An aviation cable path planning method based on a node searching algorithm

Abstract

The application discloses an aviation cable path planning method based on a node searching algorithm, and comprises the following steps: obtaining an initial node set through baseline and wiring environment information; performing redundant node screening optimization on the nodes in the constructed node set according to obstacle information, to obtain a node set with redundant nodes removed; locking the tail nodes of the node set with redundant nodes removed, and re-screening the remaining nodes; repeating the above process, iteratively optimizing the node set, and obtaining an optimal node sequence set; taking the optimal node sequence as wiring path nodes, and obtaining a cable path. The application can automatically plan the aviation cable path with smaller time complexity, the wiring can effectively avoid obstacles, and the line is the shortest under the same condition, and meanwhile, the friction caused by the line is prevented from causing influential abrasion.

Description

Technical Field

[0001] This invention belongs to the field of aviation cable path planning technology, specifically relating to an aviation cable path planning method based on a keypoint pathfinding algorithm. Background Technology

[0002] Cables are the medium for transmitting energy and signals in complex electromechanical products and are widely used in the aviation industry. The layout and assembly quality of cables are crucial factors affecting aircraft stability. Traditional cabling mainly relies on mold assembly testing, which suffers from problems such as unreasonable cabling, difficulty in determining laying processes, and low laying efficiency. With the development of computer technology, utilizing artificial intelligence for path planning has become a research focus.

[0003] The core of path planning is algorithm design. Commonly used path planning algorithms include Dijstra's algorithm, A.S. algorithm, and others. * Algorithms include particle swarm optimization (PSO) and fast search random number generation (SCR) algorithms. Dijstra's algorithm finds the global optimum through extensive traversal, but its computational efficiency decreases as the map grid granularity increases. PSO is prone to getting trapped in local optima. Fast search random number generation (SCR) algorithms struggle with path finding in confined spaces. * An algorithm is a heuristic function that reacts quickly to the environment, and is therefore widely used in path planning research.

[0004] A * As a typical grid-based path processing method, the algorithm is widely used in path planning. However, the selection of the neighborhood in the algorithm is closely related to the length and number of bends in the planned path. A larger neighborhood allows for more directional choices in path planning, reducing unnecessary bends. Optimizing by expanding the search range has a significant advantage in selecting a better planned path, but it requires determining whether there are obstacles between the target node and its neighbors that prevent straight-line routing, greatly reducing the algorithm's search efficiency. Limited by the grid selection problem, in complex cases, A... * The algorithm is difficult to meet the wiring process requirements in the actual assembly of aviation cables.

[0005] Therefore, based on the principle that "the shortest distance between two points in a plane is a straight line," this paper proposes a node-based pathfinding algorithm to effectively solve the path planning problem between nodes using a non-grid approach. * The algorithm overcomes the common problems of large length redundancy and numerous bends, enabling automatic planning of aviation cable paths with less time complexity. Summary of the Invention

[0006] In order to effectively solve A *To address the common problems of excessive length redundancy and numerous bends in conventional algorithms, this invention provides a method for aviation cable path planning based on a keypoint pathfinding algorithm. Based on the principle that "the shortest distance between two points in a plane is a straight line," this method considers using a non-grid approach to solve the path planning problem between nodes and proposes a keypoint pathfinding algorithm to obtain the optimal wiring scheme between the initial point and the desired termination point. This invention achieves automatic planning of aviation cable paths with lower time complexity, effectively avoids obstacles, minimizes the route length under the same conditions, and ensures that friction does not cause significant wear and tear on the cable.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A method for planning aviation cableway routes based on a keypoint pathfinding algorithm includes the following steps:

[0009] S1. Constructing Joint Sets: Obtain the initial set of joints J = {j1,j2,...,j...} using baseline and wiring environment information. n-1 ,j n};

[0010] S2. Redundant Joint Node Screening: Based on obstacle information, the joints in the joint point set constructed in step S1 are screened and optimized for redundancy, resulting in a joint point set J' = {j'1,j'2,...,j'} with redundant nodes removed. n-1 ,j' n};

[0011] S3. Tail Node Locking Optimization: Lock the tail nodes of the joint set after removing redundant points in step S2, and repeat step S2 to filter redundant joints for the remaining nodes; repeat the above process to iteratively optimize the joint set and obtain the optimal joint sequence set.

