Unmanned aerial vehicle path planning method in complex elevation map based on improved RRT

Abstract

The invention relates to a method for planning a path of an unmanned aerial vehicle in a complex elevation map based on improved RRT. The method comprises the following steps: establishing a grey-scale map corresponding to a grey-scale value cut according to an extended planning range and elevation data, and establishing an evasion area envelope set intersected with the extended planning range; performing path planning in the extended planning range by adopting an improved fast exploration random tree; in the heuristic path point inspection process of path planning, performing evasion area envelope inspection and grey-scale map elevation inspection on the generated path points, and removing the path points which do not pass the inspection; in a path convergence process of path planning, sampling point extension is carried out in an ellipse taking a path initial point and a target point as focuses by adopting a self-adaptive step length, and the planned path is converged; backtracking the planned path; and carrying out collision detection on the generated path points according to a reverse order, removing unnecessary path points, and screening out necessary path points to form a planned path. According to the invention, path planning in a complex scene is realized, and the path conciseness and planning efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a method for unmanned aerial vehicle path planning in a complex elevation map based on improved RRT. Background Art

[0002] When a drone performs an assigned target mission, it needs to follow the specified path to reach the target location and complete the mission. However, due to the uneven distribution density of geographic information in the actual planning space for drone missions, especially in complex scenes such as mountains and jungles, the geographic data used for planning may come from a sparse elevation set of 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 the RRT (Rapidly Exploring Random Tree) algorithm, fail to effectively combine geographic elevation information and avoidance zones, resulting in insufficient adaptability in complex scenarios. The RRT algorithm searches based on the entire map range. Even if only a portion of the map is used, the entire map needs to be searched, which affects the efficiency of the algorithm.

[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] In view of the above analysis, the present invention aims to disclose a UAV path planning method in a complex elevation map based on improved RRT, and solve the path planning problem in complex scenes.

[0006] The present invention discloses a method for UAV path planning in a complex elevation map based on improved RRT, comprising:

[0007] Step S1, establishing a grayscale map corresponding to the grayscale value and elevation data clipped according to the extended planning range, and an envelope set of avoidance zones intersecting with the extended planning range; the extended planning range includes the planning range and the range of the envelope set of avoidance zones intersecting with the planning range; the planning range is the area determined by the starting point and end point of the UAV path planning;

[0008] Step S2: using an improved fast exploration random tree to perform path planning within an extended planning range; wherein,

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

[0010] In the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the path initial point and the target point as the focus to converge the planned path;

[0011] Step S3, backtracking the planned path outputted in step S2; performing collision detection on the generated path points in reverse order, removing unnecessary path points, and selecting necessary path points to form the planned path.

[0012] Furthermore, the step S1 comprises:

[0013] Step S101, pre-processing the mission area geographic data and avoidance area data;

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

[0015] Step S103, determining an extended planning range and an avoidance area envelope set intersecting with the extended planning range by searching the avoidance area envelope set; the extended planning range includes the planning range and the range of the avoidance area envelope set intersecting with the planning range;

[0016] Step S104: according to the extended planning range, the grayscale image corresponding to the grayscale value and the elevation data is clipped to obtain the grayscale image used for planning.

[0017] Furthermore, 1) unified density interpolation processing: interpolation sampling processing is performed on the original geographic information data according to a unified set density;

[0018] 2) Generate grayscale image: convert the differenced elevation data into a grayscale image F; each pixel of the grayscale image F corresponds to a longitude and latitude point of the elevation data, and the change of each pixel in the longitude and latitude directions is the longitude step value and latitude step value of equal intervals 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 an envelope rectangle set: Construct the envelope rectangles of all avoidance zones into a set, where each envelope rectangle corresponds to an avoidance zone.

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

[0023] 1) Generate a rectangle based on the planned starting point and end point; the minimum value X of the rectangle range min and Y min Take the minimum value of the starting point and the end point, and the maximum value x max and Y max Take the maximum value of the starting point and the end point;

[0024] 2) According to the ratio λ, from the center of the rectangle to X min , Y min Direction and X max , Y max The planning range PS is obtained by expanding the diagonal distance to λ times of the original one.

[0025] Furthermore, in step S103, two avoidance area envelope rectangle set searches are performed; wherein:

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

[0027] Second search: Search the avoidance area envelope rectangle AEBR set again within the extended planning range PSE to find the avoidance area envelope rectangle set that intersects with the extended planning range PSE.

