CN113286985A - Path planning method and path planning device - Google Patents
Path planning method and path planning device Download PDFInfo
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Abstract
The embodiment of the application provides a path planning method which is applied to the field of intelligent driving. And planning a smooth path which is not limited by the grid through an improved A star algorithm. Specifically, the method determines a next node search area through a current node in the path, determines a plurality of sampling points through sampling in the search area, and then determines the position of the next node from the plurality of sampling points. The method provided by the application can be applied to intelligent automobiles, new energy automobiles and automatic driving automobiles. Because the sampling points in the method are not limited by the grids, the planned target path can meet the dynamic constraint of the target and is smoother compared with the path in the prior art.
Description
Technical Field
The present application relates to the field of intelligent driving, and in particular, to a path planning method and a path planning apparatus.
Background
The intelligent driving technology is the key point for upgrading the automobile industry in the future. The intelligent driving system can be roughly divided into three main modules, namely perception recognition, decision planning and control execution. The intelligent automobile senses and identifies the external environment through sensors such as a radar and a camera. On the basis of multi-information fusion, the intelligent driving system performs corresponding decision planning, including driving task decision, trajectory planning, exception handling and the like. The decision planning result is transmitted to the lower-layer control execution to be realized. In most intelligent driving systems, decision planning is a core module, wherein path planning is an important ring in decision planning.
The search-based path planning method is mostly used for global path planning or driving navigation, and common algorithms include Dijkstra (Dijkstra) algorithm, a Star (a-Star) algorithm, rapid-expansion random tree (RRT) algorithm, and the like. The A-search algorithm is a widely popular heuristic search algorithm, takes map grids as nodes, introduces a cost function on the basis of Dijkstra algorithm, and can quickly and efficiently search out the shortest path from a starting point to an end point.
As shown in fig. 1a and 1b, the conventional a-search algorithm needs to traverse neighboring nodes around the current node, and the movement of the vehicle is restricted by the grid. The intelligent vehicle can only move from one grid center to another grid center in the path planning of each step, and the moving distance and the steering angle are relatively fixed.
Disclosure of Invention
The embodiment of the application provides a path planning method which is used for obtaining a smooth path which is not limited by a grid.
A first aspect of an embodiment of the present application provides a path planning method, including: acquiring the position of the end point of a moving target in a map; determining a search area in the map according to the position of the first node; acquiring a plurality of sampling points in the search area; acquiring a sampling point which enables the cost function to be minimum from the plurality of sampling points as a second node, wherein the second node is the next node of the first node in the target path; and determining the target path according to the position of the end point, the position of the first node and the position of the second node.
In one possible implementation, the first node is a starting point of the target path.
In another possible implementation manner, the first node is an intermediate node of the target path; the method further comprises the following steps: and acquiring the position of the starting point of the moving target in the map, wherein the position of the starting point is used for determining the target path.
The path planning method provided by the embodiment of the application determines the searching area of the next node, namely the second node, based on the position of the first node, namely the current node, and then determines the next node from a plurality of sampling points of the searching area, so that the position of the next node in a target path is not limited by a grid in the traditional A star algorithm, an instantaneous large corner inflection point of the path is avoided, the distance and the course angle between the path nodes in the path acquired according to the method are flexible and variable, the target path is smooth, the path quality is high, and the practicability of the path planning algorithm is improved.
In a possible implementation manner of the first aspect, the determining a search area in the map according to the location of the first node specifically includes: and determining the search area according to the position of the first node and the course angle of the moving target at the position of the first node.
The path planning method provided by the embodiment of the application can also limit the search area to be determined based on the position of the current node and the course angle of the moving target at the current position, and because the vehicle cannot turn around or move laterally in the normal running process of the vehicle, part of the area near the position of the current node does not accord with the vehicle dynamics constraint.
In a possible implementation manner of the first aspect, the method further includes: acquiring dynamic constraint information of the moving target at the position of the first node and/or road information around the position of the first node; the kinetic constraint information comprises at least one of: the speed of the moving target at the position of the first node, and the course angle change rate of the moving target at the position of the first node; the road information includes at least one of: the position of an obstacle around the position of the first node and the position of a road boundary; the determining a search area in the map according to the position of the first node specifically includes: according to the position of the first node and the course angle of the mobile target at the position of the first node, and at least one of the following items: and determining the search area according to the dynamics constraint information and the road information.
According to the path planning method provided by the embodiment of the application, when the search area of the next node is determined, the position of the current node and the course angle of the moving target at the position of the current node are taken into consideration, dynamic constraint information and road information can be taken into consideration, so that the planned search area is more in line with dynamic constraint, obstacles or partial areas outside the road area can be excluded, and the calculation amount is reduced.
In a possible implementation manner of the first aspect, a length of the search area extending in a direction along a heading angle of the moving object is positively correlated with a current speed of the moving object.
According to the path planning method provided by the embodiment of the application, the length of the search area extending in the direction of the course angle of the moving target can be determined according to the current speed of the moving target, the faster the speed of the moving target is, the longer the moving distance in unit time is, and the search area can meet the actual requirement by increasing the length of the search area extending in the direction of the course angle.
In a possible implementation manner of the first aspect, the search area is an area with a position of the first node as a boundary point.
According to the path planning method provided by the embodiment of the application, the search area can be in a fan shape or a triangular shape, the area can be obtained more conveniently based on the preset shape of the search area, the calculation amount of an algorithm is reduced, and the path planning efficiency is improved.
In a possible implementation manner of the first aspect, a search angle of the search area is less than or equal to 180 degrees, the search angle of the search area is an included angle formed by two tangent lines passing through the first node, and the included angle includes a direction of a heading angle of the moving target at the position of the first node.
