New energy vehicle rescue path planning method based on fusion of A* algorithm and DWA algorithm

Abstract

The invention provides a new energy vehicle rescue path planning method based on fusion of an A * algorithm and a Dynamic Window Approach (dynamic window method) algorithm, and a reasonable management method and strategy are provided for new energy vehicle rescue. The method comprises the following steps: acquiring information (including position, electric quantity, weather information of the location and the like) of a to-be-rescued vehicle, and processing and analyzing the data information; then the distance between the new energy vehicle needing to be rescued is judged, and a new energy rescue vehicle or a fuel vehicle is determined to be used; determining whether the vehicle to be rescued is a vehicle to be rescued emergently, and if yes, preferentially considering the vehicle to be rescued emergently; a proposed A * algorithm and a DWA algorithm are adopted as planning algorithms of a rescue path, and dynamic simulation verification is carried out on a grid map. Experimental results show that the method can effectively achieve self-adaption to the target path, effectively improves the safety threshold value, and further provides a reasonable management method and strategy for new energy vehicle rescue.

Description

Technical Field

[0001] The present invention relates to a new energy vehicle with replaceable batteries. The data of new energy vehicles for road rescue on the road are collected and processed by a big data platform, and the optimal traffic path planning is performed under different conditions. A method for estimating the optimal driving path for road rescue is provided, specifically, a path planning method based on the A* algorithm and the DWA algorithm. Background Art

[0002] In today's rapidly developing urbanization process, the power of new energy vehicles mainly comes from car batteries, but the current level of battery technology restricts the rapid development of new energy vehicles. New energy vehicles may run out of power while driving and need rescue. Emergency rescue vehicles face increasingly complex traffic environments when performing their tasks. How to quickly and safely reach the rescue site on congested urban arterial roads is the key to improving rescue efficiency and reducing accident losses. However, existing path planning methods often have limitations, especially in dealing with dynamic obstacles and real-time traffic conditions. The path planning method that integrates the A* algorithm and the DWA algorithm can effectively achieve adaptation to the target path, effectively improve the safety threshold, and thus provide reasonable management methods and strategies for road rescue. Summary of the invention

[0003] The present invention proposes a path planning method for rescuing new energy vehicles based on the A* algorithm and the DWA algorithm, which can provide a more efficient and reliable path planning strategy for rescuing new energy vehicles.

[0004] The present invention is implemented as follows: a new energy vehicle rescue path planning method based on the A* algorithm and the DWA algorithm, the method comprising the following steps:

[0005] S1: Obtain information about the new energy vehicle that needs to be rescued (including location, power, weather information of the location, etc.), use big data processing technology to process and analyze the data information, and determine the remaining power and location of the new energy vehicle that needs to be rescued;

[0006] S2: Based on data information processing, determine the distance to the new energy vehicle that needs to be rescued, and determine which rescue vehicle to send. If the distance is beyond the reach of the new energy rescue vehicle and cannot return, use a fuel vehicle, otherwise use a new energy rescue vehicle;

[0007] S3: Determine whether the new energy vehicle to be rescued is a vehicle that needs emergency rescue. If so, give priority to the vehicle that needs emergency rescue;

[0008] S4: Use the grid map to build an environment model that matches the actual road environment, and use the A* algorithm and DWA algorithm as the rescue path planning algorithm;

[0009] S5: Perform dynamic simulation verification on the grid map, analyze and compare data, and determine the optimal driving path;

[0010] Furthermore, when S1 obtains and processes the data information of the new energy vehicle that needs to be rescued, it focuses on extracting the location information, the remaining power status, the type of battery used, and the road conditions to the destination of the new energy vehicle.