[0028] Furthermore, the step S2 comprises:

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

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

[0031] Step S203: Sampling point extension: Sampling point x sampled in step S202 rand Find the closest node x in the search tree near , starting from searching for the nearest node x near Start and extend towards the sampling point to get a new node x new ;

[0032] Step S204, collision detection: determine node x near With the new node x new Connection E i Whether there is a collision with an obstacle or an avoidance zone; if yes, the node that collided is discarded and the process returns to step S202; if no, a new node x that does not collide is added. new Add to the node set V and connect E i Add to 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, reselecting the parent node in the search tree to rewire the random tree, thereby reducing the connection cost between the random tree nodes;

[0035] Step S207: Determine the new node x added to the node set V. near With the target point x goal Is the distance greater than the set distance threshold? If yes, return to step S202 to repeat sampling, extension, and collision detection; if no, terminate sampling and set the target point x goal Add to the node set V and search for a shortest path;

[0036] Step S208: Adaptive path convergence; Adaptive step length is used to start from the path initial point x init and the target point x goal The sampling points are extended within the ellipse as the focus, the planned path is converged, and the planning result is obtained.

[0037] Furthermore, the step S202 includes:

[0038] The sampling points are simultaneously converted into 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 the sampling point x rand =(x rand,lon ,x rand,lat ), the sampling point x rand Transformed into the pixel point of the grayscale image F, and at the same time transformed into the avoidance zone set AEBR (PSE), it is determined whether the point is within the obstacle or avoidance zone determined by the avoidance zone set AEBR (PSE); if the sampling point x rand If it is within an obstacle or avoidance zone, repeat this step again.

[0040] Furthermore, the step S204 includes:

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

[0042] 2) Perform collision detection;

[0043] One by one, the longitude and latitude points e i,j ∈e i Convert it into a pixel point in the grayscale image to determine whether there is an obstacle at the pixel point; when e i When there are no obstacles at all points in E i No collision with obstacles; otherwise, a collision occurred;

[0044] Determine connection E i Whether it passes through the avoidance zone, the avoidance zone is the avoidance zone envelope set found by the second search and intersecting with the extended planning range PSE; if E i If no avoidance zone is crossed, then E i It does not collide with the avoidance zone, but instead a collision occurs;

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

[0046] Further, step S208 includes:

[0047] 1) Set the initial point x init and the target point x goal As the focus of the ellipse, and use the half of the shortest path PO searched from the search tree T 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 performed within the ellipse to obtain the sampling point x rand =(x rand,lon ,x rand,lat ), calculate the shortest distance between the sampling point and the obstacle γ=min{D(x rand ,X obs )}, where X obs is the set of all obstacle points within the ellipse sampling range, and the min() function is used to calculate the minimum distance between the sampling point and the obstacle point set;

[0049] 3) Calculate the distance from x on path P0 rand The nearest node x near ; Then in x rand As the center, K points are uniformly selected on the circle with a set length γ as the radius, and recorded as the point set

[0050] 4) Select a point from the K points to replace x near , which can minimize the path cost, and this point is recorded as

[0051] 5) Put the node x on path P0 near Replace 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 is for the path C={x1x2…x n}, further remove redundant path points and 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}, judge x i With x j The connection i Whether j collides with an obstacle;

[0056] Step S303: If x i With a certain x j Collision, then x i With x j All the path points in the middle are removed, and i=j is executed, and step S302 is repeated; if x i With all x j If there is no collision, then x i With x n All the path points in the middle are removed and the process ends, outputting the optimized path C. * .

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

[0058] The present invention discloses a method for UAV path planning in a complex elevation map based on improved RRT. By combining geographic information and avoidance zone data, it is ensured that each path point meets both the avoidance zone requirements and the elevation requirements. The improved RRT algorithm optimizes the search strategy, reduces the amount of calculation, and improves the success rate, convergence efficiency and search efficiency of the algorithm. By backtracking, the path points are further screened, unnecessary path points are removed, the path is simplified, and the planning efficiency is improved. The method is suitable for complex environments such as mountains and jungles, and significantly improves the adaptability and task completion rate of UAVs in complex terrains, while reducing the consumption of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0060] Figure 1 It is a flow chart of a method for UAV path planning in a complex elevation map based on improved RRT in an embodiment of the present invention;

[0061] Figure 2 The figure is a flow chart of improving RRT in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

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

[0064] Step S1, establishing a grayscale map corresponding to the grayscale value and elevation data clipped according to the extended planning range, and an envelope set of avoidance zones intersecting with the extended planning range; the extended planning range includes the planning range and the range of the envelope set of avoidance zones intersecting with the planning range; the planning range is the area determined by the starting point and end point of the UAV path planning;

[0065] Step S2: using an improved fast exploration random tree to perform path planning within an extended planning range; wherein,

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

[0067] In the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the path initial point and the target point as the focus to converge the planned path;

[0068] Step S3, backtracking the planned path outputted in step S2; performing collision detection on the generated path points in reverse order, removing unnecessary path points, and selecting necessary path points to form the planned path.

