An improved RRT-based unmanned aerial vehicle path planning method in complex elevation map

Abstract

The application relates to a complex elevation map-based unmanned aerial vehicle path planning method based on an improved RRT, which comprises the following steps: establishing a gray-scale map corresponding to gray-scale values and elevation data according to cutting of an extended planning range, and an avoidance area envelope set intersecting with the extended planning range; performing path planning in the extended planning range by using an improved fast exploration random tree; in the heuristic path point inspection process of path planning, performing avoidance area envelope inspection and gray-scale map elevation inspection on the generated path points, and removing the path points that do not pass the inspection; in the path convergence process of path planning, performing sampling point extension in an ellipse with the path initial point and the target point as the foci by using an adaptive step length, and converging the planned path; backtracking the planned path; performing collision detection on the generated path points in a reverse order, removing unnecessary path points, and screening out necessary path points to form the planned path. The application realizes path planning in a complex scene, and improves the simplicity and planning efficiency of the path.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method for UAV path planning in complex elevation maps based on an improved RRT. Background Technology

[0002] When a drone performs its assigned mission, it needs to reach the target location and complete the task according to a designated path. However, due to the uneven distribution density of geographic information in the actual planning space for drone missions, especially in complex scenarios such as mountains and jungles, the geographic data used for planning may come from sparse elevation sets from different information sources. In addition, some path planning not only relies on geographic elevation information but also needs to consider avoidance zones.

[0003] Existing global path planning algorithms, such as RRT (Rapid Exploratory Random Tree), fail to effectively integrate geographic elevation information and avoidance zones, resulting in insufficient adaptability in complex scenarios. RRT searches the entire map, requiring a full map search even when only a portion is used, thus impacting algorithm efficiency.

[0004] Therefore, a path planning scheme that can comprehensively consider geographic information and avoidance zone data is needed to improve the mission execution efficiency of UAVs in complex environments. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to disclose a UAV path planning method based on an improved RRT in complex elevation maps, thereby solving the path planning problem in complex scenarios.

[0006] This invention discloses a method for UAV path planning in complex elevation maps based on an improved RRT, comprising:

[0007] Step S1: Establish a grayscale map corresponding to grayscale values ​​and elevation data cropped according to the extended planning range, and a set of avoidance zone envelopes intersecting with the extended planning range; the extended planning range includes the planning range and the range of the avoidance zone envelope set intersecting with the planning range; the planning range is the area determined by the starting point and ending point of the UAV path planning.

[0008] Step S2: Path planning is performed within the extended planning range using an improved fast exploratory random tree; wherein,

[0009] In the heuristic path point verification process of path planning, the generated path points are subjected to avoidance zone envelope verification and grayscale elevation verification, and path points that fail the verification are removed.

[0010] During the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the initial point and the target point as the foci, thereby converging the planned path.

[0011] Step S3: Backtrack the planned path output in step S2; perform collision detection on the generated path points in reverse order, remove unnecessary path points, and select necessary path points to form the planned path.

[0012] Further, step S1 includes:

[0013] Step S101: Preprocess the geographic data of the task area and the data of the avoidance zone;

[0014] Step S102: Select a rectangle that includes the planning start point and end point, and expand it to obtain the planning area;

[0015] Step S103: Determine the extended planning range and the avoidance zone envelope set intersecting with the extended planning range by retrieving the avoidance zone envelope set; the extended planning range includes the planning range and the range of the avoidance zone envelope set intersecting with it.

[0016] Step S104: Based on the expanded planning scope, crop the grayscale image corresponding to the grayscale values ​​and elevation data to obtain the grayscale image used in the planning.

[0017] Furthermore, 1) Unified density interpolation processing: The original geographic information data is interpolated and sampled according to a unified set density;

[0018] 2) Generate grayscale image: Convert the differenced elevation data into grayscale image F; each pixel in grayscale image F corresponds to the latitude and longitude points of the elevation data, and the changes of each pixel in the longitude and latitude directions are equal intervals of longitude step value and latitude step value, respectively.

[0019] Preprocess all avoidance areas within the mission area, including:

[0020] 1) Determine the envelope rectangle of the avoidance zone: For each avoidance zone, calculate its envelope rectangle;

[0021] 2) Construct a set of envelope rectangles: Construct a set of envelope rectangles for all avoidance zones, with each envelope rectangle corresponding to an avoidance zone.