[0011] Furthermore, the step S4 uses the grid map to construct an environmental model that matches the actual road environment, and uses the A* algorithm and the DWA algorithm as the rescue path planning algorithm, including: constructing a grid map and an environmental model that matches the actual road environment, and performing dynamic experimental simulation; using the A* algorithm and the DWA algorithm as the rescue path planning algorithm includes: using the A* algorithm for global path planning to estimate the minimum cost from the current node to the target node; using the DWA algorithm to perform local obstacle avoidance optimization on the path nodes generated by the A* algorithm, reducing the path turns and increasing the path smoothness. The steps of using the grid map to construct an environmental model that matches the actual road environment are as follows:

[0012] S21: Build a grid map, including defining the size and resolution of the map, creating an initial empty map, and generating a 20m×20m map;

[0013] S22: Set the starting point position: the starting point is a blue circle and the target end point is a green circle; S23: Define obstacles on the generated grid map, the yellow grid is a dynamic unknown obstacle, the red straight line is the path of the dynamic obstacle, and the gray grid is a static unknown obstacle; S24: Use the A* algorithm to find the preliminary path of the rescue vehicle from the starting point to the target point, and based on the global path, use the DWA algorithm to perform dynamic path planning in the local range.

[0014] Furthermore, when the DWA algorithm is used to avoid obstacles on a local path in step S24, dynamic factors such as the current speed and acceleration of the vehicle need to be considered. The vehicle speed is composed of linear speed (v) and angular speed (w), which constitute the speed space. The speed sampling space constraints of the vehicle are as follows:

[0015] S31: Speed ​​limit:

[0016] V m ={(v,m)|v∈[v min ,v max ],ω∈[ω min ,ω max ]}

[0017] In the above formula, v max is the maximum linear velocity of the vehicle, v minis the minimum linear velocity, ω max is the maximum angular velocity of the vehicle, ω min is the minimum angular velocity of the vehicle.

[0018] S32: Acceleration constraint:

[0019]

[0020] The above formula v c ,ω c They represent the linear velocity and angular velocity of the vehicle at that time point, respectively. v max 、a w max Indicates the maximum linear acceleration and maximum angular acceleration of the vehicle.

[0021] S33: Environmental obstacle constraints:

[0022]

[0023] The above formula dist(v,ω) is the distance from the simulation path corresponding to the current moment to the current vehicle speed. In the absence of obstacles, dist(v,ω) will be a large constant value.

[0024] S34: Combining the above three different speed constraints, the speed sampling space obtained is the intersection point of the three speed intervals, that is, V s =V m ∩V d ∩V a ;

[0025] Furthermore, in step S34, given the speed sampling space V s After that, trajectory analysis is required. w 、E v Respectively represent the sampling resolution, and the trajectory analysis of the rescue vehicle is as follows:

[0026] S41: Trajectory Prediction:

[0027] n=[(v h -v l / E v )]*[(w h -w l ) / E w ]

[0028] v h 、v l 、w h 、w l Refers to the upper and lower limits of speed and space. The above formula shows that the linear velocity selects the corresponding value according to a certain amplitude, and the angular velocity selects the corresponding value according to a certain amplitude, thus forming a set of rotation speeds.

[0029] S42: Trajectory prediction process:

[0030] x k =x k-1 +v*cos(θ k-1 )Δt

[0031] y k =y k-1 +v*sin(θ k-1 )Δt

[0032] θ k =θ k-1 +ωΔt

[0033] In the above formula, (x, y, θ) refers to the coordinates of the vehicle, k refers to the sampling time, and Δt refers to the sampling interval.

[0034] S43: Trajectory evaluation:

[0035] G(v,ω)=σ(α*heading(v,ω))+σ(β*dist(v,ω))+σ(γ*velocity(v,ω))heading(v,ω) is the heading evaluation function, which enables the vehicle to continuously align with the next reference point. It is the error Δθ that evaluates the angle between the heading of the tracking end position generated at the current sampling rate and the target point line. The larger the evaluation function, the better. Therefore, π-Δθ is used for evaluation. That is,

[0036] heading(v,ω)=π-Δθ

[0037] dist(v,ω) is a distance evaluation function that describes the maximum distance of obstacles under the corresponding simulation trajectory and the current vehicle speed. If there is no obstacle, or the closest distance exceeds the set critical value, the value is set to a larger constant.