[0069] Specifically, the step S1 includes:

[0070] Step S101, pre-processing the geographical data of the mission area and the avoidance zone data;

[0071] During each path planning, the geographic elevation data and avoidance zone data required for planning are circled according to the starting and ending points to reduce the amount of planning calculations and increase the planning speed. Before path planning, the geographic elevation data and avoidance zone data need to be preprocessed to meet the information input required for the planning process.

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

[0073] 1) Unified density interpolation processing: Regardless of the density of the original geographic information data, interpolation sampling processing 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 of the grayscale image F corresponds to a latitude and longitude point of the elevation data (lon i ,lat i ,h), the changes of each pixel in the longitude and latitude directions are respectively equal intervals of longitude step value δ lon and latitude step value δ lat ;

[0075] Preprocess all avoidance areas within the mission area. The specific operations are as follows:

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

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

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

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

[0080] 1) Generate a rectangle based on the planned starting point and end point; the minimum value X of the rectangle range min and Y min Take the minimum value of the starting point and the end point, and the maximum value X max and Y max Take the maximum value of the starting point and the end point;

[0081] 2) From the center of the rectangle to X according to the ratio λ min , Y min Direction and X max , Y max The diagonal distance is expanded to λ times of the original to obtain the planning scope PS (Planning Scope);

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

[0083] Step S103, determining an extended planning range and an avoidance area envelope set intersecting with the extended planning range by searching the avoidance area envelope set; the extended planning range includes the planning range and the range of the avoidance area envelope set intersecting with the planning range;

[0084] Specifically, it includes two avoidance zone envelope set searches; wherein,

[0085] First search: Search the avoidance envelope bounding rectangle AEBR (Avoidance Envelope Bounding Rectangle) set in the planning scope PS, and find the avoidance envelope set AEBR (PS) that intersects with the planning scope PS; Take the envelope rectangle of the planning scope PS and the avoidance envelope set AEBR (PS), and record it as the extended planning scope PSE;

[0086] Second search: search the avoidance area envelope rectangle AEBR set again in the extended planning range PSE, and find the avoidance area envelope set AEBR (PSE) intersecting with the extended planning range PSE;

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

[0088] Step S104, according to the extended planning range PSE, the grayscale image corresponding to the grayscale value and the elevation data is clipped to obtain the grayscale image F(PSE) used for planning;

[0089] Specifically, the grayscale image may be generated in the manner of step S101.

[0090] Specifically, the step S2 is as follows: Figure 2 As shown, including:

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

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

[0093] Initialize the search tree T = (V, E), where V = {x init} is the set of nodes in the search tree, which only contains the initial point; is the edge set of the search tree, which is an empty set.

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

[0095] Specifically, the sampling points are simultaneously transformed into 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 the sampling point x rand =(x rand,lon ,x rand,lat ), the sampling point x rand Transformed into the pixel point of the grayscale image F, and at the same time transformed into the avoidance zone set AEBR (PSE), it is determined whether the point is within the obstacle or avoidance zone determined by the avoidance zone set AEBR (PSE); if the sampling point x rand If it is within an obstacle or avoidance zone, repeat this step again.

[0097] Step S203: Sampling point extension: Sampling point x sampled in step S202 rand Find the closest node x in the search tree near , starting from searching for the nearest node x near Start and extend towards the sampling point to get a new node x new ;

[0098] If x rand Not in the obstacle or avoidance zone, calculate the sampling point x rand The latitude and longitude distance D between all nodes in the set V in the search tree is D(x rand ,x i ), where x i ∈V, get the distance from the sampling point x randThe nearest node x near ; From node x near Start with a step length L towards node x rand Extend and generate a new node x new ;

[0099] Extension step length L: This extension step length is the step length in the latitude and longitude coordinate system, which mainly affects the maximum length of the edge in the search tree, that is, the step length in the drone path. The specific value needs to be selected according to the map size and requirements.