[0022] Furthermore, in step S102, the planning area expansion process includes:

[0023] 1) Generate a rectangle based on the planned start and end points; the minimum value of the rectangle's range is X. min and Y min Take the minimum value between the starting point and the ending point, and the maximum value x. max and Y max Take the maximum value between the start and end points;

[0024] 2) According to the proportion λ, from the center of the rectangle to X respectively min Y min Direction and X max Y max The direction is expanded by increasing the diagonal distance to λ times the original distance to obtain the planning range PS.

[0025] Furthermore, step S103 includes two searches of the avoidance region envelope rectangle set; wherein,

[0026] First search: Search the set of avoidance zone envelope rectangles AEBR within the planning scope PS, find the set of avoidance zone envelope rectangles that intersect with the planning scope PS; take the envelope rectangle of the planning scope PS and the set of avoidance zone envelope rectangles, and denote it as the extended planning scope PSE;

[0027] Second search: Search the set of avoidance zone envelope rectangles AEBR again within the extended planning scope PSE to find the set of avoidance zone envelope rectangles that intersect with the extended planning scope PSE.

[0028] Further, step S2 includes:

[0029] Step S201: Initialize target point, task point, and search tree:

[0030] Step S202: State sampling space sampling; sampling the generated sampling points x rand Conduct avoidance zone and elevation checks, and resample the sampling points that fail the checks;

[0031] Step S203, Sampling point extension; Extend the sampling points x sampled in step S202. rand Find the nearest node x in the search tree. near Search for the nearest node x near Starting from the sampling point, we extend to obtain a new node x. new ;

[0032] Step S204, Collision Detection: Determine the collision of node x near With the new node x new The connection E i Did a collision occur with an obstacle or avoidance zone? If yes, discard the colliding node and return to step S202; otherwise, select a new node x that does not collide. new Add it to the node set V and connect it to E. i Add it to the edge set E;

[0033] Step S205: Add a new node x to the node set V. new Reselect the parent node in the search tree:

[0034] Step S206: By reselecting the parent node in the search tree, the random tree is rewired to reduce the connection cost between nodes in the random tree.

[0035] Step S207: Determine the new node x to be added to the node set V. near With target point x goal If the distance is greater than the set distance threshold, return to step S202 and repeat sampling, extension, and collision detection; otherwise, terminate sampling and set the target point x. goal Add the node to the node set V and search for the shortest path;

[0036] Step S208, Adaptive path convergence; using an adaptive step size from the initial point x on the path. init and target point x goal By extending the sampling points within the ellipse that serves as the focus, the planned path converges, and the planning result is obtained.

[0037] Further, step S202 includes:

[0038] The sampling points are simultaneously converted to the grayscale image F and the avoidance zone set AEBR (PSE), and the sampling points that fall into the avoidance zone set AEBR (PSE) and fail the grayscale image elevation test are reselected.

[0039] Random sampling is performed in the state sampling space to obtain sampling point x. rand =(x rand,lon ,x rand,lat ), sample point x rand The pixel is converted to the grayscale image F and simultaneously converted to the avoidance zone set AEBR(PSE). It is then determined whether the point is within an obstacle or avoidance zone defined by the avoidance zone set AEBR(PSE). If the sampling point x... rand If there are obstacles or avoidance zones, repeat the steps.

[0040] Further, step S204 includes:

[0041] 1) For node x near With the new node x new The connection E i Discretize a series of latitude and longitude points e i ={e i,1 ,e i,2 ,…,e i,j ,…,e i,N}, j = 1, ..., N, where N is the number of discrete points; e i,j For connecting line E i The j-th discrete latitude and longitude point;

[0042] 2) Perform collision detection;

[0043] One by one, the latitude and longitude points e i,j ∈e i Convert the pixels into a grayscale image and determine if there are obstacles at those pixels; when e i If there are no obstacles at any point, then determine E. i It does not collide with obstacles; otherwise, a collision occurs.