The path planning method provided by the embodiment of the application further limits the shape of the search area, and considers that the vehicle cannot turn around or move laterally instantly in the normal running process, so that the search angle of the search area is limited, partial areas which are close to the position of the current node and do not accord with vehicle dynamics constraints can be eliminated, the calculated amount can be reduced, and the algorithm performance is improved.
In a possible implementation manner of the first aspect, a size of the search angle of the search area is positively correlated with a heading angle change rate of the mobile target at the position of the first node.
According to the path planning method provided by the embodiment of the application, the size of the search angle of the search area can be determined according to the current course angle change rate of the moving target, and for the condition with a large course angle change rate, such as turning, the search requirement of the target path can be met by increasing the size of the search angle.
In a possible implementation manner of the first aspect, the acquiring a plurality of sample points in the search area specifically includes: acquiring the plurality of sampling points in the search area through random sampling; or, a plurality of sampling points are obtained from the search area in a system sampling mode.
According to the path planning method provided by the embodiment of the application, the method for selecting the sampling points in the search area is various, for example, the sampling points are uniformly selected in a random sampling mode or a system sampling mode, so that the position of the next node in the determined path can get rid of the limitation of a grid in the traditional A star algorithm, the planned target path is smoother, and the path quality is improved.
A second aspect of the embodiments of the present application provides a path planning apparatus, including: an acquisition unit configured to acquire a position of an end point of a moving target in a map; a determination unit configured to determine a search area in the map according to a position of the first node; the acquisition unit is further configured to: acquiring a plurality of sampling points in the search area; the acquisition unit is further configured to: acquiring a sampling point which enables the cost function to be minimum from the plurality of sampling points as a second node, wherein the second node is the next node of the first node in the target path; the determining unit is further configured to determine the target path according to the position of the end point, the position of the first node, and the position of the second node.
In a possible implementation manner of the second aspect, the search area is determined according to the position of the first node and a heading angle of the moving target at the position of the first node.
In a possible implementation manner of the second aspect, the obtaining unit is further configured to: acquiring dynamic constraint information of the moving target at the position of the first node and/or road information around the position of the first node; the kinetic constraint information comprises at least one of: the speed of the moving target at the position of the first node, and the course angle change rate of the moving target at the position of the first node; the road information includes at least one of: the position of an obstacle around the position of the first node and the position of a road boundary; the determining unit is specifically configured to: according to the position of the first node and the course angle of the mobile target at the position of the first node, and at least one of the following items: and determining the search area according to the dynamics constraint information and the road information.
In a possible implementation manner of the second aspect, a length of the search area extending in a direction along a heading angle of the moving object is positively correlated with a current speed of the moving object.
In a possible implementation manner of the second aspect, the search area is a sector or a triangle.
In a possible implementation manner of the second aspect, the search area is an area with a position of the first node as a boundary point.
In a possible implementation manner of the second aspect, a search angle of the search area is less than or equal to 180 degrees, the search angle of the search area is an included angle formed by two tangent lines passing through the first node, and the included angle includes a direction of a heading angle of the moving target at the position of the first node.
In a possible implementation manner of the second aspect, the size of the search angle of the search area is positively correlated with the heading angle change rate of the mobile target at the position of the first node.
In a possible implementation manner of the second aspect, the obtaining unit is specifically configured to: acquiring the plurality of sampling points in the search area through random sampling; or, a plurality of sampling points are obtained from the search area in a system sampling mode.
A third aspect of the embodiments of the present application provides a path planning apparatus, including: one or more processors and memory; wherein the memory has stored therein computer readable instructions; the one or more processors read the computer-readable instructions to cause the terminal to implement the method as described in the first aspect above and any one of various possible implementations.
A fourth aspect of embodiments of the present application provides a computer program product containing instructions, which when run on a computer, causes the computer to perform the method according to the first aspect and any one of the various possible implementations.
A fifth aspect of embodiments of the present application provides a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the method according to any one of the above first aspect and various possible implementations.
A sixth aspect of embodiments of the present application provides a vehicle, including the path planning apparatus according to the second or third aspect and any one of the various possible implementation manners.
A seventh aspect of the present embodiments provides a chip including a processor. The processor is used for reading and executing the computer program stored in the memory so as to execute the method in any possible implementation mode of any one aspect. Optionally, the chip may include a memory, and the memory and the processor may be connected to the memory through a circuit or a wire. Further optionally, the chip further comprises a communication interface, and the processor is connected to the communication interface. The communication interface is used for receiving data and/or information needing to be processed, the processor acquires the data and/or information from the communication interface, processes the data and/or information, and outputs a processing result through the communication interface. The communication interface may be an input output interface.
For technical effects brought by any one implementation manner of the second, third, fourth, fifth, sixth, or seventh aspects, reference may be made to technical effects brought by a corresponding implementation manner in the first aspect, and details are not repeated here.
According to the technical scheme, the embodiment of the application has the following advantages:
according to the path planning method provided by the embodiment of the application, the search area is determined through the current node in the path, the plurality of sampling points are determined through sampling in the search area, then the position of the next node is determined from the plurality of sampling points, and the sampling points are not limited by the grids, so that the planned target path can meet the dynamic constraint of the target, and the path is smoother compared with the path in the prior art.