[0100] Step S204, collision detection: determine node x near With the new node x new Connection E i Whether there is a collision with an obstacle or an avoidance zone; if yes, the node that collided is discarded and the process returns to step S202; if no, a new node x that does not collide is added. new Add to the node set V and connect E i Add to edge set E;

[0101] Specifically, the step S204 includes:

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

[0103] 2) Perform collision detection;

[0104] One by one, the longitude and latitude points e i,j ∈e i Convert it into a pixel point in the grayscale image to determine whether there is an obstacle at the pixel point; when e i When there are no obstacles at all points in E i No collision with obstacles; otherwise, a collision occurred;

[0105] Determine connection E i Whether it passes through the avoidance zone, the avoidance zone is the avoidance zone envelope set found by the second search and intersecting with the extended planning range PSE; if E i If no avoidance zone is crossed, then E i It does not collide with the avoidance zone, but instead a collision occurs;

[0106] 3) Node x detected by collision near With the new node x new Connection E i If it collides with an obstacle or an avoidance zone, the new node x is discarded. new , and return to step S202 for resampling; if the connection E i If it does not collide with an obstacle or avoidance zone, the new node x new Add to the node set V and connect E i Add 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 Nearby with a 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 for the parent node, M is the number of neighboring nodes found in the tree; select the new node x new and neighboring node X near There are connections with minimum cost and no collision between them, thus optimizing the cost of the generated path;

[0109] The radius range R is set to twice the extension step length, that is, R=2L.

[0110] Step S206, reselecting the parent node in the search tree to rewire the random tree, thereby reducing the connection cost between the random tree nodes;

[0111] For the new node x new After reselecting the parent node, in order to further reduce the connection cost between random tree nodes, it is necessary to rewire the random tree: if the neighboring node X near The parent node is changed to x new If the path cost can be reduced, the change is made; otherwise, no change is made.

[0112] Step S207: Determine the new node x added to the node set V. near With the target point x goal Is the distance greater than the set distance threshold? If yes, return to step S202 to repeat sampling, extension, and collision detection; if no, terminate sampling and set the target point x goal Add to the node set V and search for a shortest path;

[0113] If a new node x is added to the node set near With the target point x goal If the distance between node x and node x is greater than the set latitude and longitude distance threshold δ, the above sampling, extension, and collision detection steps are repeated; if node x near With the target point x near If the distance is less than the threshold δ, the sampling is terminated, and the target point is added to the node set V, and then a shortest path Ρ0 is searched from the tree T;

[0114] Longitude and latitude distance threshold δ: This distance is the distance in the longitude and latitude coordinate system, which is used as a condition 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 searched, otherwise, it has not been searched.

[0115] Step S208: Adaptive path convergence; Adaptive step length is used to start from the path initial point x init and the target point x goal The sampling points are extended within the ellipse as the focus, the planned path is converged, and the planning result is obtained.

[0116] Specifically, the step S208 includes:

[0117] 1) Set the initial point x init and the target point x goal As the focus of the ellipse, and use the half of the shortest path PO searched from the search tree T 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 performed within the ellipse to obtain the sampling point x rand =(x rand,lon ,x rand,lat ), calculate the shortest distance between the sampling point and the obstacle γ=min{D(x rand ,X obs )}, where X obs is the set of all obstacle points within the ellipse sampling range, and the min() function is used to calculate the minimum distance between the sampling point and the obstacle point set;

[0119] 3) Calculate the distance from x on path P0 rand The nearest node x near ; Then in x rand As the center, K points are uniformly selected on the circle with a set length γ as the radius, and recorded as the point set

[0120] 4) Select a point from the K points to replace x near , which can minimize the path cost, and this point is recorded as

[0121] 5) Put the node x on path P0 near Replace 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 will become flatter, thereby concentrating the sampling points near the current path and obtaining a converged path planning result.

[0124] The threshold ΔP is set as the path convergence improvement amount, which is the distance in the longitude and latitude coordinate system. It is used as a condition for judging the 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 path, otherwise it has not reached the optimal path.

[0125] The iteration threshold is set to a large positive integer. When the number of sampling exceeds this value, the program will stop immediately. At this time, 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 n}, further remove redundant path points and 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}, judge x i With x j The connection i Whether j collides with an obstacle;

[0129] Step S303: If x i With a certain x j Collision, then x i With x j All the path points in the middle are removed, and i=j is executed, and step S302 is repeated; if x i With all x j If there is no collision, then x i With x n All the path points in the middle are removed and the process ends, outputting the optimized path C. *.