[0012] S4. Using the optimal joint sequence obtained in step S3 as the wiring path nodes, obtain the cable path.

[0013] Further, step S1 includes: selecting a starting point P according to the definition. s and endpoint P t ; By connecting the starting point P s and endpoint P t Construct baseline L st Construct a bounding box set B for obstacles and calculate collision points using a collision detection algorithm; construct an initial keypoint sequence set J = {j1,j2,...,j...} based on the collision points and bounding box vertices. n-1 ,j n}

[0014] Further, step S2 includes:

[0015] S21. Input and initialize the initial set of joints: Step S1 yields the initial set of joint order J = {j1,j2,...,j...} n-1 ,j n In the initial stage, let the head node pointer j be... s At the initial joint position, i.e., j s =j1; Tail node pointer j t Pointing to the position j of the last joint t =j n The current number of elements in the set, s;

[0016] S22. Perform a redundancy check on the first and last joints in the initial joint set. If there is interference between the baseline between the preferred joints and the bounding box of the obstacle, then shrink the tail node pointer forward by one position, i.e., j. t =j t-1 The pointer of the head node remains unchanged; if there is no interference and there are intermediate nodes, then the intermediate nodes are redundant nodes and are deleted.

[0017] S23. Adjust the positions of the first and last node pointers, j s =j s+1 j t =j' n Iteratively filter and optimize until j s .next=p t Termination, resulting in the set of joints J' = {j'1,j'2,...,j'} after removing redundant nodes. n-1 ,j' n}, where j'1=P s j' n =P t .

[0018] Further, step S3 includes: after filtering redundant nodes in step S2, a new node sequence J' = {j'1,j'2,...,j'} is obtained. n-1 ,j' n}, where j'1=P s j' n =P t ; Move node j' n Remove from list J' and store as the final retained node in J. final Set; with j' n-1 As the new target node, redundant nodes are screened again; the above process is repeated until the number of elements in the node sequence is 1, at which point it is directly stored in J. final Set up the set, end the path planning; output J in reverse order. final The elements yield the optimal set of joint sequence.

[0019] The present invention has the following beneficial effects:

[0020] This invention provides a method for aviation cable path planning based on a joint-point pathfinding algorithm, proposing a non-gridized joint-point pathfinding algorithm. Introducing the concept of a "baseline," an initial set of joints is first obtained using the baseline and cabling environment information, transforming the path planning problem into an optimization problem of selecting the joint set. Then, redundant joints are screened and optimized based on obstacle information. Finally, the set of joints with redundant points removed is iteratively optimized, with the optimal path used as the convergence criterion for adding and deleting joints. Ultimately, the joints are used as cabling path nodes to obtain the cable path.

[0021] Experimental results show that the key point routing algorithm can effectively reduce the number of bends and cable length in cable routing, and is superior in terms of routing time, making it more suitable for automatic routing trajectory planning of complex cable structures in aircraft. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the selection of the joint point set in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart illustrating the redundant keypoint filtering process described in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the initial joint construction described in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram illustrating the redundant joint filtering operation principle described in an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram illustrating the removal of joint point j2 according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram illustrating the removal of joint point j4 according to an embodiment of the present invention;

[0028] Figure 7 This is a flowchart illustrating the tail point locking optimization process described in an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram illustrating the tail point locking operation principle described in an embodiment of the present invention;

[0030] Figure 9 This is a schematic diagram of the multi-obstacle redundancy screening path described in an embodiment of the present invention;

[0031] Figure 10(a) shows the eight domains A * A schematic diagram of the path planning results from the algorithm simulation experiment;

[0032] Figure 10(b) shows the twenty-four neighborhood A. * A schematic diagram of the path planning results from the algorithm simulation experiment;

[0033] Figure 10(c) is a schematic diagram of the path planning results in the simulation experiment of the key point pathfinding algorithm;

[0034] Figure 11 This is a schematic diagram of the wiring example result of a three-dimensional model according to an embodiment of the present invention;

[0035] Figure 12 This is a schematic diagram of the wiring path of a two-dimensional model according to an embodiment of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments:

[0037] First, the terms and symbols used in this invention are explained, such as... Figure 1 As shown:

[0038] p s The starting point of the wiring path;

[0039] p t The end point of the wiring path;

[0040] Baseline: Starting point p s and the endpoint p t The line segments connecting the two lines;

[0041] Expansion boundary: In order to prevent damage caused by collision and friction with obstacles during wiring and to ensure the safety of wiring, a certain space needs to be reserved around the obstacle. The boundary of the obstacle after reserving space is called the expansion boundary.