[0044] Determine the connection E i Whether or not the plan passes through the avoidance zone, where the avoidance zone is the set of avoidance zone envelopes found in the second retrieval that intersect with the extended planning range PSE; if E i If no avoidance zone is crossed, then E is determined. i It did not collide with the avoidance zone, but instead collided with it;

[0045] 3) Node x detected by collision near With the new node x new The connection E i If a collision occurs with an obstacle or avoidance zone, the new node x is discarded. new And return to step S202 for resampling; if connection E i If it does not collide with obstacles or avoidance zones, then the new node x will be... new Add it to the node set V and connect it to E. i Add it to the edge set E.

[0046] Further, step S208 includes:

[0047] 1) Set the initial point x init and target point x goal The foci are used as the focus of the ellipse, and half of the shortest path P0 found from the search tree T is used as the sum of the distances from the points on the ellipse to the focus.

[0048] 2) Sampling is performed within the ellipse, and random sampling is conducted within the ellipse to obtain sampling points x. rand =(x rand,lon ,x rand,lat ), calculate the nearest distance γ between the sampling point and the obstacle γ = min{D(x rand ,X obs )}, where X obs Given the set of all obstacle points within the elliptical sampling range, the min() function is used to calculate the minimum distance between the sampling point and the set of obstacle points.

[0049] 3) Calculate the distance from x on path P0. rand The nearest node x near Then, with x rand Centered on a circle with a radius of a set length γ, K points are uniformly selected and denoted as the point set.

[0050] 4) Select one point from the K points to replace x. near This will minimize the path cost; let this point be denoted as...

[0051] 5) Move node x on path P0 near Change to Repeat steps 2)-3) to obtain path lengths P1, P2, P3, ..., Pn; n is the number of iterations.

[0052] 6) When the path length change value Pn-Pn-1 is less than the set threshold ΔP or the number of iterations reaches the set iteration threshold, convergence is determined and the planning result is obtained.

[0053] Furthermore, step S3 involves the path C = {x1x2…x} obtained in step S2. n Further, redundant path points are removed to optimize the path length; the optimization process includes:

[0054] Step S301: Input path C;

[0055] Step S302: Traverse all path points x i ∈C, for each path point x i traverse all subsequent path points x j ∈{x i+1 x i+2 …x n}, determine x i With x j The connection e i Does j collide with the obstacle?

[0056] Step S303, if x i With a certain x j If there is a collision, then x will be... i With x j Remove all intermediate path points and execute i=j, repeating step S302; if x i With all x j If there is no collision, then x i With x n Remove all intermediate path points and terminate the process, outputting the optimized path C. * .

[0057] This invention can achieve one of the following beneficial effects:

[0058] This invention discloses a UAV path planning method based on an improved Remote Terrain Response (RRT) algorithm in complex elevation maps. By combining geographic information and avoidance zone data, it ensures that each path point meets both avoidance zone requirements and elevation requirements. The improved RRT algorithm optimizes the search strategy, reduces computational load, and improves the algorithm's success rate, convergence efficiency, and search efficiency. Further filtering of path points through backtracking removes unnecessary path points, simplifying the path and improving planning efficiency. This method is applicable to complex environments such as mountains and jungles, significantly improving the adaptability and mission completion rate of UAVs in complex terrain while reducing computational resource consumption. Attached Figure Description

[0059] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0060] Figure 1 This is a flowchart of the UAV path planning method based on improved RRT in complex elevation maps according to an embodiment of the present invention;

[0061] Figure 2 This is a flowchart of the improved RRT in an embodiment of the present invention. Detailed Implementation

[0062] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and, together with the embodiments of the present invention, serve to illustrate the principles of the present invention.

[0063] One embodiment of the present invention discloses a method for UAV path planning in complex elevation maps based on an improved RRT, such as... Figure 1 As shown, it includes:

[0064] Step S1: Establish a grayscale map corresponding to grayscale values ​​and elevation data cropped according to the extended planning range, and a set of avoidance zone envelopes intersecting with the extended planning range; the extended planning range includes the planning range and the range of the avoidance zone envelope set intersecting with the planning range; the planning range is the area determined by the starting point and ending point of the UAV path planning.

[0065] Step S2: Path planning is performed within the extended planning range using an improved fast exploratory random tree; wherein,

[0066] In the heuristic path point verification process of path planning, the generated path points are subjected to avoidance zone envelope verification and grayscale elevation verification, and path points that fail the verification are removed.