[0130] In summary, the UAV path planning method in a complex elevation map based on improved RRT disclosed in an embodiment of the present invention ensures that each path point meets both the avoidance zone requirements and the elevation requirements by combining geographic information and avoidance zone data. The improved RRT algorithm optimizes the search strategy, reduces the amount of calculation, and improves the success rate, convergence efficiency, and search efficiency of the algorithm. The backtracking method further screens the path points, removes unnecessary path points, simplifies the path, and improves the planning efficiency. This method is suitable for complex environments such as mountains and jungles, and significantly improves the adaptability and task completion rate of UAVs in complex terrains, while reducing the consumption of computing resources.

[0131] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope 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, establishing a grayscale map corresponding to the grayscale value and elevation data clipped according to the extended planning range, and an envelope set of avoidance areas 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 end point of the UAV path planning; Step S2: using an improved fast exploration random tree to perform path planning within an extended planning range; wherein, In the heuristic path point inspection process of path planning, the generated path points are subjected to avoidance zone envelope inspection and grayscale map elevation inspection, and path points that fail the inspection are removed; In the path convergence process of path planning, an adaptive step size is used to extend the sampling points within the ellipse with the path initial point and the target point as the focus to converge the planned path; Step S3, backtracking the planned path outputted in step S2; performing collision detection on the generated path points in reverse order, removing unnecessary path points, and selecting necessary path points to form the planned path.

2. The method for UAV path planning in complex elevation map based on improved RRT according to claim 1, characterized in that: The step S1 comprises: Step S101, pre-processing the mission area geographic data and avoidance area data; Step S102: select a rectangle including the planning start point and end point, and expand it to obtain the planning area; Step S103, determining an extended planning range and an avoidance area envelope set intersecting with the extended planning range by searching the avoidance area envelope set; the extended planning range includes the planning range and the range of the avoidance area envelope set intersecting with the planning range; Step S104: according to the extended planning range, the grayscale image corresponding to the grayscale value and the elevation data is clipped to obtain the grayscale image used for planning.

3. The method for UAV path planning in complex elevation map based on improved RRT according to claim 2, characterized in that: In step S101, the preprocessing of the geographical data of the mission area including the arrays of longitude, latitude and altitude includes: 1) Unified density interpolation processing: interpolation sampling processing is performed on the original geographic information data according to a unified set density; 2) Generate grayscale image: convert the differenced elevation data into a grayscale image F; each pixel of the grayscale image F corresponds to a longitude and latitude point of the elevation data, and the change of each pixel in the longitude and latitude directions is the longitude step value and latitude step value of equal intervals 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 an envelope rectangle set: Construct the envelope rectangles of all avoidance zones into a set, where each envelope rectangle corresponds to an avoidance zone.

4. The method for UAV path planning in complex elevation map based on improved RRT according to claim 3 is characterized in that: In step S102, the process of planning area expansion includes: 1) Generate a rectangle based on the planned starting point and end point; the minimum value X of the rectangle range min and Y min Take the minimum value of the starting point and the end point, and the maximum value X max and Y max Take the maximum value of the starting point and the end point; 2) According to the ratio λ, from the center of the rectangle to X min , Y min Direction and X max , Y max The planning range PS is obtained by expanding the diagonal distance to λ times of the original one.

5. The method for UAV path planning in complex elevation map based on improved RRT according to claim 4 is characterized in that: Step S103 includes two avoidance area envelope rectangle set searches; First search: search the avoidance zone envelope rectangle AEBR set within the planning scope PS, and find the avoidance zone envelope rectangle set that intersects with the planning scope PS; take the envelope rectangle of the planning scope PS and the avoidance zone envelope rectangle set, and record it as the extended planning scope PSE; Second search: search the avoidance area envelope rectangle AEBR set again within the extended planning range PSE to find the avoidance area envelope rectangle set that intersects with the extended planning range PSE.

