CN113515128B - Unmanned vehicle real-time path planning method and storage medium - Google Patents
Unmanned vehicle real-time path planning method and storage medium Download PDFInfo
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- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
- G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
Abstract
The application discloses a real-time path planning method for an unmanned vehicle and a storage medium. The unmanned vehicle real-time path planning method comprises the following steps: the method comprises the steps of unmanned vehicle parking position attitude estimation and point cloud map construction, wherein a laser radar is operated by an unmanned vehicle to obtain a sparse point cloud map of surrounding terrain scenes, the point cloud map is matched to construct a point cloud map, and meanwhile six-dimensional attitude estimation of the unmanned vehicle is obtained; constructing a local octree map, namely performing down-sampling treatment on point cloud data after a sparse point cloud map is obtained, and forming the local octree map by taking an unmanned vehicle as a center; constructing a two-dimensional grid map, namely performing two-dimensional projection on the octree map to construct the two-dimensional grid map, wherein the two-dimensional grid map has elevation information; and planning a navigation path, namely selecting a route with the minimum height change on the two-dimensional grid map as the navigation path according to the elevation information. The method avoids the problems that the unmanned vehicle is trapped and stops when the unmanned vehicle runs on a rugged and complex path.
Description
Technical Field
The invention belongs to the field of field unmanned vehicle navigation and exploration, and particularly relates to unmanned vehicle environment perception unmanned vehicle path planning and unmanned vehicle navigation technologies, in particular to an unmanned vehicle real-time path planning method and a storage medium.
Background
In recent years, positioning and navigation techniques for unmanned vehicles have achieved significant success in structured environments. However, as the application requirements of the unmanned vehicle in the field environment are more and more, the problems of the unmanned vehicle environment sensing technology are exposed. How to achieve autonomous navigation and exploration of unmanned vehicles in unstructured environments without GPS, landmarks and artificial landmarks remains a challenge, for example, in field search and rescue, environmental detection and agricultural plant protection processes. These tasks need to involve various techniques, including unmanned vehicle active environment map construction, autonomous pose estimation and navigation, etc.
The existing navigation planning of the unmanned vehicle implements path planning based on terrain information of a two-dimensional space, but under the condition of severe environment, images of the two-dimensional space, such as potholes and slope positions of a road surface, cannot be clearly shot and obtained, so that the path planning mode easily causes the unmanned vehicle to travel on a rugged and complex path, and even causes the unmanned vehicle to be trapped and parked in an anchoring way.
Disclosure of Invention
The invention aims to provide a real-time path planning method and a storage medium for an unmanned vehicle, which expand the navigation planning of the unmanned vehicle from a two-dimensional space to a three-dimensional space by utilizing three-dimensional terrain information, realize the expansion of the navigation scene of the unmanned vehicle from a flat terrain to a rugged and complex field terrain with pits and slopes, and solve the technical problems that the implementation of the path planning based on the terrain information of the two-dimensional space easily causes the unmanned vehicle to run on the rugged and complex path, and even causes the unmanned vehicle to be trapped and break down.
In order to achieve the above object, an embodiment of the present invention provides a method for planning a real-time path of an unmanned vehicle, including the following steps:
the method comprises the steps of unmanned parking space attitude estimation and point cloud map construction, wherein an unmanned vehicle runs a laser radar to obtain a sparse point cloud map of surrounding terrain scenes, the point cloud map is matched to construct a point cloud map, and meanwhile six-dimensional attitude estimation of the unmanned vehicle is obtained;
constructing a local octree map, namely performing downsampling processing on point cloud data after a sparse point cloud map is obtained, and forming the local octree map by taking an unmanned vehicle as a center;
constructing a two-dimensional grid map, namely performing two-dimensional projection on the octree map to construct the two-dimensional grid map, wherein the two-dimensional grid map has elevation information; and
and planning a navigation path, namely selecting a route with the minimum height change on the two-dimensional grid map as the navigation path according to the elevation information.
Furthermore, the laser radar comprises a plurality of laser sensors, and the laser sensors select the terrain features to form a point cloud picture by acquiring a plurality of features of surrounding terrain scenes.
Further, the unmanned vehicle is provided with a wheel type odometer sensor, and the wheel type odometer sensor assists in positioning and calculates motion compensation to form a point cloud picture.
Further, a point cloud map coordinate system is constructed based on the position of the center of mass when the unmanned vehicle is started, the coordinate system comprises coordinate axes in the x direction, the y direction and the z direction, the x direction and the y direction are located on the horizontal plane, the z direction is perpendicular to the horizontal plane, and the elevation information is the altitude in the z direction.