[0038] velocity(v,ω) is a velocity evaluation function that represents the current velocity amplitude and can be expressed by the amplitude of the current linear velocity. A larger value indicates faster operation on the planned track and a higher evaluation score.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention uses big data processing technology to accurately receive information requesting help from owners of new energy vehicles, and combines the A* algorithm with the DWA algorithm to obtain the optimal path to the destination in emergency rescue situations, or obtain a local optimal solution in different situations such as multiple rescue vehicles or complex traffic planning routes. It effectively saves the energy that will be consumed by rescue vehicles in multiple vehicle situations, and saves the waiting time of trapped car owners. At the same time, it provides car owners with a solution to the charging problem, which not only saves time and energy to meet the car owners' demand for vehicle charging, but will also further promote the development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of using big data processing technology to accurately receive assistance information from new energy vehicle owners and perform route planning as described in the present invention;

[0042] Figure 2 This is a flow chart of the fusion algorithm of the present invention;

[0043] Figure 3 A flowchart of the present invention for obtaining information of a vehicle requiring rescue, determining whether first aid is required, and performing route planning;

[0044] Figure 4 A 20m×20m grid map constructed according to the present invention;

[0045] Figure 5 Using the grid map, an environment model map is constructed to match the actual road environment;

[0046] Figure 6 This is a graph of path planning for the fusion algorithm of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] See also Figure 1 As shown, a new energy vehicle rescue path planning method using an A* algorithm and a DWA algorithm is characterized in that the method comprises the following steps:

[0049] S1: Obtain information about the new energy vehicle that needs to be rescued (including location, power, weather information of the location, etc.), use big data processing technology to process and analyze the data information, and determine the remaining power and location of the new energy vehicle that needs to be rescued;

[0050] S2: Based on data information processing, determine the distance to the new energy vehicle that needs to be rescued, and determine which rescue vehicle to send. If the distance is beyond the reach of the new energy rescue vehicle and cannot return, use a fuel vehicle, otherwise use a new energy rescue vehicle;

[0051] S3: Determine whether the new energy vehicle to be rescued is a vehicle that needs emergency rescue. If so, give priority to the vehicle that needs emergency rescue;

[0052] S4: Use the grid map to build an environment model that matches the actual road environment, and use the A* algorithm and DWA algorithm as the rescue path planning algorithm;

[0053] S5: Perform dynamic simulation verification on the grid map, analyze and compare data, and determine the optimal driving path.

[0054] In step S1, when acquiring and processing the data information of the new energy vehicle that needs to be rescued, the location information, the remaining power status, the type of battery used, and the road conditions to the destination of the new energy vehicle are extracted.

[0055] In step S4, an environment model matching the actual road environment is constructed to generate a raster map, including defining the size and resolution of the map and building an initial empty map.

[0056] Furthermore, in step S4, the grid map is used to construct an environmental model that matches the actual road environment, and the A* algorithm and the DWA algorithm are used as the rescue path planning algorithm, including: constructing a grid map and an environmental model that matches the actual road environment, and performing dynamic experimental simulation; the A* algorithm and the DWA algorithm are used as the rescue path planning algorithm, including: using the A* algorithm for global path planning to estimate the minimum cost from the current node to the target node; using the DWA algorithm to perform local obstacle avoidance optimization on the path nodes generated by the A* algorithm, reducing the path turns and increasing the path smoothness. The steps of using the grid map to construct an environmental model that matches the actual road environment are as follows:

[0057] S21: Build a grid map, including defining the size and resolution of the map, and generate a 20m×20m map by creating an initial empty map;

[0058] S22: Set the starting point position: the starting point is a blue circle, and the target end point is a green circle;

[0059] S23: Define obstacles on the generated grid map. The yellow grid is a dynamic unknown obstacle, the red straight line is the path of the dynamic obstacle, and the gray grid is a static unknown obstacle.

