A mobile energy-consuming equipment energy supplement path planning method

Abstract

The application relates to a mobile energy-consuming equipment energy supplement path planning method, which comprises the following steps: when the mobile energy-consuming equipment detects that the energy thereof is lower than a preset threshold, collecting energy supplement station energy information and map information, wherein the map information comprises position coordinate information of the mobile energy-consuming equipment, position coordinate information of the energy supplement station and obstacle position coordinate information; according to the energy information, the position coordinate information of the mobile energy-consuming equipment, the position coordinate information of the energy supplement station and the obstacle position coordinate information, an optimal path is calculated through a bidirectional path optimization algorithm with the introduction of energy supplement information intensity; and energy supplement navigation is carried out according to the optimal path. Compared with the prior art, the optimal path is calculated through the bidirectional optimization algorithm with the introduction of the energy supplement information intensity, the energy supplement information intensity at different distances is adjusted, the range of path search is controlled, the convergence ability of the algorithm is adjusted, and the result accuracy is high.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method for planning a path for replenishing energy for mobile energy-consuming devices. Background Technology

[0002] Path planning plays an increasingly important role in many modern industries, especially in logistics and industrial material handling. The technologies involved in path planning vary, and most mobile energy-consuming devices require recharging.

[0003] Common path planning algorithms include: Dijkarta algorithm, artificial potential field method, A* algorithm, D* algorithm, PRM algorithm, RRT algorithm, neural network algorithm, ant colony algorithm, and genetic algorithm. In the classic A* algorithm, the structure and parameters of the heuristic function can significantly impact the path search results. During path search, the closer a mobile energy-consuming device is to a refueling station, the lower the estimated cost; consequently, the smaller the weight of the heuristic function within the cost function. To mitigate the weight reduction issue in the A* algorithm, the weight of the heuristic function in the cost function can be increased. However, excessively high weights can lead to a small map exploration range for the mobile energy-consuming device in the early stages of path optimization, causing it to get stuck in local optimization and making it difficult to find the optimal path. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology in finding the optimal path and to provide a method for planning the recharge path of mobile energy-consuming devices.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for planning a recharge path for mobile energy-consuming devices includes the following steps:

[0007] S1: When the mobile energy-consuming device detects that its own energy is lower than a preset threshold, it collects energy information and map information of the power replenishment station. The map information includes the location coordinates of the mobile energy-consuming device, the location coordinates of the power replenishment station, and the location coordinates of obstacles.

[0008] S2: Based on energy information, location coordinates of mobile energy-consuming devices, location coordinates of refueling stations, and location coordinates of obstacles, the optimal path is calculated using a bidirectional path optimization algorithm that incorporates the intensity of refueling information.

[0009] S3: Perform energy replenishment navigation based on the optimal path.

[0010] Furthermore, the map information specifically includes: defining the current position of the mobile energy-consuming device as the origin, establishing a Cartesian coordinate system with the current direction of movement of the mobile energy-consuming device and the horizontal and vertical directions of the current direction of movement, and the current position coordinates of the mobile energy-consuming device, the position coordinates of each energy replenishment station, and the position coordinates of obstacles in this coordinate system.

[0011] Furthermore, step S2 specifically includes:

[0012] S21: Create a first priority list, a second priority list, a first hash list, and a second hash list, and store the current location and the coordinates of each refueling station into the first priority list and the second priority list;

[0013] S22: The starting coordinates of the mobile energy-consuming device and the coordinates of each energy replenishment station are respectively used as the starting nodes of the bidirectional path planning;

[0014] S23: Search for the current location of the mobile energy-consuming device and the extended nodes of the coordinates of each energy replenishment station, and add the current location of the mobile energy-consuming device and the coordinates of each energy replenishment station to the first hash list and the second hash list respectively;

[0015] S24: Add the current location of the mobile energy-consuming device and the extended nodes of each energy replenishment station's coordinates to the first priority list and the second priority list respectively, calculate the cost value using the cost formula, and select the extended node with the lowest cost value.

[0016] S25: When the extended nodes selected in both directions are inconsistent, move the extended node with the smallest value in the forward search process into the first hash list and use this extended node as the initial node for the next forward search. Move the extended node with the smallest value in the reverse search process into the second hash list and use this extended node as the initial node for the next reverse search. Return to S23. When the coordinates of the extended nodes selected in both directions are the same, the optimal path is obtained.

[0017] Furthermore, in step S23, the expanded node is the node with the lowest cost estimate among the adjacent nodes centered on the current position node.