Drawings
FIG. 1a is a schematic diagram of a conventional A-star algorithm;
FIG. 1b is another schematic diagram of a conventional A-star algorithm;
fig. 2 is an architecture diagram of an application scenario of a path planning method in an embodiment of the present application;
fig. 3a is a schematic diagram of an embodiment of a path planning method in the embodiment of the present application;
FIG. 3b is a flowchart of an algorithm of a path planning method in an embodiment of the present application;
FIG. 4a is a schematic diagram of an embodiment of a search area in an embodiment of the present application;
fig. 4b is a schematic diagram of a path planning simulation effect of the path planning method according to the embodiment of the present application;
fig. 4c is a schematic diagram of a path planning simulation effect obtained based on a conventional a-x search algorithm;
FIG. 5a is a schematic diagram of another embodiment of a search area in an embodiment of the present application;
fig. 5b is another schematic diagram of a path planning simulation effect of the path planning method according to the embodiment of the present application;
fig. 6 is a schematic diagram of an embodiment of a path planning apparatus in an embodiment of the present application;
fig. 7 is a schematic diagram of another embodiment of a path planning apparatus in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a path planning method which is used for obtaining a smooth path which is not limited by a grid.
For the sake of understanding, some technical terms related to the embodiments of the present application are briefly described as follows:
1. open list (Open list): for saving all nodes which have been generated but not examined;
2. close list (Close list): for recording the accessed nodes;
3. the cost function F (n) is a function for calculating the search cost, the obtained value of F (n) represents the importance of the current node, and the smaller the value of F (n), the smaller the cost (the shorter the distance) to the terminal point through the node is, the stronger the importance of the node is. The expression of the cost function of the A star algorithm is as follows: f (n) ═ g (n) + h (n).
Wherein G (n): for indicating the cost that has been paid from the originating node to the current node n, for example: the shortest path value from the starting point to the node n; h (n): for indicating the cost to be paid from the current node n to the end point. For example: the shortest path value from node n to the end point.
The expression of the cost function in the embodiment of the present application may also be expressed as: f ═ G + H.
Optionally, wherein G represents a distance from a sampling point to a last node and a change in a heading angle; h is the shortest path from the sample point to the destination.
4. System sampling: samples are taken from the population according to a sampling distance. Specifically, the sampling method divides the total into a plurality of balanced parts, and then extracts an individual from each part according to a predefined rule to obtain the required sample.
The algorithm a will first be briefly described, with reference to fig. 1 a-1 b:
the A-search algorithm is a widely popular heuristic search algorithm, takes map grids as nodes, introduces a cost function on the basis of Dijkstra algorithm, and can quickly and efficiently search out the shortest path from a starting point to an end point.
The basic steps of the conventional a-search algorithm are as follows:
1. add start to Open list, Close list is initialized to null.
2. It is determined whether the Open list is empty. If so, finishing the algorithm and failing in path planning; if not, the next step is carried out.
3. And taking the node with the minimum cost function value from the Open list as the next exploration node P, and moving the node into the Close list.
4. It is determined whether the node P is the target point (i.e., the end point). If yes, directly ending the step 8; if not, the next step is carried out.
5. And searching adjacent nodes Pi around the node P and executing the step 6.
6. If Pi is not in Close list, calculating the cost function value of the Pi node and adding the cost function value to Open list. And if the Pi node is already in the Open list and the cost function value recorded by the Open list is larger than the current calculated value, updating the information recorded by the Pi node in the Open list.
7. And after all Pi nodes are traversed, returning to the step 2.
8. And searching parent node information in the Close list in the reverse order to obtain a global path from the starting point to the end point.
As shown in fig. 1a, the a-algorithm can not only search for the shortest distance from the starting point to the end point, but also plan the shortest obstacle avoidance path when the obstacle information is known.
As shown in fig. 1b, the conventional a-x algorithm constructs a grid in the map, and nodes in the planned path are all the center points of the grid. When searching for a node of a path terminal, traversing 8 adjacent nodes (1-8 in fig. 1 b) around the current node position from the current node position without considering the driving state of the vehicle, for example, in fig. 1b, the current driving direction of the vehicle is that the vehicle head points to the node 5 in the forward direction, during normal driving, since the vehicle cannot turn around instantaneously or move sideways, the nodes 1, 2, 4, 6 and 7 obviously do not accord with vehicle dynamics constraints, and the one-to-one investigation of the nodes wastes computing resources, and the searching efficiency is low. Furthermore, since the nodes in the path are all the center points of the grid, the resulting path is planned, and in the position shown in fig. 1b, the vehicle is restricted to travel only straight ahead towards node 5, or to travel 45 degrees to the left and forward towards node 3, or to travel 45 degrees to the right and forward towards node 8. It will be appreciated that the resulting path, the track machine, may present a transient large corner inflection point.
In some search-based path planning methods, a vehicle dynamics model is combined to improve a search algorithm, such as a hybrid a star algorithm (hybrid a), a variation of a fast-spanning random tree (RRT) algorithm, and the like. The method adds calculation of a dynamic model in the algorithm flow, can effectively screen out search nodes which accord with vehicle dynamic constraint, and avoids the occurrence of instantaneous large corner inflection points on the vehicle running path. However, in the path search algorithm combined with the vehicle dynamics model, the step of model iterative operation is added, the calculation is complex, and the search efficiency is reduced.
The path planning method provided by the embodiment of the application can be applied to the movement of various moving targets (including motor vehicles, non-motor vehicles, ships, boats, pedestrians or robots and the like) on various paths (including expressways, urban roads, rural roads or indoor paths and the like), such as the route planning of unmanned ships and unmanned yachts and the like. The following embodiments are described by taking the path planning of the vehicle on the road as an example, but those skilled in the art may extend the following embodiments to the field of path planning of other targets, and the details are not limited herein.