[0060] S24: Use the A* algorithm to find the preliminary path of the rescue vehicle from the starting point to the target point, and use the DWA algorithm to perform dynamic path planning in the local range based on the global path.

[0061] Furthermore, when the DWA algorithm is used to avoid obstacles on a local path in step S24, dynamic factors such as the current speed and acceleration of the vehicle need to be considered. The vehicle speed is composed of linear speed (v) and angular speed (w), which constitute the speed space. The speed sampling space constraints of the vehicle are as follows:

[0062] S31: Speed ​​limit:

[0063] V m ={(v,m)|v∈[v min ,v max ],ω∈[ω min ,ω max ]}

[0064] In the above formula, v max is the maximum linear velocity of the vehicle, v min is the minimum linear velocity, ω max is the maximum angular velocity of the vehicle, ω min is the minimum angular velocity of the vehicle.

[0065] S32: Acceleration constraint:

[0066]

[0067] The above formula v c 、w c They represent the linear velocity and angular velocity of the vehicle at that time point, respectively. v max 、a w max Indicates the maximum linear acceleration and maximum angular acceleration of the vehicle.

[0068] S33: Environmental obstacle constraints:

[0069]

[0070] The above formula dist(v,ω) is the distance from the simulation path corresponding to the current moment to the current vehicle speed. In the absence of obstacles, dist(v,ω) will be a large constant value.

[0071] S34: Combining the above three different speed constraints, the speed sampling space obtained is the intersection point of the three speed intervals, that is, V s =V m ∩V d ∩V a ;

[0072] Furthermore, in step S34, given the speed sampling space Vs After that, trajectory analysis is required. w 、E v Respectively represent the sampling resolution, and the trajectory analysis of the rescue vehicle is as follows:

[0073] S41: Trajectory prediction:

[0074] n=[(v h -v l / E v )]*[(w h -w l ) / E w ]

[0075] v h 、v l 、w h 、w l Refers to the upper and lower limits of speed and space. The above formula shows that the linear velocity selects the corresponding value according to a certain amplitude, and the angular velocity selects the corresponding value according to a certain amplitude, thus forming a set of rotation speeds.

[0076] S42: Trajectory prediction process:

[0077] x k =x k-1 +v*cos(θ k-1 )Δt

[0078] y k =y k-1 +v*sin(θ k-1 )Δt

[0079] θ k =θ k-1 +ωΔt

[0080] In the above formula, (x, y, θ) refers to the coordinates of the vehicle, k refers to the sampling time, and Δt refers to the sampling interval.

[0081] S43: Trajectory Evaluation:

[0082] G(v,ω)=σ(α*heading(v,ω))+σ(β*dist(v,ω))+σ(γ*velocity(v,ω))

[0083] heading(v,ω) is the azimuth evaluation function, which enables the vehicle to continuously align with the next reference point. It is the error Δθ that evaluates the angle between the azimuth of the tracking end position generated at the current sampling rate and the target point line. The larger the evaluation function, the better. Therefore, π-Δθ is used for evaluation. That is,

[0084] heading(v,ω)=π-Δθ

[0085] dist(v,ω) is a distance evaluation function that describes the maximum distance of obstacles under the corresponding simulation trajectory and the current vehicle speed. If there is no obstacle, or the closest distance exceeds the set critical value, the value is set to a larger constant.

[0086] velocity(v,ω) is a velocity evaluation function that represents the current velocity amplitude and can be expressed by the amplitude of the current linear velocity. A larger value indicates faster operation on the planned track and a higher evaluation score.

[0087] The present invention uses big data processing technology to accurately receive information seeking help from owners of new energy vehicles, and combines the use of the A* algorithm and the DWA algorithm to obtain the optimal path to the destination in emergency rescue situations, effectively saving the energy that the rescue vehicles will consume when moving around in multiple vehicles, and saving the waiting time of trapped car owners. At the same time, it provides car owners with a solution to the charging problem, which not only saves time and energy to meet the car owners' needs for vehicle charging, but also will further promote the development of new energy vehicles. Experiments show that the path planning method based on the A* algorithm and the DWA algorithm proposed in the present invention, under the path where the initial point and the target point remain unchanged, simultaneously improves the average path increase and the average time increase, which improves the driving safety of the vehicle to a certain extent.