[0042] Bounding box: Obstacles are generally irregular and irregular in shape. They are represented by safe and standardized rectangles. These rectangles used to represent obstacles are called bounding boxes. Bounding boxes are rectangular representations of obstacles that cover the expansion boundary.

[0043] v i The four vertices of the rectangular bounding box;

[0044] p1, p2: Collision points, i.e., the intersections of the baseline and the bounding box boundary;

[0045] Detour path: The shortest route between two collision points around the boundary of the bounding box;

[0046] Around-the-boundary vertices: The bounding box vertices traversed by the around-the-boundary path.

[0047] The following is a brief introduction to the principle of the joint pathfinding algorithm proposed in this invention:

[0048] The starting point p of the wiring paths to the destination p t Baseline L between st Represented as:

[0049] L st =L(p s ,p t )

[0050] Suppose that in a cabling environment, there exists a series of obstacles S = {S1, S2, S3, ..., S...} n}, expand the elements in the obstacle list and construct bounding boxes to obtain a new obstacle list B = {B1, B2, B3, ..., B}. n}

[0051] Define L st The points where the elements intersect with those in B are called collision points, and the set of collision points is P. n =C(L) st ,B), where function C is the collision point detection function, and its result has the following three cases:

[0052] (1) If set P n The number of elements in the middle is 0, which means L st Do not interfere with B;

[0053] (2) If set P n When the number of elements in L is 1, this means that L st It interferes precisely with the vertices of the bounding box;

[0054] (3) If set P n The number of elements in the middle is 2, which indicates interference.

[0055] If interference exists, the wiring path needs to enter the obstacle region from one collision point, travel along the bounding box edge, and finally exit the obstacle region from the other collision point. This process is called safe detour. Clearly, there are two detour directions: clockwise and counter-clockwise. Based on the constraint of shorter wiring path, the detour distance for both methods is calculated, and the shorter path is selected as the final detour method. The bounding box vertices existing on the detour path are called detour vertices, and the set of detour vertices is called the detour vertex set, denoted by V. n Indicates. Usually V n The number of elements in it is 1 or 2.

[0056] Set P = {P1, P2, ..., P} n} represents L st The set of all collision points when an element in B collides with an element, V = {V1, V2, ..., V...} n} represents L stThe set of all detour points when colliding with B, set J pv ={P 11 V1,P 12 ,P 21 V2,P 22 ,...,P n1 V n ,P n2} represents the set of key points in sequence. Let set J = {P} s J pv ,P t Let} be the set of joints. Path optimization algorithms based on the set of joints are called joint pathfinding algorithms.

[0057] Example

[0058] This embodiment is a method for planning aviation cableway routes based on a keypoint pathfinding algorithm, including the following steps:

[0059] S1. Constructing Joint Sets: Obtain the initial set of joints J = {j1,j2,...,j...} using baseline and wiring environment information. n-1 ,j n The path planning problem is transformed into an optimization problem of selecting a set of key points.

[0060] Select the starting point P according to the definition. s and endpoint P t Construct the default baseline L st Construct a set of obstacle bounding boxes B, and use a collision detection algorithm to calculate collision points. Use the collision points and interference bounding box vertices to construct an initial set of key points.