[0067] During the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the initial point and the target point as the foci, thereby converging the planned path.

[0068] Step S3: Backtrack the planned path output in step S2; perform collision detection on the generated path points in reverse order, remove unnecessary path points, and select necessary path points to form the planned path.

[0069] Specifically, step S1 includes:

[0070] Step S101: Preprocess the geographical data of the task area and the data of the avoidance zone;

[0071] During each route planning process, the required geographic elevation data and avoidance zone data are delineated based on the starting and ending points to reduce the computational load and increase the planning speed. Before route planning, the geographic elevation data and avoidance zone data need to be preprocessed to meet the information input requirements of the planning process.

[0072] Preprocessing of the task area geographic data, which includes arrays of longitude (Lon), latitude (Lat), and height (H), includes:

[0073] 1) Uniform density interpolation processing: Regardless of the density of the original geographic information data, interpolation sampling is performed according to the set density to ensure data consistency and accuracy;

[0074] 2) Generate grayscale image: Convert the differenced elevation data into a grayscale image F; each pixel in grayscale image F corresponds to a latitude and longitude point of the elevation data (lon). i ,lat i The variation of each pixel in the longitude and latitude directions is represented by equally spaced longitude step values ​​δ. lon and latitude step value δ lat ;

[0075] Preprocess all avoidance zones within the mission area, as follows:

[0076] 1) Determine the avoidance envelope rectangle: For each avoidance envelope (AE), calculate its avoidance envelope bounding rectangle (AEBR);

[0077] 2) Construct the envelope set: Construct a set of envelope rectangles for all avoidance zones, with each envelope rectangle (AEBR) corresponding to an avoidance zone (AE).

[0078] Step S102: Select a rectangle that includes the planning start point and end point, and expand it to obtain the planning area;

[0079] Specifically, the expansion process of the planned area includes:

[0080] 1) Generate a rectangle based on the planned start and end points; the minimum value of the rectangle's range is X. min and Y min Take the minimum value between the starting point and the ending point, and the maximum value X. max and Y max Take the maximum value between the start and end points;

[0081] 2) According to the proportion λ, from the center of the rectangle to X min Y min Direction and X max Y max Expanding in each direction, the diagonal distance is increased to λ times the original distance to obtain the planning scope PS.

[0082] λ is an empirical parameter, which can be set to 1.414.

[0083] Step S103: Determine the extended planning range and the avoidance zone envelope set intersecting with the extended planning range by retrieving the avoidance zone envelope set; the extended planning range includes the planning range and the range of the avoidance zone envelope set intersecting with it.

[0084] Specifically, this includes two retrievals of the avoidance region envelope set; among which,

[0085] First search: Search the set of avoidance envelope bounding rectangles AEBR (Avoidance Envelope Bounding Rectangle) within the planning range PS, find the avoidance envelope set AEBR(PS) that intersects with the planning range PS; take the envelope rectangle of the planning range PS and the avoidance envelope set AEBR(PS), and denote it as the extended planning range PSE;

[0086] Second search; Search again within the extended planning scope PSE for the set of avoidance zone envelope rectangles AEBR, and find the set of avoidance zone envelopes AEBR(PSE) that intersects with the extended planning scope PSE;

[0087] Subsequent planning will employ the extended planning scope (PSE) and the avoidance zone envelope set (AEBR) for path planning.

[0088] Step S104: Based on the extended planning scope PSE, crop the grayscale image corresponding to the grayscale value and elevation data to obtain the grayscale image F(PSE) used for planning.

[0089] Specifically, the grayscale image can be generated in the manner described in step S101.

[0090] Specifically, step S2, as follows Figure 2 As shown, it includes:

[0091] Step S201: Initialize target point, task point, and search tree:

[0092] Set the initial point latitude and longitude x init =(x init,lon ,x init,lat ) and target point latitude and longitude (x goal =(x goal,lon ,x goal,lat )) , Select latitude and longitude space as the state sampling space X;

[0093] Initialize the search tree T = (V, E), where V = {x init} represents the set of nodes in the search tree, containing only the initial node; Let be the set of edges of the search tree, which is an empty set.