Further, the step of planning the navigation path comprises: forming a navigation grid map, namely performing expansion processing on the two-dimensional grid map to form the navigation grid map; forming a terrain obstacle expanded area, namely forming an expanded area from an area with the altitude within a first threshold range in the elevation information, and forming a plurality of expanded areas according to different areas with the altitude within the first threshold range; and selecting a navigation path, planning a plurality of routes based on the expansion area where the wheel of the unmanned vehicle is located at the altitude, and calculating the route with the minimum height change of all the expansion areas in the routes as the navigation path.
Further, the expansion area comprises a ground area, a terrain concave area and a terrain convex area; the ground area is an area where the altitude of wheels of the unmanned vehicle is within a first threshold range; the elevation of the terrain depressed area is lower than the elevation of the ground area; the elevation of the terrain convex area is higher than the elevation of the ground area; each route comprises a plurality of ground areas, a plurality of terrain concave areas and a plurality of terrain convex areas, and the route with the smallest difference between the altitude of the terrain concave areas and the altitude of the terrain convex areas and the altitude of the ground areas and the smallest number of the terrain concave areas and the terrain convex areas is selected as a navigation path.
Further, the ground area, the terrain depressed area and the terrain raised area are filled with different colors.
And further, planning a navigation path from the starting point to the end point on the two-dimensional grid map by taking the position of the unmanned vehicle as the starting point and taking a given node of the global map as the end point.
Further, for altitude height values of three adjacent point clouds in the octree map, the altitude information is obtained by calculating the average of the vertexes of the triangle in a triangular mode.
The application further provides a storage medium, where multiple instructions are stored, where the instructions are suitable for being loaded by a processor to perform the steps in the unmanned vehicle real-time path planning method described in any one of the foregoing.
The unmanned vehicle real-time path planning method and the storage medium have the advantages that the performance limit of the unmanned vehicle is taken care of on the basis of the map based on the octree diagram, the data size is reduced on the basis of ensuring the sampling feasibility, the ground constraint condition from the elevation information is added in the sampling process, the ground state mapping is realized, the navigation scene of the unmanned vehicle is expanded from the flat terrain to the rugged and complex three-dimensional terrain with pits and slopes, the rapid path searching is carried out by adopting the path planning algorithm based on the elevation information, the optimal navigation path is generated, and the method is different from other RRT navigation and exploration methods, so that the problems that the unmanned vehicle is trapped and stops without anchor when the unmanned vehicle runs on the rugged and complex path are avoided.
Drawings
The following detailed description of the embodiments of the present application, taken in conjunction with the accompanying drawings, presents the technical solutions and other advantages of the present application.
Fig. 1 is a flowchart of a method for planning a real-time path of an unmanned vehicle according to an embodiment of the present application.
Fig. 2 is a logic diagram of a method for planning a real-time path of an unmanned vehicle according to an embodiment of the present application.
Fig. 3 is a logic diagram of the steps of unmanned parking stall pose estimation and point cloud map construction provided in the embodiment of the present application.
Fig. 4 is a logic diagram of the steps of constructing the two-dimensional grid map and planning the navigation path according to the embodiment of the present application.
Fig. 5 is a two-dimensional grid map manufactured based on octree map projection in the step of constructing a two-dimensional grid map according to the embodiment of the present application.
Fig. 6 is a schematic global path diagram after a navigation path is planned according to an embodiment of the present application.
Fig. 7 is a flowchart of a step of planning a navigation path according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Specifically, referring to fig. 1 and 2, an embodiment of the present application provides a method for planning a real-time path of an unmanned vehicle, including the following steps S1 to S4.
S1, unmanned vehicle parking position attitude estimation and point cloud map construction, wherein the unmanned vehicle runs a laser radar to obtain a sparse point cloud map of surrounding terrain scenes, the point cloud map is matched to construct the point cloud map, and meanwhile six-dimensional attitude estimation of the unmanned vehicle is obtained. Referring to fig. 3, the lidar includes a plurality of laser sensors, and the laser sensors acquire a plurality of features of surrounding terrain scenes and select terrain features to form a point cloud chart. The unmanned vehicle is provided with a wheel type odometer sensor, and a point cloud picture is formed by auxiliary positioning and motion compensation of the wheel type odometer sensor. The unmanned vehicle integrates the laser radar and the wheel-type odometer sensor, self pose estimation and positioning are achieved, and a sparse point cloud map of the surrounding environment is established in real time based on laser radar data.