[0061] Constructing the initial set of joints is the initialization step of the joint-point pathfinding algorithm. The preprocessing of obstacles and the selection of elements in the initial joint set J are particularly crucial. For example... Figure 1 As shown, the obstacle undergoes an expansion operation, constructing a virtual expansion boundary that keeps the obstacle within a safe range. The rectangular envelope of the expansion boundary serves as the bounding box for the obstacle, uniformly representing irregularly shaped obstacles with safe and standardized rectangles. This is achieved by connecting the starting point P... s and endpoint P t Construct a baseline and use the baseline to select key points. Figure 1 In the diagram, p1 and p2 are collision points, the bypass path is p1→v1→p2, and the bypass vertex is v1. The sole criterion for choosing the bypass path is its length. Finally, by integrating the starting point, ending point, bypass vertex, and collision point, we obtain the initial key sequence set J = {j1,j2,...,j...}. n-1 ,j n}

[0062] S2. Redundant Joint Node Screening: Based on obstacle information, the joints in the joint node set constructed in step S1 are screened and optimized for redundancy, resulting in a joint node set J' = {j1',j'2,...,j'} with redundant nodes removed. n-1 ,j' n}, where j1'=P s j' n =P t :

[0063] like Figure 4 As shown, there is an ordered joint sequence J. ab ={j a ,j a+1 ,...,j b-1 ,j b} is the key point j a To the joint point j b The initial wiring path, J ab It is a subset J of the initial keypoint sequence, J ab ∈J. If there exists a straight line L ab =L(j a ,j b ), and does not interfere with the bounding box set B of obstacles, that is The node sequence J is called (a+1)(b-1) ={j a+1 ,...,j b-1} represents a sequence of redundant nodes, and the operation J' = JJ (a+1)(b-1) This is called redundant node filtering.

[0064] S21. Input and initialize the initial set of joints: Step S1 yields the initial set of joint order J = {j1,j2,...,j...} n-1 ,j n In the initial stage, let the head node pointer j be... s At the initial joint position, i.e., j s =j1, tail node pointer j t Pointing to the position j of the last joint t =j n , where s is the number of elements in the current set.

[0065] In this embodiment, as Figure 3 The initial node sequence J = {j1,j2,j3,j4,j5} is shown, with 5 elements, and the wiring trajectory is j1→j2→j3→j4→j5.

[0066] S22. Perform a redundancy check on the first and last joints in the initial joint set. If there is interference between the baseline between the preferred joints and the bounding box of the obstacle, shrink the tail node pointer by one position, i.e., j t =jt-1 The pointer of the head node remains unchanged; if there is no interference and there is an intermediate node, the intermediate node is a redundant node, and the redundant node is deleted.

[0067] In this embodiment, firstly, the head node pointer j is... s Pointer to j1, tail node pointer j t Pointing to j5. After interference checking, L 1,5 Interference with the obstacle causes the tail node pointer to move forward one position, j t =j4. At this time, L 1,4 There is interference similar to that with the obstacle, so it continues to move forward, j t =j3. Interference analysis revealed that L 1,3 There is no longer any interference with the obstacle, so the redundant keypoint filtering process begins. Node j2 is removed, resulting in a new sequence J' = {j1,j3,j4,j5}, as shown below. Figure 5 As shown.

[0068] S23. Adjust the positions of the first and last node pointers, j s =j s+1 j t =j' n Iteratively filter and optimize until j s .next=p t Termination, resulting in the set of joints J' = {j1', j'2, ..., j'} after removing redundant nodes. n-1 ,j' n}, where j1'=P s j' n =P t .

[0069] In this embodiment, after the removal operation is performed, j s Move one position backward, j t Returning to the end of the sequence, i.e., j s =j3,j t =j5. Because L 3,5 There is no interference with obstacles, so the redundant node filtering operation is performed again to exclude j4 from the sequence, as follows. Figure 6 As shown. readjust the positions of the first and last pointers again, j s =j3,j t =j5. Since the two pointers are adjacent and j t The pointer points to the last node of sequence J', so the optimization termination condition is triggered, ending the entire redundant joint filtering process.

[0070] S3. Tail Node Locking Optimization: Lock the tail nodes of the joint set after removing redundant points in step S2, and repeat step S2 to filter redundant joints for the remaining nodes; repeat the above process to iteratively optimize the joint set and obtain the optimal joint sequence set.

