on the target position, and the control terminal sends a motion instruction to the unmanned vehicle based on the predetermined navigation path;

[0009] Step 2: The unmanned vehicle terminal receives the motion instruction and the predetermined navigation path from the control terminal, and the unmanned vehicle plans the virtual parameters of the predetermined navigation path based on the unmanned vehicle state monitoring and the unmanned vehicle road condition monitoring, thereby realizing the real-time correction of the predetermined navigation path by the unmanned vehicle terminal.

[0010] As a further solution of the present invention: In step 2, the acquisition process of the unmanned vehicle state monitoring is as follows:

[0011] Obtain the remaining battery level of the driverless vehicle, the usage time of the driverless vehicle, and the stability maintenance rate of the driverless vehicle;

[0012] Mark the remaining battery level of the driverless vehicle as D i; mark the usage time of the driverless vehicle as M i; mark the stability maintenance rate of the driverless vehicle as Wi;

[0013] Through the formula Calculate the operating state index Rc of the driverless vehicle, where d1, d2, and d3 are preset proportionality coefficients.

[0014] As a further solution of the present invention: the stability maintenance rate Wi of the driverless vehicle is the ratio of the sum of the maintenance times of the driverless vehicle to the sum of the failure times of the driverless vehicle.

[0015] As a further solution of the present invention: in step two, the process of monitoring the road conditions of the driverless vehicle is as follows:

[0016] Obtain the load of the driverless vehicle, the road condition base number of the path of the driverless vehicle, and the environmental base number of the area where the driverless vehicle travels;

[0017] Mark the load of the driverless vehicle as P i; mark the road condition base number of the path of the driverless vehicle as L i; mark the environmental base number of the area where the driverless vehicle travels as H i;

[0018] Perform weight assignment on the load of the driverless vehicle, the road condition base number of the path of the driverless vehicle, and the environmental base number of the area where the driverless vehicle travels, and mark the load weight of the driverless vehicle as a1, the road condition base number of the path of the driverless vehicle as a2, and the environmental base number of the area where the driverless vehicle travels as a3, where a1 + a2 + a3 = 1;

[0019] Calculate the cruising road condition operation base number Va through the formula Va = P i * a1 + L i * a2 + H i * a3, and preset the limit values of the current cruising road condition operation base number of the driverless vehicle as Va1 and Va2, where Va1 < Va2:

[0020] When Va < Va1, it means that the cruising path road condition safety of the driverless vehicle is high, and a high cruising path safety coefficient signal is generated;

[0021] When Va1 < Va < Va2, it means that the cruising path road condition safety of the driverless vehicle is medium, and a medium cruising path safety coefficient signal is generated;

[0022] When Va > Va2, it means that the cruising path road condition safety of the driverless vehicle is low, and a low cruising path safety coefficient signal is generated.

[0023] As a further solution of the present invention: preset the limit values of the current operating state index of the driverless vehicle as Rc1 and Rc2, where Rc1 < Rc2:

[0024] When Rc < Rc1, the operating state of the driverless vehicle is poor, and a signal indicating poor operating state of the driverless vehicle is obtained;

[0025] When Rc1 < Rc < Rc2, the operating state of the driverless vehicle is good, and a signal indicating good operating state of the driverless vehicle is obtained;

[0026] When Rc > Rc2, the operating state of the driverless vehicle is excellent, and a signal indicating excellent operating state of the driverless vehicle is obtained;

[0027] Map the operating state signal of the driverless vehicle and the safety factor signal of the cruise path of the driverless vehicle.

[0028] As a further solution of the present invention: When the signal indicating excellent operating state of the driverless vehicle is obtained, the cruise paths corresponding to the signals of high safety factor of the cruise path, medium safety factor of the cruise path, and low safety factor of the cruise path can be mapped simultaneously.

[0029] As a further solution of the present invention: When the signal indicating good operating state of the driverless vehicle is obtained, the control terminal can map the cruise paths corresponding to the signals of high safety factor of the cruise path and medium safety factor of the cruise path simultaneously.

[0030] As a further solution of the present invention: When the signal indicating poor operating state of the driverless vehicle is obtained, the control terminal can map the cruise path corresponding to the signal of high safety factor of the cruise path.