[0018] Furthermore, the power replenishment reminder is an analog signal generated by mobile power-consuming devices.

[0019] Furthermore, in step S2, the expression for the bidirectional path optimization algorithm is:

[0020] f(n)=g(n)+Qh i (n)

[0021] In the formula, f(n) is the cost function of the mobile energy-consuming device reaching the planned end point of the energy replenishment station via any node n from its starting position, g(n) is the actual cost from the starting point of the mobile energy-consuming device's path planning to node n, where the actual cost is the distance the mobile energy-consuming device travels from the starting point to node n, i is the energy storage station number, and h... i (n) represents the estimated cost from node n to the end point of the path planning, the energy replenishment station i. The estimated cost is equal to the energy replenishment distance, and Q is the energy replenishment information intensity.

[0022] Furthermore, the formula for calculating the energy replenishment information intensity Q is as follows:

[0023]

[0024] In the formula, d is the energy replenishment distance, V is the energy replenishment rate, and N is the energy supply information. The x-coordinate of the location of the refueling station. Let x be the ordinate of the location of the refueling station. n Let y be the x-coordinate of the location of the mobile energy-consuming device. n Let y be the ordinate of the location of the mobile energy-consuming device. The energy replenishment information intensity Q is the ratio of the product of the energy replenishment rate, the energy supply information, and the actual cost g(n) from the starting point to the current node to the distance from the current node to the target node.

[0025] Furthermore, the expression for calculating the energy replenishment distance d is:

[0026]

[0027] In the formula, The x-coordinate of the location of the refueling station. Let x be the ordinate of the location of the refueling station. n Let y be the x-coordinate of the location of the mobile energy-consuming device. n The vertical coordinate represents the location of the mobile energy-consuming device.

[0028] Furthermore, the energy replenishment rate is equal to the power of the charging pile.

[0029] Furthermore, energy information includes replenishment rate and supply information, as well as distance information.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1) This invention optimizes the cost function of the bidirectional path optimization algorithm and adopts a design heuristic function to adjust the energy supplementation information intensity. By introducing the energy supplementation information intensity into the bidirectional optimization algorithm, the optimal path is calculated. The energy supplementation information intensity at different distances is adjusted to control the range of path search, adjust the convergence ability of the algorithm, and obtain accurate energy supplementation path search results.

[0032] 2) The present invention uses an improved bidirectional path optimization algorithm to search for the current position of the mobile energy-consuming device and the coordinate positions of each energy-replenishing station from both ends until the coordinates of the bidirectional selected extended nodes are the same, thereby reducing the search radius on one side and improving the overall search efficiency of the algorithm. Attached Figure Description

[0033] Figure 1 This is a flowchart of a method for planning a power replenishment path for mobile energy-consuming devices according to the present invention;

[0034] Figure 2 The flowchart is for the traditional A* algorithm;

[0035] Figure 3 This is a flowchart of the bidirectional path optimization algorithm of the present invention;

[0036] Figure 4 This is a schematic diagram of a two-way path planning method for energy replenishment path planning of mobile energy-consuming devices according to the present invention;

[0037] Figure 5 This is a schematic diagram illustrating the attenuation of energy replenishment information as the distance increases in a mobile energy-consuming device energy replenishment path planning method according to the present invention. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0039] Example 1

[0040] like Figure 1 As shown, the present invention is a method for planning a power replenishment path for mobile energy-consuming devices, comprising the following steps:

[0041] S1: After the mobile energy-consuming device detects that the energy has decreased to the threshold, it will start a power replenishment reminder. The mobile energy-consuming device will start to collect energy information from the power replenishment station and obtain current map information, including the location coordinates of the mobile energy-consuming device and the power replenishment station, as well as the location coordinates of obstacles in the map.

[0042] Once the mobile energy-consuming device detects that the energy level has dropped to a threshold, it will issue a recharge reminder. The mobile energy-consuming device will begin collecting energy information from recharge stations and obtaining current map information, specifically including:

[0043] The energy replenishment reminder is triggered when the energy level of a mobile energy-consuming device drops to a threshold. The analog signal generated by the device is converted into a high-level digital signal by an analog-to-digital converter and then fed back to the control system to initiate the energy replenishment reminder. The collected energy information includes the energy replenishment rate, supply information, and distance information. The energy replenishment rate is equivalent to the power of the charging station, the supply information is the number of available charging stations, and the map information includes the location coordinates of the mobile energy-consuming device and the energy replenishment station, as well as the location coordinates of obstacles on the map. The location coordinates are the horizontal and vertical coordinates after defining the mobile energy-consuming device as the origin.