S2, constructing a local octree map, namely performing downsampling processing on point cloud data after the sparse point cloud map is obtained, and forming the local octree map by taking an unmanned vehicle as a center. To reduce the interference of the on-site environment with the point cloud and the huge computational cost of navigating directly on the point cloud map, an explicit map representation of octree is used. The method comprises the steps of adding point clouds in a certain range with the position of a vehicle as the center into an octree map, and maintaining and updating an octree map thought diagram, so that the map maintenance cost of the vehicle in the real-time navigation field is low. And after the sparse point cloud map is obtained, performing down-sampling processing on the point cloud data. The purpose of this step is to take care of the unmanned vehicle's performance restriction, reduce the data size on the basis of guaranteeing the sampling feasibility.
And S3, constructing a two-dimensional grid map, namely performing two-dimensional projection on the octree map to construct the two-dimensional grid map, wherein the two-dimensional grid map has elevation information. Referring to fig. 4 and 5, an incremental octree map is generated according to the point cloud map, the environment around the unmanned vehicle within a certain diameter range is updated in real time, and finally, the octree map is subjected to two-dimensional projection to form a grid map for navigation. And extracting elevation information on the octree map, and setting an impassable area through flatness calculation and the like. The method comprises the steps of constructing a point cloud map coordinate system based on the position of a mass center when the unmanned vehicle is started, wherein the coordinate system comprises coordinate axes in the x direction, the y direction and the z direction, the x direction and the y direction are located on a horizontal plane, the z direction is perpendicular to the horizontal plane, and the elevation information is the altitude in the z direction. And obtaining the elevation information by calculating the average of the normal lines of the vertexes of the triangles in a triangular mode according to the altitude values of the three adjacent point clouds in the octree map. Conventional methods of processing surfaces defined by height maps such as topographical maps typically average the triangle normals in a triangular fashion and average all adjacent surface normals, which is very slow in overall computation, especially for larger height maps. Here we compute the computation based only on the height map computing vertex normals, similar to the way the height map is converted to a normal map. For each vertex normal, 4 samples are taken, namely the four positions left, right, top, and bottom of the current height map pixel, to obtain the average slope in the x and z directions. This step will yield two 2D vertex normals (one in the x-direction and one in the z-direction). Finally, only one 3D vector projected onto the x =0, z =0 plane needs to be found and normalized to the same slope as the 2D normal, and the calculation speed of the method is tested to be faster than that of the conventional method.
And S4, planning a navigation path, namely selecting a route with the minimum height change on the two-dimensional grid map as the navigation path according to the elevation information. Referring to fig. 4 and 6, on the basis of the established complex terrain grid map, a path planning algorithm based on elevation information is adopted to perform fast path search by taking the map itself as a starting point and taking a node given by a global map as an end point, so as to generate an optimal navigation path. Wherein the dashed box represents a local octree map.
In this embodiment, referring to fig. 7, the step S4 of planning the navigation path includes: s41, forming a navigation grid map, namely expanding the two-dimensional grid map to form the navigation grid map; s42, forming a terrain obstacle expansion area, namely forming an expansion area from an area with the altitude in the altitude information within a first threshold range, and forming a plurality of expansion areas according to different areas with the altitude within the first threshold range; s43, a step of selecting a navigation path, which is to plan a plurality of routes based on the expansion area where the height of the unmanned vehicle wheels is located, and calculate all routes with the minimum height change in the expansion area as the navigation path.
In this embodiment, the expansion area comprises a ground area, a terrain depressed area, and a terrain raised area; the ground area is an area where the altitude of wheels of the unmanned vehicle is within a first threshold range; the elevation of the terrain-depressed area is lower than the elevation of the ground area; the elevation of the terrain convex area is higher than the elevation of the ground area; each route comprises a plurality of ground areas, a plurality of terrain concave areas and a plurality of terrain convex areas, and the route with the smallest difference between the altitude of the terrain concave areas and the altitude of the terrain convex areas and the altitude of the ground areas and the smallest number of the terrain concave areas and the terrain convex areas is selected as a navigation path.
In this embodiment, the ground area, the terrain concave area and the terrain convex area are filled with different colors.
In this embodiment, a navigation path is planned from the start point to the end point on the two-dimensional grid map by using the position of the unmanned vehicle as the start point and using a node given by the global map as the end point.
The present application further provides a storage medium storing a plurality of instructions, the instructions being adapted to be loaded by a processor to perform the steps of the real-time path planning method for an unmanned vehicle according to any of the above.