[0094] Step S202: State sampling space sampling; sampling the generated sampling points x rand Conduct avoidance zone and elevation checks, and resample the sampling points that fail the checks;

[0095] Specifically, the sampling points are simultaneously converted to the grayscale image F and the avoidance zone set AEBR (PSE), and the sampling points that fall into the avoidance zone set AEBR (PSE) and fail the grayscale image elevation test are reselected;

[0096] Random sampling is performed in the state sampling space to obtain sampling point x. rand =(x rand,lon ,x rand,lat ), sample point x rand The pixel is converted to the grayscale image F and simultaneously converted to the avoidance zone set AEBR(PSE). It is then determined whether the point is within an obstacle or avoidance zone defined by the avoidance zone set AEBR(PSE). If the sampling point x... rand If there are obstacles or avoidance zones, repeat the steps.

[0097] Step S203, Sampling point extension; Extend the sampling points x sampled in step S202. rand Find the nearest node x in the search tree. near Search for the nearest node x near Starting from the sampling point, we extend to obtain a new node x. new ;

[0098] If x rand Calculate sampling point x if it is not within an obstacle or avoidance zone. rand The latitude and longitude distance D = D(x) between the set V and all nodes in the search tree rand ,x i ), where x i ∈V, obtain the distance from the sampling point x randThe nearest node x near From node x near Start moving towards node x with step size L rand Extend the node to generate a new node x. new ;

[0099] Extended Step Size L: This extended step size is the step size in the latitude and longitude coordinate system. It mainly affects the maximum length of the edges in the search tree, that is, the step size in the UAV path. The specific value needs to be selected according to the map size and requirements.

[0100] Step S204, Collision Detection: Determine the collision of node x near With the new node x new The connection E i Did a collision occur with an obstacle or avoidance zone? If yes, discard the colliding node and return to step S202; otherwise, select a new node x that does not collide. new Add it to the node set V and connect it to E. i Add it to the edge set E;

[0101] Specifically, step S204 includes:

[0102] 1) For node x near With the new node x new The connection E i Discretize a series of latitude and longitude points e i ={e i,1 ,e i,2 ,…,e i,j ,…,e i,N}, j = 1, ..., N, where N is the number of discrete points; e i,j For connecting line E i The j-th discrete latitude and longitude point;

[0103] 2) Perform collision detection;

[0104] One by one, the latitude and longitude points e i,j ∈e i Convert the pixels into a grayscale image and determine if there are obstacles at those pixels; when e i If there are no obstacles at any point, then determine E. i It does not collide with obstacles; otherwise, a collision occurs.

[0105] Determine the connection E i Whether or not the plan passes through the avoidance zone, where the avoidance zone is the set of avoidance zone envelopes found in the second retrieval that intersect with the extended planning range PSE; if E i If no avoidance zone is crossed, then E is determined. i It did not collide with the avoidance zone, but instead collided with it;

[0106] 3) Node x detected by collision near With the new node x new The connection E i If a collision occurs with an obstacle or avoidance zone, the new node x is discarded. new And return to step S202 for resampling; if connection E i If it does not collide with obstacles or avoidance zones, then the new node x will be... new Add it to the node set V and connect it to E. i Add it to the edge set E.

[0107] Step S205: Add a new node x to the node set V. new Reselect the parent node in the search tree:

[0108] At the new node x new Near the set radius R within r near Find all neighboring nodes X in the tree near ={x near,1 ,x near,2 ,…,x near,M}, as a replacement for x new The candidate parent node is M, which represents the number of neighboring nodes found in the tree; the new node x is selected. new and neighboring node X near The connection between them has the minimum cost and no collision, thus optimizing the cost of generating the path;

[0109] The radius range R is set to twice the extension step size, i.e., R = 2L.

[0110] Step S206: By reselecting the parent node in the search tree, the random tree is rewired to reduce the connection cost between nodes in the random tree.

[0111] For the new node x new After reselecting the parent node, to further reduce the connection cost between nodes in the random tree, the random tree needs to be rewired: if the neighboring node X near Change the parent node to x new If the path cost can be reduced, then make the change; otherwise, do not make the change.