With reference to fig. 2, an architecture diagram of an application scenario of the path planning method in the embodiment of the present application briefly introduces a network element related to the present application:
(1) positioning and sensing sensors, such as cameras, lidar, and the like;
(2) vehicle pose sensors, such as Inertial Measurement Units (IMUs), Global Positioning System (GPS), and the like;
(3) a vehicle-mounted gateway, such as a T-BOX (terminal Box) of a front-end loader (T-Box) and the like, is mainly used for communicating with a background system/a mobile phone APP to realize vehicle information display and control of the mobile phone APP;
(4) a vehicle-mounted data arithmetic unit such as a Mobile Data Center (MDC) or the like;
(5) and a cloud data operation unit, such as a cloud server.
The path planning method is realized through a path planning device, and the path planning device can be a vehicle-mounted data operation unit and also can be a cloud data operation unit, and is not limited in the concrete. Optionally, when the vehicle-mounted data operation unit in the vehicle is in shortage of operation resources or breaks down, the path planning algorithm can be switched to the cloud data operation unit for operation, and therefore normal running of the intelligent vehicle is not affected.
Referring to fig. 3a, a path planning method provided in an embodiment of the present application is introduced, where the method includes:
3011. the path planning device acquires the position of the end point of the moving target in the map;
the path planning device can preset a map, or acquire a map of a corresponding geographic position area on line according to the real-time position of the moving target. The specific type of the map is not limited, and the map may be a general map or a high-precision map. It can be understood that the current position of the moving target, the position of the end point and the target path to be planned are all located in the area covered by the map. Before the path planning is performed, the position of the end point of the moving target and the position of the first node are also acquired. The target path is a path that the path planning apparatus desires to obtain, and the first node may be any one of intermediate nodes in the target path or a starting point of the target path. In this embodiment, the first node is taken as an example of the starting point of the target path.
3012. Determining a search area in the map according to the position of the first node;
in the path planning method of the embodiment of the application, the position of the middle node of the path needs to be determined, and then the target path is determined according to the position of the starting point, the position of the middle node, and the position of the end point, and a method for determining the path based on a plurality of nodes in the path, such as a difference method, is the prior art, and is not limited herein. It should be noted that, besides the known position of the start point and the known position of the end point, the path planning apparatus needs to acquire at least one intermediate node in the target path, and it is understood that, generally, to determine the target path, a plurality of intermediate nodes between the start point and the end point need to be searched, and for convenience of description, in this embodiment, according to an order in the target path, each node from the start point is sequentially referred to as a first node and a second node … …, an nth search area of the second node can be determined according to the position of the first node, and so on, until the position of the end point included in the nth search area of the nth node is determined, the search can be ended. The method for determining the search area from the first node to the nth node is similar to the method for determining the search area of the first node, and is not repeated here. The following is a description of the search process of the second node as an example.
The path planning apparatus determines the position of the next node, i.e., the second node, in the path according to the position of the first node, and first determines a search area according to the position of the first node, and the method for determining the search area is described below.
The search area may be determined in a plurality of ways, optionally the path planning means is based on the position (x) of the first node0，y0) And a heading angle (theta) of the moving object at the position of the first node0) A search area is determined. Here, the heading angle of the moving object at the starting position may be a preset value, and the specific direction is not limited. The course angle of the target vehicle at the position of the intermediate node may be calculated based on the position of the previous node and the position of the current node.
Optionally, in addition to the position (x) of the first node0，y0) And the target vehicle is inCourse angle (theta) at position of a node0) The path planning device also determines a search area according to dynamic constraint information of the target vehicle at the current first node, wherein the dynamic constraint information comprises speed, course angle change rate and the like and reflects the motion state of the target vehicle. Alternatively, the speed or the heading angle change rate of the target vehicle may be a preset value.
Optionally, the path planning apparatus further obtains road environment information, such as the position of an obstacle or the position of a road boundary, and the like, and may be used to determine the search area.
Optionally, the search area is an area that uses the position of the first node as a boundary point, a search angle of the search area is less than or equal to 180 degrees, and the search angle of the search area is an included angle that includes the heading angle direction and is formed by two tangent lines that pass through the first node. Optionally, the search area is a predetermined shape, such as a sector or a triangle.
The size of the search area may be a preset size or determined according to the motion state of the target vehicle, and is not limited herein. Optionally, the length of the search area extending in the direction of the heading angle is positively correlated with the current speed of the target vehicle. Optionally, the size of the search angle in the search area is positively correlated with the current heading angle change rate of the target vehicle, and the search angle in the search area is an included angle which is formed by two tangent lines passing through the first node and contains the heading angle direction.
3013. Determining a plurality of sampling points in the search area;
the path planner determines a plurality of sample points in the search area. The number of the sampling points is at least two, the specific numerical value is related to the search area and the search precision, and the specific number is not limited here.
The selection method of the sampling point has multiple kinds, and optionally, the path planning device determines the multiple sampling points in the search area through random sampling; or the path planning device determines a plurality of sampling points for the search area in a system sampling mode. The specific method for selecting the sampling points is not limited herein.
3014. Determining a sampling point which enables the cost function to be minimum from the plurality of sampling points as a first node;
the path planning device traverses the plurality of sampling points Pi, and determines that the central point with the minimum cost function is the first intermediate node. Optionally, the expression of the cost function in the embodiment of the present application may also be expressed as: f ═ G + H. Wherein G represents the distance from the sampling point to the previous node and the change of the course angle; h is the shortest path from the sample point to the destination.
3015. Determining a target path according to the position of the first node, the position of the second node and the position of the end point;
the path planning device determines the target path according to the position of the first node, the position of the second node, and the position of the end point, and it is understood that the number of the first nodes is not limited, and may be one or more.
The path planning method provided by the embodiment of the application specifically comprises the steps of determining a next node search area through a current node in a path, determining a plurality of sampling points through sampling in the search area, and then determining the position of the next node from the plurality of sampling points.