[0044] like Figure 2 As shown, this is the traditional A* algorithm. This invention improves upon the A* algorithm to obtain the following... Figure 3 The improved A* algorithm shown is described below. The specific process of improving the A* algorithm is as follows:

[0045] S2: The mobile energy-consuming device uses an improved A* algorithm for bidirectional path optimization. Based on the collected energy information of the energy replenishment station and the current map information, namely the coordinate information of the energy replenishment station, the distance information from the energy replenishment station to the mobile energy-consuming device, and the coordinate information of the location of obstacles in the map, the device performs path planning to obtain the energy replenishment path and thus performs energy replenishment navigation.

[0046] S21: Create a first priority list (open-1), a second priority list (open-2), a first hash list (close-1), and a second hash list (close-2). Define the starting coordinate position s of the mobile energy-consuming device as the origin, the direction of movement of the mobile energy-consuming device at the starting position as the positive y-axis, and the horizontal rightward direction as the positive x-axis. Set the starting coordinate position s of the mobile energy-consuming device and the coordinate position g of the energy replenishment station respectively. i , where i is the refueling station number, is stored in the first priority list and the second priority list;

[0047] The expression for the bidirectional path optimization algorithm that incorporates the energy replenishment information intensity Q is as follows:

[0048] f(n)=g(n)+Qh i (n)

[0049] In the formula, f(n) is the cost function of the mobile energy-consuming device reaching the planned end point of the energy replenishment station via any node n from its starting position, g(n) is the actual cost from the starting point of the mobile energy-consuming device's path planning to node n, where the actual cost is the distance the mobile energy-consuming device travels from the starting point to node n, i is the energy storage station number, and h... i (n) represents the estimated cost from node n to the end point of the path planning, the energy replenishment station i. The estimated cost is equal to the energy replenishment distance, and Q is the energy replenishment information intensity.

[0050] The formula for calculating the energy replenishment information intensity Q is as follows:

[0051]

[0052] In the formula, d is the energy replenishment distance, V is the energy replenishment rate, and N is the energy supply information. The x-coordinate of the location of the refueling station. Let x be the ordinate of the location of the refueling station. n Let y be the x-coordinate of the location of the mobile energy-consuming device. n The vertical coordinate represents the location of the mobile energy-consuming device.

[0053] The expression for calculating the energy replenishment distance d is:

[0054]

[0055] In the formula, The x-coordinate of the location of the refueling station. Let x be the ordinate of the location of the refueling station. n Let y be the x-coordinate of the location of the mobile energy-consuming device. n The vertical coordinate represents the location of the mobile energy-consuming device.

[0056] The energy replenishment information intensity Q is the ratio of the product of the energy replenishment rate, the energy supply information, and the actual cost g(n) from the starting point to the current node to the distance from the current node to the target node. Therefore, the value of Q changes dynamically with the search process. In the early stage of the contraction process, the value of Q is small, which shortens the search time, improves the search efficiency, and increases the search range. In the later stage of the contraction process, the value of Q is large, which can improve the search accuracy and enable the algorithm to converge quickly.

[0057] S22: Set the starting coordinate position s of the mobile energy-consuming device and the coordinate position g of the energy replenishment station. i These serve as the starting nodes for bidirectional path planning;

[0058] S23: Search for the starting coordinates 's' of mobile energy-consuming devices and the coordinates 'g' of refueling stations. i Expand the node, and combine s and g i The coordinates are added to the first hash list and the second hash list respectively;

[0059] S24: Combine s and g i The extended nodes are added to the first priority list and the second priority list respectively, and the cost value is calculated using the cost formula. The extended node with the smallest cost value is selected.

[0060] S25: If the extended nodes selected in both directions are inconsistent, the extended node with the lowest value in the forward search process is moved into the first hash list and used as the initial node for the next forward search. The extended node with the lowest value in the reverse search process is moved into the second hash list and used as the initial node for the next reverse search. Return to S23. If the coordinates of the extended nodes selected in both directions are the same, exit the path optimization loop and find the optimal path in the first hash list and the second hash list according to their respective forward pointers.

[0061] S3: Perform energy replenishment navigation based on the obtained optimal path.

[0062] like Figure 4 The diagram shows a bidirectional path planning method for recharging mobile energy-consuming devices according to the present invention. The black blocks represent obstacles, the white blocks represent unobstructed paths for the mobile energy-consuming devices, the black pentagram marked with 's' represents the starting point, the black pentagram marked with 'g' represents the target recharging station location, the black arrows represent the direction of the bidirectional path planning algorithm, and the black lines connecting the two pentagrams represent the planned route.