[0112] Step S207: Determine the new node x to be added to the node set V. near With target point x goal If the distance is greater than the set distance threshold, return to step S202 and repeat sampling, extension, and collision detection; otherwise, terminate sampling and set the target point x. goal Add the node to the node set V and search for the shortest path;

[0113] If a new node x is added to the node set near With target point x goal If the distance to node x is greater than the set latitude and longitude distance threshold δ, then repeat the above sampling, extension, and collision detection steps; if node x near With target point x near If the distance is less than the threshold δ, then the sampling is terminated, the target point is added to the node set V, and then a shortest path P0 is searched from the tree T.

[0114] Latitude and longitude distance threshold δ: This distance is in the latitude and longitude coordinate system and is used to determine whether the current node is near the target point. When it is less than this threshold, it means that the target point has been found; otherwise, it has not been found.

[0115] Step S208, Adaptive path convergence; using an adaptive step size from the initial point x on the path. init and target point x goal By extending the sampling points within the ellipse that serves as the focus, the planned path converges, and the planning result is obtained.

[0116] Specifically, step S208 includes:

[0117] 1) Set the initial point x init and target point x goal The foci are used as the focus of the ellipse, and half of the shortest path P0 found from the search tree T is used as the sum of the distances from the points on the ellipse to the focus.

[0118] 2) Sampling is performed within the ellipse, and random sampling is conducted within the ellipse to obtain sampling points x. rand =(x rand,lon ,x rand,lat ), calculate the nearest distance γ between the sampling point and the obstacle γ = min{D(x rand ,X obs )}, where X obs Given the set of all obstacle points within the elliptical sampling range, the min() function is used to calculate the minimum distance between the sampling point and the set of obstacle points.

[0119] 3) Calculate the distance from x on path P0. rand The nearest node x near Then, with x rand Centered on a circle with a radius of a set length γ, K points are uniformly selected and denoted as the point set.

[0120] 4) Select one point from the K points to replace x. near This will minimize the path cost; let this point be denoted as...

[0121] 5) Move node x on path P0 near Change to Repeat steps 2)-3) to obtain path lengths P1, P2, P3, ..., Pn; n is the number of iterations.

[0122] 6) When the path length change value Pn-Pn-1 is less than the set threshold ΔP or the number of iterations reaches the set iteration threshold, convergence is determined and the planning result is obtained.

[0123] As Pn becomes shorter, the ellipse also becomes flatter, thus concentrating the sampling points near the current path and obtaining a converged path planning result.

[0124] A threshold ΔP is set as the path convergence improvement amount, which is the distance in the latitude and longitude coordinate system. It is used as a condition to judge path convergence. When the length change value Pn-Pn-1 is less than the set threshold ΔP, it means that the path is close to the optimal; otherwise, it has not reached the optimal.

[0125] The iteration threshold is set to a large positive integer. The program will stop immediately when the number of samples exceeds this value. At this point, if a path is found, the path node is returned; otherwise, no value is returned.

[0126] Specifically, step S3 is for the path C = {x1x2…x} obtained in step S2. n Further, redundant path points are removed to optimize the path length; the optimization process includes:

[0127] Step S301: Input path C;

[0128] Step S302: Traverse all path points x i ∈C, for each path point x i traverse all subsequent path points x j ∈{x i+1 x i+2 …x n}, determine x i With x j The connection e i Does j collide with the obstacle?

[0129] Step S303, if x i With a certain x j If there is a collision, then x will be... i With x j Remove all intermediate path points and execute i=j, repeating step S302; if x i With all x j If there is no collision, then x i With x n Remove all intermediate path points and terminate the process, outputting the optimized path C. *.

[0130] In summary, the UAV path planning method based on improved RRT in complex elevation maps disclosed in this invention combines geographic information and avoidance zone data to ensure that each path point meets both avoidance zone requirements and elevation requirements. The improved RRT algorithm optimizes the search strategy, reduces computational load, and improves the algorithm's success rate, convergence efficiency, and search efficiency. The backtracking method further filters path points, removing unnecessary ones, simplifying the path, and improving planning efficiency. This method is suitable for complex environments such as mountains and jungles, significantly improving the adaptability and mission completion rate of UAVs in complex terrain, while reducing computational resource consumption.