It should be noted that the path planning method provided by the embodiment of the present application is improved based on the conventional a-star algorithm. As shown in fig. 3b, the algorithm flow of the path planning method in the embodiment of the present application includes the following steps:
3021. add the start point to the Open list.
3022. Judging whether the Open list is empty, if yes, executing a step 3023; if not, go to step 3024
3023. If the Open list is empty, the path planning fails, and the program is exited;
3024. and if the Open list is not empty, finding the node with the minimum cost function value from the Open list as the next exploration node P.
3025. Judging whether the node P is an end point, if so, executing a step 3026; if not, go to step 3027.
3026. If the node P is the destination, the parent node information is searched in the Close list in the reverse order, and a global path from the starting point to the destination is obtained.
3027. If the node P is not the end point, determining a search area of the next node, and determining a plurality of sampling points Pi in the search area for traversing.
It should be noted that, if the search area of the next node covers the position of the end point, the end point may be directly used as the unique sampling point, that is, determined as the next node.
3028. If Pi is in Close list, skipping the sample point; and calculating a cost function value of Pi, and adding the information of the sampling point to the Open list.
Optionally, in the algorithm flow of the embodiment of the present application, the auxiliary computation may be performed based on a grid, and the information included in the Open list and the Close list includes: horizontal and vertical coordinates (x) of grid occupied by sampling pointsg，yg) The horizontal and vertical coordinates (x) of the sampling point itselfs，ys) And a heading angle thetasThe horizontal and vertical coordinates (x) of the grid occupied by the father nodegp，ygp) The abscissa and ordinate (x) of the parent node itselfsp，ysp) And a heading angle thetaspAnd a cost function value F. In step 3028, if the grid occupied by the sample point to be examined is already in the Open list and the cost function value recorded in the Open list is greater than the current value, the corresponding sample point information and the cost function value are updated.
The shapes of the search areas in the path planning method according to the embodiment of the present application may be of various types, and a fan shape and a triangle shape are respectively used as an example for description below.
Referring to fig. 4a, fig. 4a shows a sector search area in an embodiment of the present application;
in the embodiment, a sector is taken as a search area, and the circle center of the sector and the current seat of the vehicleThe index coincides with the radius L and the central angle thetamaxThe vehicle dynamic constraint system is related to vehicle pose, vehicle dynamic constraint and road environment information.
1) Determining a search area;
for example, if only the vehicle speed is considered, the position of the sector area may be calculated according to the following formula.
When it is desired to strengthen the vehicle dynamics constraints, for example when the vehicle speed is greater than or equal to a predetermined threshold, L may be increased and θ may be decreasedmax. When the road environment is more complicated, such as more obstacles and larger road curvature, L can be reduced, and theta can be increasedmax。
L＝L0+α*V
θmax＝θmax0-V/β*π
Wherein V is the vehicle speed (m/s); l is0A preset value, for example, a value range of 3 to 5 meters; thetamax0Pi can be taken; alpha is a preset coefficient and the value range is [0, 1 ]]For example 0.5; β is also a predetermined coefficient, and the specific value is not limited, for example, 50.
2) Sampling in a search area to obtain a plurality of sampling points;
the present embodiment performs quantitative sampling in the sector search area, that is, quantitatively dividing the radius and central angle of the sector. At the known current node coordinates (x)0，y0) And heading angle theta0Based on the above formula, the coordinates (x) of the sampling point can be obtained by using the following formulas，ys) And heading angle thetas。
θs＝θ0+Δθ
xs＝x0+l*cosθs
ys＝y0+l*sinθs
Wherein Δ θ is ═ θmax/2，…，-θmax/N1，0，θmax/N1，…，θmax/2；
l＝L/N2，2L/N2，3L/N2… L; and l represents the distance from the current node to the sampling point.
N1Is the number of divisions of the central angle of the sector, N2Is the number of the radius divisions of the sector.
3) Calculating cost functions of a plurality of sampling points;
the cost function value F in this embodiment is calculated by the following formula.
F＝G+H
G＝k1*l+k2*|Δθ|
Wherein k is1And k2Are weight coefficients, are all non-negative numbers and are usually k1＜k2；(xd，yd) Is the endpoint coordinate.
Fig. 4b shows a path planning simulation effect obtained by the path planning method provided in the embodiment of the present application, and fig. 4c shows a path planning simulation effect obtained based on a conventional a-search algorithm;
the path planning method enables the intelligent automobile to smoothly move from the starting point to the end point without exceeding the road boundary or contacting with the obstacle. And the course angle change of the path is more flexible, and simultaneously the method is more consistent with the vehicle dynamics constraint, and many 90-degree folding angles like the traditional A-star search algorithm result do not appear. The path nodes in the result of the embodiment of the present invention are obtained by sampling in the sector search area, and are not limited by the map grid, so the number of the path nodes is much smaller than that of the path nodes of the traditional a-x search.
Referring to fig. 5a, fig. 5a shows a triangular search area in an embodiment of the present application;
in the embodiment of the present application, a search area is taken as an isosceles triangle as an example for explanation, a vertex corresponding to a vertex angle of the isosceles triangle coincides with a current coordinate of a vehicle, and a height L of the vertex passing through the vertex coincides with a vertex angle θmaxThe vehicle dynamic constraint system is related to vehicle pose, vehicle dynamic constraint and road environment information. When it is desired to strengthen the vehicle dynamics constraints, such as at higher vehicle speeds, L may be increased and θ may be decreasedmax. When the road environment is relatively complex, e.g.When the number of obstacles is large and the curvature of the road is large, L can be reduced and theta can be increasedmax。
As shown in fig. 5a, the present embodiment performs random sampling in the triangular search area, and assumes that the sampling points are uniformly distributed in the triangular search area. At the known current node coordinates (x)0，y0) And heading angle theta0Based on the above formula, the coordinates (x) of the sampling point can be obtained by using the following formulas，ys) And heading angle thetas。
θs＝θ0+acrtan(d/l)
xs＝x0+l*cosθs
ys＝y0+l*sinθs
Wherein L is [0, L]Uniformly distributing the upper layer; d is in [ -l tan (theta)max/2)，l*tan(θmax/2)]And uniformly distributed.