[0088] The present invention is further described in detail below in conjunction with examples.

[0089] Step 1: Obtain information about the vehicle that needs rescue (including location, power level, local weather information, etc.), and process and analyze the data information. This can be obtained through platforms such as Amap or Baidu Map.

[0090] Step 2: Use the proposed A* algorithm and DWA algorithm as the rescue path planning algorithm, and perform dynamic simulation verification on the grid map.

[0091] The steps of using the grid map to construct an environment model that matches the actual road environment and perform dynamic experimental simulation are as follows:

[0092] S21: Create an initial empty map by generating a raster map, including defining the size and resolution of the map, and generating a 20m×20m map;

[0093] S22: Set the starting point position: the starting point is a blue circle, and the target end point is a green circle;

[0094] S23: Define obstacles on the generated grid map. The yellow grid is a dynamic unknown obstacle, the red straight line is the path of the dynamic obstacle, and the gray grid is a static unknown obstacle.

[0095] S24: Use the A* algorithm to find the preliminary path of the rescue vehicle from the starting point to the target point, and use the DWA algorithm to perform dynamic path planning in the local range based on the global path.

[0096] Figure 4 This is a 20m×20m grid map constructed according to the present invention. Figure 5 The present invention uses a grid map to construct an environmental model map that matches the actual road environment. In the generated map, black squares represent the locations of obstacles, yellow grids represent dynamic unknown obstacles, gray grids represent static unknown obstacles, and red straight lines represent the paths of dynamic obstacles. Figure 6 The figure shows the path planning of the fusion algorithm of the present invention. The blue dotted line is the global planning path of the A* algorithm, and the blue solid line is the path generated by the fusion of the A* and DWA algorithms. At this time, the distance of the global planning path of the A* algorithm is 27.94m, and the time is 104.45s; the distance planned by the fusion algorithm is 29.38m, and the time is 112.17s. The average path has increased by 0.52%, and the average time has increased by 0.74%. It can be seen that the path planning method based on the A* algorithm and the DWA algorithm of the present invention has improved the driving safety of the vehicle to a certain extent, and provided a reasonable management method and strategy for road rescue.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention.

Claims

1. A new energy vehicle rescue path planning method based on the integration of A* and DWA algorithms, characterized in that: The method comprises the following steps: S1: Obtain information about the new energy vehicle that needs to be rescued (including location, power, weather information of the location, etc.), use big data processing technology to process and analyze the data information, and determine the remaining power and location of the new energy vehicle that needs to be rescued; S2: Based on data information processing, determine the distance to the new energy vehicle that needs to be rescued, and determine to dispatch a rescue vehicle. If the distance is beyond the reach of the new energy rescue vehicle and cannot be returned, a fuel vehicle is selected, otherwise a new energy rescue vehicle is selected; S3: Determine whether the new energy vehicle to be rescued is a vehicle that needs emergency rescue. If so, give priority to the vehicle that needs emergency rescue; S4: Use the grid map to build an environment model that matches the actual road environment, and use the A* algorithm and DWA algorithm as the rescue path planning algorithm; S5: Perform dynamic simulation verification on the grid map, analyze and compare data, and determine the optimal driving path.

2. The method according to claim 1, characterized in that When acquiring and processing the data information of the new energy vehicle that needs to be rescued, step S1 focuses on extracting the location information, remaining power status, battery type used, and road conditions to the destination of the new energy vehicle.