[0131] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for UAV path planning in complex elevation maps based on improved RRT, characterized in that, include: Step S1: Establish a grayscale map corresponding to the grayscale values ​​and elevation data clipped according to the extended planning range, as well as a set of avoidance zone envelopes that intersect with the extended planning range; The extended planning range includes the planning range and the range of the avoidance zone envelope set that intersects with the planning range; the planning range is the area determined by the starting point and ending point of the UAV path planning. Step S2: Path planning is performed within the extended planning range using an improved fast exploratory random tree; wherein, In the heuristic path point verification process of path planning, the generated path points are subjected to avoidance zone envelope verification and grayscale elevation verification, and path points that fail the verification are removed. During the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the initial point and the target point as the foci, thereby converging the planned path. Step S3: Backtrack the planned path output in step S2; perform collision detection on the generated path points in reverse order, remove unnecessary path points, and select necessary path points to form the planned path. Step S2 includes: Step S201: Initialize target point, task point, and search tree: Step S202: State sampling space sampling; sampling the generated sampling points. Conduct avoidance zone and elevation checks, and resample the sampling points that fail the checks; Step S203, Sampling point extension; extending the sampling points sampled in step S202. Find the nearest node in the search tree. Search for the nearest node Starting from the sampling point, we extend to obtain a new node. ; Step S204, Collision Detection: Determine Nodes With new nodes connection Did a collision occur with an obstacle or avoidance zone? If yes, discard the colliding node and return to step S202; otherwise, select a new node that does not collide. Add to the node set V and connect the nodes. Add it to the edge set E; Step S205: Add new nodes to the node set V. Reselect the parent node in the search tree: Step S206: By reselecting the parent node in the search tree, the random tree is rewired to reduce the connection cost between nodes in the random tree. Step S207: Determine the new node to be added to the node set V. With the target point If the distance is greater than the set distance threshold, return to step S202 and repeat sampling, extension, and collision detection; otherwise, terminate sampling and set the target point... Add the node to the node set V and search for the shortest path; Step S208, Adaptive path convergence; using an adaptive step size from the path initial point... and target point By extending the sampling points within the ellipse that serves as the focus, the planned path converges, and the planning result is obtained.

2. The UAV path planning method in complex elevation maps based on improved RRT according to claim 1, characterized in that, Step S1 includes: Step S101: Preprocess the geographic data of the task area and the data of the avoidance zone; Step S102: Select a rectangle that includes the planning start point and end point, and expand it to obtain the planning area; Step S103: Determine the extended planning range and the avoidance zone envelope set intersecting with the extended planning range by retrieving the avoidance zone envelope set; the extended planning range includes the planning range and the range of the avoidance zone envelope set intersecting with it. Step S104: Based on the expanded planning scope, crop the grayscale image corresponding to the grayscale values ​​and elevation data to obtain the grayscale image used in the planning.

3. The UAV path planning method in complex elevation maps based on improved RRT according to claim 2, characterized in that, In step S101, the preprocessing of the geographic data of the task area, including arrays of longitude, latitude, and altitude, includes: 1) Unified density interpolation processing: The original geographic information data is interpolated and sampled according to a unified set density; 2) Generate grayscale image: Convert the differenced elevation data into grayscale image F; each pixel in grayscale image F corresponds to the latitude and longitude points of the elevation data, and the changes of each pixel in the longitude and latitude directions are equal intervals of longitude step value and latitude step value, respectively. Preprocess all avoidance areas within the mission area, including: 1) Determine the envelope rectangle of the avoidance zone: For each avoidance zone, calculate its envelope rectangle; 2) Construct a set of envelope rectangles: Construct a set of envelope rectangles for all avoidance zones, with each envelope rectangle corresponding to an avoidance zone.

4. The UAV path planning method in complex elevation maps based on improved RRT according to claim 3, characterized in that, Step S102, the planning area expansion process, includes: 1) Generate a rectangle based on the planned start and end points; the minimum value of the rectangle's range. and Take the minimum and maximum values ​​of the x-axis and y-axis coordinates of the starting and ending points. and Take the maximum values ​​of the x-axis and y-axis coordinates of the starting and ending points; 2) According to proportion From the center of the rectangle respectively towards direction and The direction is expanded, increasing the diagonal distance to its original value. The planned scope is obtained by multiples of PS.