Then, according to the coordinates (x) of the sampling points，ys) The corresponding grid coordinates (x) can be found in the mapg，yg)。
The cost function value F in this embodiment is calculated by the following formula.
F＝G+H
G＝k1*l+k2*|d|
Wherein k is1And k2Are weight coefficients, are all non-negative numbers and are usually k1＜k2；(xd，yd) Is the endpoint coordinate.
As shown in fig. 5b, the path planning simulation effect obtained by the path planning method provided by the embodiment of the present application is shown, and compared with the conventional a search algorithm, the path obtained by the path planning method according to the embodiment of the present application is smoother and better conforms to the vehicle dynamics law. Because the path nodes are obtained by random sampling in the triangular search area, the path nodes are not limited by the map grid any more, and the randomness of path search is improved.
The path planning method in the embodiment of the present application is introduced above, and a path planning apparatus for implementing the method is introduced below.
Please refer to fig. 6, which is a diagram illustrating an embodiment of a path planning apparatus according to an embodiment of the present application.
Only one or more of the various modules in fig. 6 may be implemented in software, hardware, firmware, or a combination thereof. The software or firmware includes, but is not limited to, computer program instructions or code and may be executed by a hardware processor. The hardware includes, but is not limited to, various integrated circuits such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or an Application Specific Integrated Circuit (ASIC).
The path planning device comprises:
an acquisition unit 601 configured to acquire a position of an end point of a moving target in a map;
a determining unit 602, configured to determine a search area in the map according to the location of the first node;
the obtaining unit 601 is further configured to: acquiring a plurality of sampling points in the search area;
the obtaining unit 601 is further configured to: acquiring a sampling point which enables the cost function to be minimum from the plurality of sampling points as a second node, wherein the second node is the next node of the first node in the target path;
the determining unit 602 is further configured to determine the target path according to the position of the end point, the position of the first node, and the position of the second node.
Optionally, the determining unit 602 is specifically configured to: and determining the search area according to the position of the first node and the course angle of the moving target at the position of the first node.
Optionally, the obtaining unit 601 is further configured to: acquiring dynamic constraint information of the moving target at the position of the first node and/or road information around the position of the first node; the kinetic constraint information comprises at least one of: the speed of the moving target at the position of the first node, and the course angle change rate of the moving target at the position of the first node; the road information includes at least one of: the position of an obstacle around the position of the first node and the position of a road boundary; the determining unit 602 is specifically configured to: according to the position of the first node and the course angle of the mobile target at the position of the first node, and at least one of the following items: and determining the search area according to the dynamics constraint information and the road information.
Optionally, the length of the search area extending in the direction along the heading angle of the moving object is positively correlated with the current speed of the moving object.
Optionally, the search area is a sector or a triangle.
Optionally, the search area is an area with the position of the first node as a boundary point.
Optionally, a search angle of the search area is less than or equal to 180 degrees, the search angle of the search area is an included angle formed by two tangent lines passing through the first node, and the included angle includes a direction of a heading angle of the moving target at the position of the first node.
Optionally, the size of the search angle of the search area is positively correlated with the heading angle change rate of the mobile target at the position of the first node.
Optionally, the obtaining unit 601 is specifically configured to: acquiring the plurality of sampling points in the search area through random sampling; or, a plurality of sampling points are obtained from the search area in a system sampling mode.
Please refer to fig. 7, which is a schematic diagram of another embodiment of a path planning apparatus according to an embodiment of the present application;
the path planning apparatus provided in this embodiment may be an electronic device such as a server or a terminal, and a specific device form of the path planning apparatus is not limited in this embodiment.
The path planning apparatus 700, which may have large differences due to different configurations or performances, may include one or more processors 701 and a memory 702, where the memory 702 stores programs or data.
The path planner 700 also includes one or more wired or wireless network interfaces 703, such as ethernet interfaces.
Optionally, although not shown in fig. 7, the path planner 700 may also include one or more power supplies; the input/output interface may be used to connect a display, a mouse, a keyboard, a touch screen device, a sensing device, or the like, and the input/output interface is an optional component, and may or may not be present, and is not limited herein.
The process executed by the processor 701 in the path planning apparatus 700 in this embodiment may refer to the method process described in the foregoing method embodiment, which is not described herein again.
Claims (22)
1. A method of path planning, comprising:
acquiring the position of the end point of a moving target in a map;
determining a search area in the map according to the position of the first node;
acquiring a plurality of sampling points in the search area;
acquiring a sampling point which enables the cost function to be minimum from the plurality of sampling points as a second node, wherein the second node is the next node of the first node in the target path;
and determining the target path according to the position of the end point, the position of the first node and the position of the second node.
2. The method of claim 1,
the determining a search area in the map according to the position of the first node specifically includes:
and determining the search area according to the position of the first node and the course angle of the moving target at the position of the first node.