6. The method for UAV path planning in complex elevation map based on improved RRT according to any one of claims 1 to 5, characterized in that: The step S2 comprises: Step S201, initialize the target point, task point, and search tree: Step S202: state sampling space sampling; the generated sampling point x rand Conduct avoidance zone and elevation inspections, and resample sampling points that fail the inspections; Step S203: Sampling point extension: Sampling point x sampled in step S202 rand Find the closest node x in the search tree near , starting from searching for the nearest node x near Start and extend towards the sampling point to get a new node x new ; Step S204, collision detection: determine node x near With the new node x new Connection E i Whether there is a collision with an obstacle or an avoidance zone; if yes, the node that collided is discarded and the process returns to step S202; if no, a new node x that does not collide is added. new Add to the node set V and connect E i Add to edge set E; Step S205: add a new node x to the node set V new Reselect the parent node in the search tree: Step S206, reselecting the parent node in the search tree to rewire the random tree, thereby reducing the connection cost between the random tree nodes; Step S207: Determine the new node x added to the node set V. near With the target point x goal Is the distance greater than the set distance threshold? If yes, return to step S202 to repeat sampling, extension, and collision detection; if no, terminate sampling and set the target point x goal Add to the node set V and search for a shortest path; Step S208: Adaptive path convergence; Adaptive step length is used to start from the path initial point x init and the target point x goal The sampling points are extended within the ellipse as the focus, the planned path is converged, and the planning result is obtained.

7. The method for UAV path planning in complex elevation map based on improved RRT according to claim 6, characterized in that: Step S202 includes: The sampling points are simultaneously converted into 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; Random sampling is performed in the state sampling space to obtain the sampling point x rand =(x rand,lon ,x rand,lat ), the sampling point x rand Transformed into the pixel point of the grayscale image F, and at the same time transformed into the avoidance zone set AEBR (PSE), it is determined whether the point is within the obstacle or avoidance zone determined by the avoidance zone set AEBR (PSE); if the sampling point x rand If it is within an obstacle or avoidance zone, repeat this step again.

8. The method for UAV path planning in complex elevation map based on improved RRT according to claim 6, characterized in that: The step S204 includes: 1) For node x near With the new node x new Connection E i Discretize a series of longitude and latitude points e i ={e i,1 ,e i,2 ,…,e i,j ,…,e i,N }, j = 1, ..., N, N is the number of discrete points; e i,j For connection E i The jth discrete longitude and latitude point on the graph; 2) Perform collision detection; One by one, the longitude and latitude points e i,j ∈e i Convert it into a pixel point in the grayscale image to determine whether there is an obstacle at the pixel point; when e i When there are no obstacles at all points in E i No collision with obstacles; otherwise, a collision occurred; Determine connection E i Whether it passes through the avoidance zone, the avoidance zone is the avoidance zone envelope set found by the second search and intersecting with the extended planning range PSE; if E i If no avoidance zone is crossed, then E i It does not collide with the avoidance zone, but instead a collision occurs; 3) Node x detected by collision near With the new node x new Connection E i If it collides with an obstacle or avoidance zone, the new node x is discarded. new , and return to step S202 for resampling; if the connection E i If it does not collide with an obstacle or avoidance zone, the new node x new Add to the node set V and connect E i Add to the edge set E.

9. The method for UAV path planning in complex elevation map based on improved RRT according to claim 6, characterized in that: Step S208 includes: 1) Set the initial point x init and the target point x goal As the focus of the ellipse, and use the half of the shortest path PO searched from the search tree T 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 performed within the ellipse to obtain the sampling point x rand =(x rand,lon ,x rand,lat ), calculate the shortest distance between the sampling point and the obstacle γ=min{D(x rand ,X obs )}, where X obs is the set of all obstacle points within the ellipse sampling range, and the min() function is used to calculate the minimum distance between the sampling point and the obstacle point set; 3) Calculate the distance from x on path P0 rand The nearest node x near ; Then in x rand As the center, K points are uniformly selected on the circle with a set length γ as the radius, and recorded as the point set 4) Select a point from the K points to replace x near , which can minimize the path cost, and this point is recorded as 5) Put the node x on path P0 near Replace 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.

10. The method for UAV path planning in complex elevation map based on improved RRT according to claim 6, characterized in that: Step S3: For the path C obtained in step S2, n }, further remove redundant path points and optimize the path length; the optimization process includes: Step S301, input path C; Step S302: traverse all path points x i ∈C, for each path point x i , traverse all subsequent path points x j ∈{x i+ 1x i+2 …x n }, judge x i With x j The connection ij Whether there is a collision with an obstacle; Step S303: If x i With a certain x j Collision, then x i With x j All the path points in the middle are removed, and i=j is executed, and step S302 is repeated; if x i With all x j If there is no collision, then x i With x n All the path points in the middle are removed and the process ends, outputting the optimized path C. * .

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