The unmanned vehicle real-time path planning method and the storage medium have the advantages that the performance limit of the unmanned vehicle is taken care of on the basis of the map based on the octree diagram, the data size is reduced on the basis of ensuring the sampling feasibility, the ground constraint condition from the elevation information is added in the sampling process, the ground state mapping is realized, the navigation scene of the unmanned vehicle is expanded from the flat terrain to the rugged and complex three-dimensional terrain with pits and slopes, the rapid path searching is carried out by adopting the path planning algorithm based on the elevation information, the optimal navigation path is generated, and the method is different from other RRT navigation and exploration methods, so that the problems that the unmanned vehicle is trapped and stops without anchor when the unmanned vehicle runs on the rugged and complex path are avoided.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
The unmanned vehicle real-time path planning method and the storage medium provided by the embodiments of the present application are introduced in detail, and specific examples are applied in the present application to explain the principles and implementation of the present application, and the description of the embodiments is only used to help understand the technical solutions and core ideas of the present application; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.
Claims (9)
1. A real-time path planning method for an unmanned vehicle is characterized by comprising the following steps:
the method comprises the steps of unmanned parking space attitude estimation and point cloud map construction, wherein an unmanned vehicle runs a laser radar to obtain a sparse point cloud map of surrounding terrain scenes, the point cloud map is matched to construct a point cloud map, and meanwhile six-dimensional attitude estimation of the unmanned vehicle is obtained;
constructing a local octree map, namely performing downsampling processing on point cloud data after a sparse point cloud map is obtained, and forming the local octree map by taking an unmanned vehicle as a center;
constructing a two-dimensional grid map, namely performing two-dimensional projection on the octree map to construct the two-dimensional grid map, wherein the two-dimensional grid map has elevation information; and
planning a navigation path, namely selecting a route with the minimum height change on the two-dimensional grid map as the navigation path according to the elevation information;
for altitude height values of three adjacent point clouds in the octree map, the altitude information is obtained by triangulating the average of triangle vertex normals, computing vertex normals based only on the height map, for each vertex normal, 4 samples are taken, namely, the left, right, top, and bottom four positions of the current height map pixel, to obtain the average slope in x and z directions, this step will generate two 2D vertex normals, one in x direction and one in z direction, and finally find a 3D vector projected to the x =0, z =0 plane and normalize it to the same slope as the 2D normals.
2. The unmanned vehicle real-time path planning method of claim 1, wherein the lidar comprises a plurality of laser sensors, and the laser sensors select terrain features to form a point cloud map by acquiring a plurality of features of a surrounding terrain scene.
3. The unmanned vehicle real-time path planning method according to claim 1, wherein the unmanned vehicle is provided with a wheel type odometer sensor, and the wheel type odometer sensor is used for assisting positioning and calculating motion compensation to form a point cloud picture.
4. The unmanned aerial vehicle real-time path planning method of claim 1, wherein a point cloud map coordinate system is constructed based on a position of a centroid when the unmanned aerial vehicle is started, the coordinate system comprises coordinate axes in an x direction, a y direction and a z direction, the x direction and the y direction are located on a horizontal plane, the z direction is perpendicular to the horizontal plane, and the elevation information is an altitude in the z direction.
5. The unmanned vehicle real-time path planning method according to claim 1, wherein the step of planning the navigation path comprises:
forming a navigation grid map, namely performing expansion processing on the two-dimensional grid map to form the navigation grid map;
forming a terrain obstacle expanded area, wherein an area with the altitude within a first threshold range in the altitude information forms an expanded area, and a plurality of expanded areas are formed according to different areas with the altitude within the first threshold range; and
and selecting a navigation path, planning a plurality of routes based on the expansion area where the vehicle wheels of the unmanned vehicle are located at the altitude, and calculating the route with the minimum height change of all the expansion areas in the routes as the navigation path.
6. The unmanned vehicle real-time path planning method of claim 5, wherein the expansion area comprises a ground area, a terrain concave area and a terrain convex area; the ground area is an area where the altitude of wheels of the unmanned vehicle is in a first threshold range; the elevation of the terrain-depressed area is lower than the elevation of the ground area; the elevation of the terrain convex area is higher than the elevation of the ground area; each route comprises a plurality of ground areas, a plurality of terrain concave areas and a plurality of terrain convex areas, and the route with the smallest difference between the altitude of the terrain concave areas and the altitude of the terrain convex areas and the altitude of the ground areas and the smallest number of the terrain concave areas and the terrain convex areas is selected as a navigation path.
7. The method of real-time unmanned vehicle path planning according to claim 6, wherein the ground area, the terrain concave area and the terrain convex area are filled with different colors.
8. The unmanned vehicle real-time path planning method according to claim 1, wherein a navigation path is planned from a starting point to an end point on the two-dimensional grid map by taking a position of the unmanned vehicle as a starting point and a given node of the global map as an end point.
9. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the method for real-time path planning for unmanned vehicles according to any of claims 1-8.
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