3. The method of claim 2, further comprising:
acquiring dynamic constraint information of the moving target at the position of the first node and/or road information around the position of the first node;
the kinetic constraint information comprises at least one of: the speed of the moving target at the position of the first node, and the course angle change rate of the moving target at the position of the first node;
the road information includes at least one of: the position of an obstacle around the position of the first node and the position of a road boundary;
the determining a search area in the map according to the position of the first node specifically includes:
according to the position of the first node and the course angle of the mobile target at the position of the first node, and at least one of the following items: and determining the search area according to the dynamics constraint information and the road information.
4. The method according to any one of claims 1 to 3, wherein a length of the search area extending in a direction along a heading angle of the moving object is positively correlated with a current speed of the moving object.
5. The method of any one of claims 1 to 4, wherein the search area is a sector or a triangle.
6. The method according to any one of claims 1 to 5, wherein the search area is an area having a position of the first node as a boundary point.
7. The method according to claim 6, wherein the search angle of the search area is less than or equal to 180 degrees, the search angle of the search area is an included angle formed by two tangent lines passing through the first node, and the included angle includes a direction of a heading angle of the moving object at the position of the first node.
8. The method of claim 7, wherein the size of the search angle of the search area is positively correlated to the rate of change of the heading angle of the mobile object at the location of the first node.
9. The method according to any one of claims 1 to 8,
the obtaining of the plurality of sampling points in the search area specifically includes:
acquiring the plurality of sampling points in the search area through random sampling; or,
and obtaining a plurality of sampling points for the search area in a system sampling mode.
10. A path planning apparatus, comprising:
an acquisition unit configured to acquire a position of an end point of a moving target in a map;
a determination unit configured to determine a search area in the map according to a position of the first node;
the acquisition unit is further configured to: acquiring a plurality of sampling points in the search area;
the acquisition unit is further configured to: acquiring a sampling point which enables the cost function to be minimum from the plurality of sampling points as a second node, wherein the second node is the next node of the first node in the target path;
the determining unit is further configured to determine the target path according to the position of the end point, the position of the first node, and the position of the second node.
11. The apparatus according to claim 10, wherein the determining unit is specifically configured to:
and determining the search area according to the position of the first node and the course angle of the moving target at the position of the first node.
12. The apparatus of claim 11, wherein the obtaining unit is further configured to:
acquiring dynamic constraint information of the moving target at the position of the first node and/or road information around the position of the first node;
the kinetic constraint information comprises at least one of: the speed of the moving target at the position of the first node, and the course angle change rate of the moving target at the position of the first node;
the road information includes at least one of: the position of an obstacle around the position of the first node and the position of a road boundary;
the determining unit is specifically configured to: according to the position of the first node and the course angle of the mobile target at the position of the first node, and at least one of the following items: and determining the search area according to the dynamics constraint information and the road information.
13. The apparatus according to any one of claims 10 to 12, wherein a length of the search area extending in a direction along a heading angle of the moving object is positively correlated with a current speed of the moving object.
14. The apparatus of any one of claims 10 to 13, wherein the search area is a sector or a triangle.
15. The apparatus according to any one of claims 10 to 14, wherein the search area is an area having a position of the first node as a boundary point.
16. The apparatus of claim 15, wherein the search angle of the search area is less than or equal to 180 degrees, and the search angle of the search area is an included angle formed by two tangent lines passing through the first node, and the included angle includes a direction of a heading angle of the moving object at the position of the first node.
17. The apparatus of claim 16, wherein the size of the search angle of the search area is positively correlated to the rate of change of the heading angle of the moving object at the location of the first node.
18. The apparatus of any one of claims 10 to 17,
the obtaining unit is specifically configured to:
acquiring the plurality of sampling points in the search area through random sampling; or,
and obtaining a plurality of sampling points for the search area in a system sampling mode.
19. A path planning apparatus, comprising: one or more processors and memory; wherein,
the memory has stored therein computer readable instructions;
the one or more processors read the computer-readable instructions to cause the terminal to implement the method of any of claims 1-9.
20. A computer program product comprising computer readable instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 9.
21. A computer readable storage medium comprising computer readable instructions which, when run on a computer, cause the computer to perform the method of any of claims 1 to 9.