5. The UAV path planning method in complex elevation maps based on improved RRT according to claim 4, characterized in that, Step S103 includes two searches of the avoidance region envelope rectangle set; wherein, First search: Search the set of avoidance zone envelope rectangles AEBR within the planning scope PS, find the set of avoidance zone envelope rectangles that intersect with the planning scope PS; take the envelope rectangle of the planning scope PS and the set of avoidance zone envelope rectangles, and denote it as the extended planning scope PSE; Second search: Search the set of avoidance zone envelope rectangles AEBR again within the extended planning scope PSE to find the set of avoidance zone envelope rectangles that intersect with the extended planning scope PSE.

6. The UAV path planning method in complex elevation maps based on improved RRT according to claim 5, characterized in that, Step S202 includes: The sampling points are simultaneously converted to the grayscale image F and the set of avoidance area envelope rectangles from the second search. Sampling points that fall into the set of avoidance area envelope rectangles and fail the grayscale image elevation test are reselected. Random sampling is performed in the state sampling space to obtain sampling points. , sampling points The pixels are converted to grayscale image F and simultaneously converted to the avoidance zone envelope rectangle set. It is determined whether the point is within the obstacle or avoidance zone defined by the avoidance zone envelope rectangle set. If the sampling point... If there are obstacles or avoidance zones, repeat the steps.

7. The UAV path planning method for complex elevation maps based on improved RRT according to claim 1, characterized in that, Step S204 includes: 1) For nodes With new nodes connection Discretize a series of latitude and longitude points , , The number of discrete points; For connecting lines Upper A discrete latitude and longitude point; 2) Perform collision detection; One by one, the latitude and longitude points Convert the pixels into a grayscale image and determine if there are obstacles at those pixels; when If there are no obstacles at any point, then determine It does not collide with obstacles; otherwise, a collision occurs. Determine the connection Whether or not the plan passes through the avoidance zone, where the avoidance zone is the set of avoidance zone envelopes found in the second retrieval that intersect with the extended planning range PSE; if If no avoidance zone is crossed, then it is judged that... It did not collide with the avoidance zone, but instead collided with it; 3) Nodes detected by collision With new nodes connection If a collision occurs with an obstacle or avoidance zone, the new node is discarded. And return to step S202 for resampling; if the connection If it does not collide with obstacles or avoidance zones, a new node will be created. Add to the node set V and connect the nodes. Add it to the edge set E.

8. The UAV path planning method in complex elevation maps based on improved RRT according to claim 1, characterized in that, Step S208 includes: 1) Set the initial point and target point The foci are used as the focus of the ellipse, and half of the shortest path P0 found from the search tree T is used as the sum of the distances from the points on the ellipse to the focus. 2) Sampling is performed within the ellipse, and random sampling is conducted within the ellipse to obtain sampling points. Calculate the closest distance between the sampling point and the obstacle. ,in, It is the set of all obstacle points within the elliptical sampling range. The function is used to calculate the minimum distance between the sampling point and the set of obstacle points; 3) Calculate the distance from path P0 The nearest node Then in Centered on, to set the length Uniformly selected on a circle with radius A set of points. ; 4) From the above One point is selected from the points to be replaced. This will minimize the path cost; let this point be denoted as... ; 5) Add nodes on path P0 Change to Repeat steps 2)-3) to obtain path lengths P1, P2, P3, ..., Pn; n is the number of iterations. 6) When the path length change value Pn-Pn-1 is less than the set threshold ΔP or the number of iterations reaches the set iteration threshold, convergence is determined and the planning result is obtained.

9. The UAV path planning method in complex elevation maps based on improved RRT according to claim 1, characterized in that, Step S3 is based on the path obtained in step S2. Further, redundant path points are removed to optimize the path length; the optimization process includes: Step S301: Input path ; Step S302: Traverse all path points For each waypoint traverse all subsequent path points ,judge and connection Whether it collided with an obstacle; Step S303, if With a certain A collision will... and Remove all intermediate path points and execute. Repeat step S302; if With all If there is no collision, then... and Remove all intermediate path points and terminate the process, outputting the optimized path. .

Images