[0071] After the redundant node selection and optimization process, some simple path planning tasks can be completed satisfactorily. However, for complex environments, there are still cases where a better routing path can be found. The main reason for this is that the redundant node selection and optimization is based on a local optimization algorithm using the first and last nodes. Therefore, changing the settings of the first and last nodes often leads to new, more optimized routing path plans. The tail node locking optimization algorithm, by locking the tail node and resetting the start and end positions of the path optimization, aims to further find an optimized path. This process is a secondary optimization process based on redundant node optimization, adhering to the following two criteria:

[0072] First, under the same trend, the number of bends and the length change in the same direction, that is, more bends generally result in a longer length.

[0073] Second, under the same number of bends, the smaller the bending angle, the closer it is to a straight line, and the smaller the internal stress of the cable.

[0074] When performing tail-point locking optimization, the tail node is first pushed into J as the final retained node. final Stack, such as Figure 8 As shown. Then, the tail joint pointer j t Select one node position forward, i.e., j t =j' n-1 Re-execute the redundant keypoint filtering and optimization, retain the better path, and perform tail point locking. When j s .next=j t After the operation is complete, J will pop up sequentially. final The elements in the stack represent the optimal sequence of key points.

[0075] like Figure 7 As shown, the tail point locking optimization steps are as follows:

[0076] After filtering redundant nodes in step S2, the new node sequence obtained is J'={j'1,j'2,...,j' n-1 ,j' n}, where j1'=P s j' n =P t . Node j' n Remove from list J' and store as the final retained node in J. final Set; with j' n-1As the new target node, redundant nodes are screened again; the above process is repeated until the number of elements in the node sequence is 1, at which point it is directly stored in J. final Set up the set, end the path planning; output J in reverse order. final The element is the set of optimal key sequence obtained.

[0077] In this embodiment, as Figure 8 As shown, the solid line represents the routing path optimized after redundancy screening in a multi-obstacle environment, while the dashed line represents the optimal routing path. Comparing the two, it can be seen that the redundancy screening optimization result is longer and has more bends compared to the optimal path. Therefore, tail point locking is used to further optimize the path. First, joint j'7 is directly pushed into J... final In the stack, the tail node pointer points to j'6, and redundancy filtering optimization is performed. Due to the different baseline, a new planned path is obtained, such as... Figure 8 As shown, compared to the original wiring path, the new planned path is shorter and has fewer bends. Based on the two criteria mentioned earlier, the new path is superior and therefore replaces the original path in the new optimization process. After final optimization, the number of nodes decreased from 7 to 5, and the number of bends decreased from 6 to 4. The optimized path is completely consistent with the optimal path.

[0078] S4. Using the optimal joint sequence obtained in step S3 as the wiring path nodes, obtain the cable path.

[0079] To verify the correctness and reliability of the path planning method of this invention, simulation experiments based on Matlab were conducted. The algorithm of this invention was compared with the eight-neighbor A... * The algorithm's sum of 24 neighborhoods A * Comparative experiments were conducted on the algorithms.

[0080] As can be seen from the simulation data in Figure 10, although the 24-neighborhood is currently the largest in A... * While this algorithm is considered relatively good, its improvement over the 8-neighborhood algorithm is not significant. The keypoint pathfinding algorithm proposed in this invention, however, significantly reduces path length and the number of bends, resulting in superior routing path planning. To further verify the reliability of the algorithm, multiple sets of comparative experiments were conducted, and the experimental data are shown in Tables 1 and 2. The experimental results show that, under the same routing conditions, it significantly outperforms A in terms of both path length and the number of bends. * algorithm.

[0081] Table 1 Comparison of Bending Numbers

[0082]

[0083] Table 2 Comparison of Wiring Length

[0084]

[0085] Since aircraft often have a large number of onboard devices installed inside, and the wiring environment is very complex, algorithm efficiency is also an important indicator for evaluating its quality.

[0086] A * The algorithm needs to traverse the neighborhood of each target node to calculate the heuristic function, thereby obtaining the path planning result. The computational efficiency is directly related to the grid granularity and the spatial complexity of the wiring environment. However, aircraft are generally large in size and have long wiring distances, which significantly increases the computational efficiency. * The time cost of the algorithm. In contrast, the time complexity of the articulation algorithm depends only on the layout and number of obstacles in the wiring environment, and is independent of the wiring distance, so it is more advantageous for path planning in large spaces.