22. A vehicle comprising a path planner according to any of claims 10 to 18 or a path planner according to claim 19.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114111788A (en) * | 2021-11-09 | 2022-03-01 | 武汉乐庭软件技术有限公司 | Trajectory planning method and device based on multi-segment clothoid and storage device |
| CN114577217A (en) * | 2022-05-05 | 2022-06-03 | 季华实验室 | Route planning method, device, equipment and storage medium based on Von Lonouh graph |
| CN114815845A (en) * | 2022-05-10 | 2022-07-29 | 山东省计算中心（国家超级计算济南中心） | Automatic driving agricultural machinery smooth path planning method based on hybrid A-x algorithm |
| CN116124162A (en) * | 2022-12-28 | 2023-05-16 | 北京理工大学 | Park trolley navigation method based on high-precision map |
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844364A (en) * | 2016-04-08 | 2016-08-10 | 上海派毅智能科技有限公司 | Service robot optimal path program method based on heuristic function |
| CN105955280A (en) * | 2016-07-19 | 2016-09-21 | Tcl集团股份有限公司 | Mobile robot path planning and obstacle avoidance method and system |
| CN106989748A (en) * | 2017-05-16 | 2017-07-28 | 南京农业大学 | A kind of Agriculture Mobile Robot man-computer cooperation paths planning method based on cloud model |
| CN108073176A (en) * | 2018-02-10 | 2018-05-25 | 西安交通大学 | A kind of modified D*Lite vehicle dynamic path planing methods |
| CN108253976A (en) * | 2018-01-04 | 2018-07-06 | 重庆大学 | It is a kind of fully by the three stage Online Map matching algorithms in vehicle course |
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| CN108896052A (en) * | 2018-09-20 | 2018-11-27 | 鲁东大学 | A kind of mobile robot smooth paths planing method under the environment based on DYNAMIC COMPLEX |
| CN108983781A (en) * | 2018-07-25 | 2018-12-11 | 北京理工大学 | A kind of environment detection method in unmanned vehicle target acquisition system |
| WO2019042295A1 (en) * | 2017-08-31 | 2019-03-07 | 广州小鹏汽车科技有限公司 | Path planning method, system, and device for autonomous driving |
| CN110081894A (en) * | 2019-04-25 | 2019-08-02 | 同济大学 | A kind of real-time planing method of unmanned wheel paths based on the fusion of road structure weight |
| CN110426053A (en) * | 2019-07-12 | 2019-11-08 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method and mobile robot |
| CN111369066A (en) * | 2020-03-09 | 2020-07-03 | 广东南方数码科技股份有限公司 | Path planning method and device, electronic equipment and readable storage medium |
| CN111422741A (en) * | 2020-03-24 | 2020-07-17 | 苏州西弗智能科技有限公司 | Method for planning movement path of bridge crane |
| DE102019134487A1 (en) * | 2019-01-28 | 2020-07-30 | GM Global Technology Operations LLC | SYSTEM AND METHOD OF AN ALGORITHMIC SOLUTION FOR GENERATING A SMOOTH VEHICLE SPEED PROJECT FOR AN AUTONOMOUS VEHICLE WITH SPATIAL SPEED LIMITS |
| CN111504325A (en) * | 2020-04-29 | 2020-08-07 | 南京大学 | Global path planning method based on weighted A-algorithm for expanding search neighborhood |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE4415497C1 (en) * | 1994-05-03 | 1995-05-24 | Siemens Ag | Path finding procedure for mobile robot |
-
2020
- 2020-09-17 WO PCT/CN2020/115825 patent/WO2022056770A1/en active Application Filing
- 2020-09-17 CN CN202080006453.9A patent/CN113286985A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844364A (en) * | 2016-04-08 | 2016-08-10 | 上海派毅智能科技有限公司 | Service robot optimal path program method based on heuristic function |
| CN105955280A (en) * | 2016-07-19 | 2016-09-21 | Tcl集团股份有限公司 | Mobile robot path planning and obstacle avoidance method and system |
| CN106989748A (en) * | 2017-05-16 | 2017-07-28 | 南京农业大学 | A kind of Agriculture Mobile Robot man-computer cooperation paths planning method based on cloud model |
| WO2019042295A1 (en) * | 2017-08-31 | 2019-03-07 | 广州小鹏汽车科技有限公司 | Path planning method, system, and device for autonomous driving |
| CN108253976A (en) * | 2018-01-04 | 2018-07-06 | 重庆大学 | It is a kind of fully by the three stage Online Map matching algorithms in vehicle course |
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| CN108073176A (en) * | 2018-02-10 | 2018-05-25 | 西安交通大学 | A kind of modified D*Lite vehicle dynamic path planing methods |
| CN108983781A (en) * | 2018-07-25 | 2018-12-11 | 北京理工大学 | A kind of environment detection method in unmanned vehicle target acquisition system |
| CN108896052A (en) * | 2018-09-20 | 2018-11-27 | 鲁东大学 | A kind of mobile robot smooth paths planing method under the environment based on DYNAMIC COMPLEX |
| DE102019134487A1 (en) * | 2019-01-28 | 2020-07-30 | GM Global Technology Operations LLC | SYSTEM AND METHOD OF AN ALGORITHMIC SOLUTION FOR GENERATING A SMOOTH VEHICLE SPEED PROJECT FOR AN AUTONOMOUS VEHICLE WITH SPATIAL SPEED LIMITS |
| CN110081894A (en) * | 2019-04-25 | 2019-08-02 | 同济大学 | A kind of real-time planing method of unmanned wheel paths based on the fusion of road structure weight |
| CN110426053A (en) * | 2019-07-12 | 2019-11-08 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method and mobile robot |
| CN111369066A (en) * | 2020-03-09 | 2020-07-03 | 广东南方数码科技股份有限公司 | Path planning method and device, electronic equipment and readable storage medium |
| CN111422741A (en) * | 2020-03-24 | 2020-07-17 | 苏州西弗智能科技有限公司 | Method for planning movement path of bridge crane |
| CN111504325A (en) * | 2020-04-29 | 2020-08-07 | 南京大学 | Global path planning method based on weighted A-algorithm for expanding search neighborhood |
Non-Patent Citations (1)
| Title |
|---|
| 陈荣军等: "基于改进A~*算法的无人配送车避障最优路径规划", 《广东技术师范大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114111788A (en) * | 2021-11-09 | 2022-03-01 | 武汉乐庭软件技术有限公司 | Trajectory planning method and device based on multi-segment clothoid and storage device |
| CN114577217A (en) * | 2022-05-05 | 2022-06-03 | 季华实验室 | Route planning method, device, equipment and storage medium based on Von Lonouh graph |
| CN114577217B (en) * | 2022-05-05 | 2022-07-29 | 季华实验室 | Route planning method, device, equipment and storage medium based on Von Lonouh graph |
| CN114815845A (en) * | 2022-05-10 | 2022-07-29 | 山东省计算中心（国家超级计算济南中心） | Automatic driving agricultural machinery smooth path planning method based on hybrid A-x algorithm |
| CN116124162A (en) * | 2022-12-28 | 2023-05-16 | 北京理工大学 | Park trolley navigation method based on high-precision map |
| CN116124162B (en) * | 2022-12-28 | 2024-03-26 | 北京理工大学 | Park trolley navigation method based on high-precision map |
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