3. The method according to claim 1, characterized in that The step S4 uses the grid map to construct an environmental model that matches the actual road environment, and uses the A* algorithm and the DWA algorithm as the rescue path planning algorithm, including: constructing a grid map and an environmental model that matches the actual road environment, and performing dynamic experimental simulation; using the A* algorithm and the DWA algorithm as the rescue path planning algorithm includes: using the A* algorithm for global path planning to estimate the minimum cost from the current node to the target node; using the DWA algorithm to perform local obstacle avoidance optimization on the path nodes generated by the A* algorithm, reducing the path turns and increasing the path smoothness. The steps of using the grid map to construct an environmental model that matches the actual road environment and performing dynamic experimental simulation are as follows: S21: Build a grid map, including defining the size and resolution of the map, creating an initial empty map, and generating a 20m×20m map; S22: Set the starting point position: the starting point is a blue circle, and the target end point is a green circle; S23: Define obstacles on the generated grid map. The yellow grid is a dynamic unknown obstacle, the red straight line is the path of the dynamic obstacle, and the gray grid is a static unknown obstacle. S24: Use the A* algorithm to find the preliminary path of the rescue vehicle from the starting point to the target point, and use the DWA algorithm to perform dynamic path planning in the local range based on the global path.

4. The method according to claim 3, characterized in that: When using the DWA algorithm to avoid obstacles on a local path in step S24, dynamic factors such as the current speed and acceleration of the vehicle need to be considered. The vehicle speed is composed of linear speed (v) and angular speed (w), which constitute the speed space. The speed sampling space constraints of the vehicle are as follows: S31: Speed ​​limit: V m ={(v,w)|v∈[v min ,v max ],ω∈[ω min ,ω max ]} Where ω max is the maximum linear speed of the vehicle, ω min is the minimum linear velocity, w max is the maximum angular velocity of the vehicle, w min is the minimum angular velocity of the vehicle. S32: Acceleration constraint: The above formula v c 、w c They represent the linear velocity and angular velocity of the vehicle at that time point, respectively. vmax 、a wmax Indicates the maximum linear acceleration and maximum angular acceleration of the vehicle. S33: Environmental obstacle constraints: The above formula dist(v,ω) is the distance from the simulation path at the current moment to the current vehicle speed. In the absence of obstacles, dist(v,ω) will be a large constant value. S34: Combining the above three different speed constraints, the speed sampling space obtained is the intersection point of the three speed intervals, that is, V s =V m ∩V d ∩V a .

5. The method according to claim 4, characterized in that: In step S34, given a speed sampling space V s After that, trajectory analysis is required. w 、E v Respectively represent the sampling resolution, and the trajectory analysis of the rescue vehicle is as follows: S41: Trajectory prediction: n[(v h -v l / E v )]*[(w h -w l ) / E w ] v h 、v l 、w h 、w l Refers to the upper and lower limits of speed and space. The above formula shows that the linear velocity selects the corresponding value according to a certain amplitude, and the angular velocity selects the corresponding value according to a certain amplitude, thus forming a set of rotation speeds. S42: Trajectory prediction process: x k =x k-1 +v*cos(θ k-1 )Δt and k =and k-1 +v*sin(θ k-1 )Δt i k =θ k-1 +ωΔt In the above formula, (x, y, θ) refers to the coordinates of the vehicle, k refers to the sampling time, and Δt refers to the sampling interval. S43: Trajectory evaluation: G(v,ω)=σ(α*heading(v,ω))+σ(β*dist(v,ω))+σ*(γ*velocity(v,ω)) heading(v,ω) is the azimuth evaluation function, which enables the vehicle to continuously align with the next reference point. It is the error Δθ that evaluates the angle between the azimuth of the tracking end position generated at the current sampling rate and the target point line. The larger the evaluation function, the better. Therefore, π-Δθ is used for evaluation. That is, heading(v,ω)=π-Δθ dist(v,ω) is a distance evaluation function that describes the maximum distance of obstacles under the corresponding simulation trajectory and the current vehicle speed. If there is no obstacle, or the closest distance exceeds the set critical value, the value is set to a larger constant. velocity(v,ω) is a velocity evaluation function that represents the current velocity amplitude and can be expressed by the amplitude of the current linear velocity. A larger value indicates faster operation on the planned track and a higher evaluation score.

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