[0087] To test the execution efficiency of the joint pathfinding algorithm, the computation time of three algorithms was statistically analyzed, and the experimental data is shown in Table 3. The relevant data indicates that the joint pathfinding algorithm, compared to A... * The algorithm has higher execution efficiency and is more suitable for the long-distance wiring conditions of aircraft.

[0088] Table 3 Comparison of computation time

[0089]

[0090] To verify the effectiveness of the joint-point pathfinding algorithm in engineering applications, an automatic wiring experiment based on a 3D model was conducted, such as... Figure 11 As shown. Unlike two-dimensional planar experiments, in a three-dimensional environment, there is height information, which requires vertical projection and flattening of the three-dimensional model to reduce the three-dimensional model to a two-dimensional model in order to achieve the purpose of wall-mounted wiring.

[0091] First, the starting point is projected directly onto the base plate as a temporary starting point for path planning. After the routing is completed, the distance from the starting point to the projection is automatically increased. Then, the side plate is rotated counterclockwise by π / 2 to coincide with the base plate. Finally, the routing process is implemented based on the two-dimensional unfolded model, as follows: Figure 12 As shown.

[0092] Experiments show that, in three-dimensional space, the pathfinding algorithm combined with the wall-hugging principle can effectively obtain a better wiring path, proving the effectiveness of the method in three-dimensional space.

[0093] The following conclusions were drawn through comparative simulation:

[0094] (1) The wiring path length generated by the keypoint pathfinding algorithm, compared to the 8-neighborhood A * The algorithm reduces the average path length by 5% compared to the 24-neighborhood A. *The algorithm reduces path length by an average of 3%.

[0095] (2) In terms of the number of bends, the planning results of the joint pathfinding algorithm are better than those of the 8-neighborhood A algorithm. * The algorithm reduces the average number of bends by 52% compared to a 24-neighborhood A. * The algorithm reduces the average number of bends by 64%.

[0096] (3) In terms of algorithm efficiency, compared with traditional A * The algorithm is 62% faster than the 24-neighbor A algorithm. * The algorithm is improved by 86%, which is a significant improvement.

Claims

1. A method for planning aviation cableway paths based on a keypoint pathfinding algorithm, characterized in that, Includes the following steps: S1. Constructing the joint set: Obtain the initial set of joints using baseline and wiring environment information; Step S1 includes: selecting the starting point according to the definition. and the end point ; By connecting the starting point and the end point Constructing baselines Construct a set of obstacle bounding boxes The collision detection algorithm is used to calculate the collision points; an initial set of keypoint sequences is constructed using the collision points and bounding box vertices. ; S2. Redundant Joint Node Screening: Based on obstacle information, the joints in the joint node set constructed in step S1 are screened and optimized for redundancy, resulting in a joint node set with redundant nodes removed; step S2 includes: S21. Input and initialize the initial set of joints: Step S1 yields the initial set of joint sequences. In the initial stage, set the head node pointer. At the initial joint position, i.e. Tail node pointer Pointing to the end of the joint position The number of elements in the current set ; S22. Perform a redundancy check on the first and last joints in the initial joint set. If there is interference between the baseline between the preferred joints and the bounding box of the obstacle, then the pointer of the last node is moved forward by one position. The pointer of the head node remains unchanged; if there is no interference and there are intermediate nodes, then the intermediate nodes are redundant nodes and are deleted. S23. Adjust the positions of the first and last node pointers. , Iteratively filter and optimize until... Termination, resulting in a set of joints with redundant nodes removed. ,in, , ; S3. Tail Node Locking Optimization: Lock the tail nodes of the joint set after removing redundant points in step S2. Repeat step S2 for redundant joint point filtering on the remaining nodes. Repeat the above process to iteratively optimize the joint set and obtain the optimal joint sequence set. The new node sequence obtained after redundant joint point filtering in step S2 is as follows: ,in, , ; will node From list Removed from the list and stored as the final retained node. Set; to As the new target node, redundant nodes are screened again; the above process is repeated until the number of elements in the node sequence is 1, at which point it is directly stored. Set up the set, end the path planning; output in reverse order. Elements, to obtain the set of optimal key point sequences; S4. Using the optimal joint sequence obtained in step S3 as the wiring path nodes, obtain the cable path.

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