[0031] As a further solution of the present invention: The environmental base number of the travel area of the driverless vehicle is obtained by comprehensively calculating the temperature, humidity, and wind force in the travel area of the driverless vehicle.

[0032] As a further solution of the present invention: The road condition base number of the path of the driverless vehicle is the average value of the slope ratios of all single paths in the planned path.

[0033] Advantages of the present invention:

[0034] (1) In the path planning process of the driverless vehicle of the present invention, the operating state of the driverless vehicle is mapped to the cruise road condition operation base number. During the path planning process, the cruise road condition is selected according to the operating state of the driverless vehicle, so that the driverless vehicle with poor operating state can plan a path with good cruise road condition operation base number, thereby effectively ensuring the running stability of the driverless vehicle along the planned path;

[0035] (2) In the process of obtaining the operating state of the driverless vehicle of the present invention, the remaining battery power of the driverless vehicle, the usage time of the driverless vehicle, and the stability maintenance rate of the driverless vehicle are combined, so that the control terminal can identify the operating state of the driverless vehicle, and play an effective pre-reminder role for the management and maintenance of the driverless vehicle by the management personnel;

[0036] (3) In the process of obtaining the cruise road condition operation base of the present invention, the load of the driverless vehicle, the road condition base of the driverless vehicle path, and the environmental base of the driverless vehicle traveling area are combined, so that the coincidence degree between the cruise road condition operation base simulated by the driverless vehicle and the actual driving trajectory is higher, and the accuracy of the path planning of the driverless vehicle is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings.

[0038] Figure 1 It is a schematic structural diagram of the flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Please refer to Figure 1 As shown, the present invention is a method for path planning of a driverless vehicle based on three-dimensional modeling, including:

[0041] Step 1: The control terminal constructs multiple predetermined navigation paths for the driverless vehicle based on the target position, and the control terminal sends a motion instruction to the driverless vehicle based on the predetermined navigation path;

[0042] Step 2: The driverless vehicle terminal receives the motion instruction and the predetermined navigation path from the control terminal, and the driverless vehicle virtual parameter-plans the predetermined navigation path based on the driverless vehicle state monitoring and the driverless vehicle road condition monitoring, so as to realize the real-time correction of the predetermined navigation path at the driverless vehicle terminal.

[0043] The path cruise process of the driverless vehicle terminal is divided into driverless vehicle state monitoring and driverless vehicle road condition monitoring:

[0044] The driverless vehicle state monitoring process is as follows:

[0045] W1: Obtain the remaining power of the driverless vehicle, the usage time of the driverless vehicle, and the stability rate of the driverless vehicle;

[0046] W2: Mark the remaining power of the driverless vehicle as Di; mark the usage time of the driverless vehicle as Mi; mark the stability rate of the driverless vehicle as Wi;

[0047] W3: Through the formula Calculate the driverless vehicle operation state index Rc, where d1, d2, and d3 are preset proportional coefficients;

[0048] W4: Preset the limit values of the current operating state index of the driverless vehicle as Rc1 and Rc2, where Rc1 < Rc2:

[0049] When Rc < Rc1, the operating state of the driverless vehicle is poor, and a signal indicating poor operating state of the driverless vehicle is obtained;

[0050] When Rc1 < Rc < Rc2, the operating state of the driverless vehicle is good, and a signal indicating good operating state of the driverless vehicle;

[0051] When Rc > Rc2, the operating state of the driverless vehicle is excellent, and a signal indicating excellent operating state of the driverless vehicle is obtained;

[0052] Among them, the stability maintenance rate Wi of the driverless vehicle is the ratio of the sum of the maintenance times of the driverless vehicle to the sum of the failure times of the driverless vehicle.