[0063] Figure 5 This is a schematic diagram illustrating the attenuation of energy replenishment information intensity as distance increases in the path planning of mobile energy-consuming devices according to the present invention. In the diagram, lines of different solid and dashed colors and different thicknesses represent the energy replenishment information intensity at different distances. When the mobile energy-consuming device is far from the target energy replenishment station, the information intensity is weak and is represented by dashed lines. As the distance approaches, the information intensity changes from dashed lines to solid lines. As the distance between the device and the energy replenishment station decreases, the lines gradually thicken, indicating that the energy replenishment information intensity gradually increases.

[0064] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for planning a recharge path for mobile energy-consuming devices, characterized in that, Includes the following steps: S1: When the mobile energy-consuming device detects that its own energy is lower than a preset threshold, it collects energy information and map information of the power replenishment station. The map information includes the location coordinates of the mobile energy-consuming device, the location coordinates of the power replenishment station, and the location coordinates of obstacles. S2: Based on energy information, location coordinates of mobile energy-consuming devices, location coordinates of refueling stations, and location coordinates of obstacles, the optimal path is calculated using a bidirectional path optimization algorithm that incorporates the intensity of refueling information. S3: Perform energy replenishment navigation based on the optimal path; Step S2 is as follows: S21: Create a first priority list, a second priority list, a first hash list, and a second hash list, and store the current location and the coordinates of each refueling station into the first priority list and the second priority list; S22: The starting coordinates of the mobile energy-consuming device and the coordinates of each energy replenishment station are respectively used as the starting nodes of the bidirectional path planning; S23: Search for the current location of the mobile energy-consuming device and the extended nodes of the coordinates of each energy replenishment station, and add the current location of the mobile energy-consuming device and the coordinates of each energy replenishment station to the first hash list and the second hash list respectively; S24: Add the current location of the mobile energy-consuming device and the extended nodes of each energy replenishment station's coordinates to the first priority list and the second priority list respectively, calculate the cost value using the cost formula, and select the extended node with the lowest cost value. S25: When the extended nodes selected in both directions are inconsistent, move the extended node with the lowest cost value in the forward search process into the first hash list and use this extended node as the initial node for the next forward search. Move the extended node with the lowest cost value in the reverse search process into the second hash list and use this extended node as the initial node for the next reverse search. Return to S23. When the coordinates of the extended nodes selected in both directions are the same, the optimal path is obtained. In step S2, the expression for the bidirectional path optimization algorithm is: In the formula, Let be the cost function for a mobile energy-consuming device to reach the destination energy replenishment station via any node n from its starting position. Let i be the actual cost from the starting point of the path planning for the mobile energy-consuming device to node n, where i is the distance the mobile energy-consuming device travels from the starting point to node n, and i is the energy storage station number. The estimated cost value is the distance from node n to the end point of the path planning, namely, the energy replenishment station i. The estimated cost value is equal to the energy replenishment distance, and Q is the energy replenishment information intensity. The formula for calculating the energy replenishment information intensity Q is as follows: In the formula, d is the energy replenishment distance, V is the energy replenishment rate, and N is the energy supply information. The x-coordinate of the location of the refueling station. The vertical coordinate of the location of the refueling station. The x-coordinate represents the location of the mobile energy-consuming device. Let y be the ordinate of the location of the mobile energy-consuming device. The energy replenishment information intensity Q is the ratio of the product of the energy replenishment rate, the energy supply information, and the actual cost g(n) from the starting point to the current node to the distance from the current node to the target node.

2. The method for planning a recharge path for mobile energy-consuming devices according to claim 1, characterized in that, The map information specifically includes: defining the current position of the mobile energy-consuming device as the origin, establishing a Cartesian coordinate system with the current direction of movement of the mobile energy-consuming device and the horizontal and vertical directions of the current direction of movement, and the current position coordinates of the mobile energy-consuming device, the position coordinates of each energy replenishment station, and the position coordinates of obstacles in this coordinate system.

3. The method for planning a recharge path for mobile energy-consuming devices according to claim 1, characterized in that, In step S23, the extended node is the node with the lowest cost estimate among the adjacent nodes centered on the current position node.

4. The method for planning a recharge path for mobile energy-consuming devices according to claim 1, characterized in that, The energy replenishment rate is equal to the power of the charging pile.

5. The method for planning a recharge path for mobile energy-consuming devices according to claim 1, characterized in that, The energy information includes replenishment rate, supply information, and distance information.

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