[0053] The road condition monitoring process of the driverless vehicle is as follows:

[0054] V1: Obtain the load of the driverless vehicle, the road condition base number of the driverless vehicle path, and the environmental base number of the area where the driverless vehicle travels;

[0055] V2: Mark the load of the driverless vehicle as Pi; mark the road condition base number of the driverless vehicle path as Li; mark the environmental base number of the area where the driverless vehicle travels as Hi;

[0056] V3: Assign weights to the load of the driverless vehicle, the road condition base number of the driverless vehicle path, and the environmental base number of the area where the driverless vehicle travels. Mark the weight of the load of the driverless vehicle as a1, the road condition base number of the driverless vehicle path as a2, and the environmental base number of the area where the driverless vehicle travels as a3, where a1 + a2 + a3 = 1;

[0057] V4: Calculate the cruise road condition operation base number Va through the formula Va = Pi * a1 + Li * a2 + Hi * a3. Preset the limit values of the current cruise road condition operation base number of the driverless vehicle as Va1 and Va2, where Va1 < Va2:

[0058] When Va < Va1, it indicates that the road condition safety of the cruise path of the driverless vehicle is high, and a signal with a high cruise path safety coefficient is generated;

[0059] When Va1 < Va < Va2, it indicates that the road condition safety of the cruise path of the driverless vehicle is medium, and a signal with a medium cruise path safety coefficient is generated;

[0060] When Va > Va2, it indicates that the road condition safety of the cruise path of the driverless vehicle is low, and a signal with a low cruise path safety coefficient is generated.

[0061] Among them, the process of obtaining the environmental base number Hi of the area where the driverless vehicle travels is as follows:

[0062] Monitor the environment of the unmanned vehicle driving area. The environmental monitoring module is implemented by multiple temperature monitoring units, humidity monitoring units, and wind monitoring units. The temperature monitoring unit includes several first temperature monitoring nodes, the humidity monitoring unit includes several first humidity monitoring nodes, and the wind monitoring unit includes several wind monitoring nodes;

[0063] Obtain the temperature value by real-time monitoring of the monitoring area through the first temperature monitoring node, and mark it as T i , i = 1, 2, ……, n; where i is the number of the first temperature monitoring nodes; Obtain the humidity value by real-time monitoring of the monitoring area through the first humidity monitoring node, and mark it as Y j , j = 1, 2, ……, m; where m is the number of the first humidity monitoring nodes; Obtain the wind force value by real-time monitoring of the monitoring area through the wind monitoring node, and mark it as U k , k = 1, 2, ……, l; where k is the number of wind monitoring nodes;

[0064] Obtain the environmental base number Hi of the unmanned vehicle traveling area through the formula .

[0065] Among them, the road condition base number Li of the unmanned vehicle path is the average value of all slope ratios of a single path in the planned path.

[0066] The path planning at the unmanned vehicle end maps the unmanned vehicle status monitoring and the unmanned vehicle road condition monitoring;

[0067] When the control terminal obtains the signal of excellent unmanned vehicle operation state, the control terminal can simultaneously map the cruise paths corresponding to the signals of high cruise path safety factor, medium cruise path safety factor, and low cruise path safety factor, and complete the unmanned vehicle path planning;

[0068] When the control terminal obtains the signal of good unmanned vehicle operation state, the control terminal can simultaneously map the cruise paths corresponding to the signals of high cruise path safety factor and medium cruise path safety factor, and complete the unmanned vehicle path planning;

[0069] When the control terminal obtains the signal of poor unmanned vehicle operation state, the control terminal can map the cruise path corresponding to the signal of high cruise path safety factor, and complete the unmanned vehicle path planning.

[0070] Among them, the construction method of the unmanned vehicle corrected navigation path is as follows:

[0071] S1: The control terminal accesses the unmanned vehicle operation area, and draws an electronic map according to the scene of the operation area to construct a map module;

[0072] S2: According to the map module, the control terminal accesses the GPS positioning module for the unmanned vehicle to set the initial position of the corrected navigation path;

[0073] S3: Based on the map module and supported by the end position as the position information, construct the corrected navigation path end position coordinates, and construct a path slope ratio base between the initial position of the corrected navigation path and the corrected navigation path end position, thereby obtaining the predetermined navigation path of the driverless vehicle.

[0074] The above has described in detail an embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the patent coverage scope of the present invention.

Claims

1. An unmanned vehicle path planning method based on three-dimensional modeling, characterized in that: Including: Step 1: The control terminal constructs multiple predetermined navigation paths for the driverless vehicle based on the target location, and the control terminal sends motion instructions to the driverless vehicle based on the predetermined navigation paths; Step 2: The driverless vehicle terminal receives the motion instructions and the predetermined navigation paths from the control terminal, and the driverless vehicle virtual parameter plans the predetermined navigation paths based on the driverless vehicle status monitoring and the driverless vehicle road condition monitoring, so as to realize the real-time correction of the predetermined navigation paths at the driverless vehicle terminal.

2. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 1 is characterized in that: In Step 2, the acquisition process of the driverless vehicle status monitoring is as follows: Obtain the remaining battery power of the driverless vehicle, the usage time of the driverless vehicle, and the stability maintenance rate of the driverless vehicle; Mark the remaining battery power of the driverless vehicle as Di; mark the usage time of the driverless vehicle as Mi; mark the stability maintenance rate of the driverless vehicle as Wi; By formula The unmanned vehicle operation status index Rc is calculated, where d1, d2 and d3 are preset proportional coefficients.

3. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 2 is characterized in that: The stability maintenance rate Wi of the driverless vehicle is the ratio of the sum of the maintenance times of the driverless vehicle to the sum of the failure times of the driverless vehicle.

4. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 3 is characterized in that: In Step 2, the driverless vehicle road condition monitoring process is as follows: Obtain the load of the driverless vehicle, the road condition base number of the driverless vehicle path, and the environmental base number of the driverless vehicle traveling area; Mark the load of the driverless vehicle as Pi; mark the road condition base number of the driverless vehicle path as Li; mark the environmental base number of the driverless vehicle traveling area as Hi; Perform weight allocation on the load of the driverless vehicle, the road condition base number of the driverless vehicle path, and the environmental base number of the driverless vehicle traveling area, and mark the weight of the load of the driverless vehicle as a1, the road condition base number of the driverless vehicle path as a2, and the environmental base number of the driverless vehicle traveling area as a3, where a1 + a2 + a3 = 1; Calculate the cruise road condition operation base number Va through the formula Va = Pi * a1 + Li * a2 + Hi * a3, and preset the limit values of the current cruise road condition operation base number of the driverless vehicle as Va1 and Va2, where Va1 < Va2: When Va < Va1, it means that the cruise path road condition safety of the driverless vehicle is high, and a high cruise path safety coefficient signal is generated; When Va1 < Va < Va2, it means that the cruise path road condition safety of the driverless vehicle is medium, and a medium cruise path safety coefficient signal is generated; When Va > Va2, it means that the cruise path road condition safety of the driverless vehicle is low, and a low cruise path safety coefficient signal is generated.

5. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 4 is characterized in that: Preset the limit values of the current driverless vehicle operation state index as Rc1 and Rc2, where Rc1 < Rc2: When Rc < Rc1, the operation state of the driverless vehicle is poor, and a poor driverless vehicle operation state signal is obtained; When Rc1 < Rc < Rc2, the operation state of the driverless vehicle is good, and a good driverless vehicle operation state signal; When Rc > Rc2, the operation state of the driverless vehicle is excellent, and an excellent driverless vehicle operation state signal is obtained; Map the driverless vehicle operation state signal and the driverless vehicle cruise path safety coefficient signal.

6. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 5 is characterized in that: When the excellent driverless vehicle operation state signal is obtained, it can simultaneously map the cruise paths corresponding to the high cruise path safety coefficient signal, the medium cruise path safety coefficient signal, and the low cruise path safety coefficient signal.

7. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 5 is characterized in that: When the good driverless vehicle operation state signal is obtained, the control terminal can simultaneously map the cruise paths corresponding to the high cruise path safety coefficient signal and the medium cruise path safety coefficient signal.

8. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 5 is characterized in that: When the poor driverless vehicle operation state signal is obtained, the control terminal can map the cruise path corresponding to the high cruise path safety coefficient signal.

9. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 4 is characterized in that: The environmental base of the unmanned vehicle traveling area is obtained by comprehensive calculation of the temperature, humidity and wind force of the unmanned vehicle traveling area.

10. The unmanned vehicle path planning method based on three-dimensional modeling according to claim 4 is characterized in that: The road condition cardinality of the unmanned vehicle path is the average of all slope ratios of a single path in the planned path.

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