CN114527761A - Intelligent automobile local path planning method based on fusion algorithm - Google Patents
Intelligent automobile local path planning method based on fusion algorithm Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
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Abstract
The invention relates to an intelligent automobile local path planning method based on a fusion algorithm, which comprises the steps of acquiring geographic information through a vehicle-mounted sensor and using the geographic information as an input value of an improved A star algorithm module; calculating the geographic information in a global path generation unit through an improved star A algorithm, and outputting the value as global path planning information; calculating global path planning information in a local path generating unit through a lattice algorithm, and outputting a value as local path planning information; and selecting an optimal route from the local path planning information and feeding the optimal route back to the control module. The improved A-star algorithm can eliminate unnecessary points in the classic A-star algorithm so as to improve the operation efficiency, simultaneously considers the direction attribute and the actual motion constraint of an object, optimizes a heuristic function, generates a plurality of candidate paths by sampling, calculates cost functions of different paths by combining other information such as obstacles and the like, and selects a smooth and obstacle-free optimal local path.
Description
Technical Field
The invention belongs to the technical field of intelligent automobile local path planning methods, and particularly relates to an intelligent automobile local path planning method based on a fusion algorithm.
Background
The intellectualization of the automobile is the key point of the transformation of the automobile industry at present, the automatic driving technology of the automobile is one of the important technologies of the intellectualization of the automobile, and the safety of the automatic driving technology is the most important consideration standard of the technology. How to more effectively and stably ensure the safety of the intelligent automobile is a key point for research and concern of industry experts and governments of various countries, and the automatic driving technology is acknowledged as a technology capable of effectively and directly reducing the incidence rate of traffic safety accidents, so that the automatic driving technology becomes a hotspot and a key point for researching the automobile and intelligent traffic field all over the world.
The automatic driving technology of the automobile depends on technologies such as environment perception, planning control and prediction decision, wherein the planning control is a bridge for connecting the external environment perception and the vehicle prediction decision by an intelligent automobile. The planning control technology of the intelligent automobile can realize the functions of active avoidance, automatic navigation and the like of the automobile, and is one of important technologies for automobile intellectualization.
The current path planning method of the intelligent automobile is mainly divided into three categories, namely a planning algorithm based on search, a planning algorithm based on sampling and an algorithm based on reinforcement learning. The most widely used of these is the search-based path planning algorithm. The current major graph search methods include: dijkstra algorithm, Floyd algorithm, a, D, etc. The conventional a algorithm has the following steps:
a) setting an input starting point A, a target point S and a barrier map;
b) establishing an OPEN table and a CLOSE table;
c) an evaluation function f (i) = g (i) + h (i) is established. Wherein i is the ith node in the costmap, the mapping relationship G represents the cost relationship between the function independent variable and the starting point, the step length is set to be 1, and the mapping relationship H represents the cost relationship between the independent variable node and the end point, and can be taken as the distance value between the independent variable node and the end point, namely H (i) = distance (i, T);
d) placing an initial point in an OPEN table;
e) calculating the values of the evaluation functions in the OPEN table, and arranging the values in the order from small to large;
f) if the OPEN table is not empty, the independent variable K = i corresponding to the minimum function value in the table is selectedminOtherwise, the algorithm has no solution;
g) judging the node K: if the target point is not the target point, the algorithm is ended, and if not, the step h is continued;
h) expanding four neighborhood or eight neighborhood nodes of the K node to obtain new four or eight nodes;
i) judging whether the newly obtained four or eight nodes are in the CLOSE table, if so, abandoning the nodes, otherwise, continuing the step j;
j) and calculating the evaluation value of the step point according to an evaluation function F (i) and putting the evaluation value into an OPEN table.
k) The K points and their associated function values are put into the CLOSE table.
l) returning to the step e;
the A star algorithm step is an optimization process and has the characteristics of optimality and completeness, but the A star algorithm step has the following problems: lack of dynamics and because the planning route is all connected by between the node, so caused the planning route not smooth enough, not satisfied vehicle non-integrity constraint.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent automobile local path planning method based on a fusion algorithm, avoid the problem of unnecessary traversal and pinch points in the classic A star algorithm and obtain the effect of selecting a smooth and barrier-free optimal local path.
In order to solve the technical problems, the invention adopts the following technical scheme:
an intelligent automobile local path planning method based on a fusion algorithm comprises the following steps:
s1: acquiring geographic information through a vehicle-mounted sensor, and using the geographic information as an input value of an improved A star algorithm module;
s2: calculating the geographic information in a global path generation unit through an improved star A algorithm, outputting a global path planning information as an output value, and executing S3;
s3: calculating the global path planning information in a local path generating unit through a lattice algorithm, and outputting a value as local path planning information;
s4: selecting an optimal route from the local path planning information and feeding the optimal route back to a control module;
s5: and (6) ending.
Further perfecting the above technical solution, said step S2 further includes:
s2.1: determining and setting a starting point, a target point, obstacle information and a vehicle motion control model of vehicle operation according to the geographic information;
s2.2: establishing an OPEN table and a CLOSE table;
s2.3: establishing an evaluation function F (i) = G (i) + H (i);
wherein i represents the ith node, g (i) represents the cost value from the starting point to the ith node, Hi) represents the cost value from the ith node to the target point;
s2.4: putting a starting point in an OPEN table;
s2.5: unnecessary traversal points are removed from the OPEN table, and the node with the minimum G value is selected as a father node;
s2.6: calculating a cost value G from a father node to a starting point;
s2.7: comparing the cost values of the Reed-Shepp curve, the Dubins curve and the Manhattan distance, taking the maximum value of the Reed-Shepp curve, the Dubins curve and the Manhattan distance as H, and well recording;
s2.8: calculating the minimum value of the evaluation function as F and recording;
s2.9: removing pinch points;
s2.10: judging whether the node corresponding to the minimum value F of the evaluation function is a target point, if so, outputting the node and putting the node into a CLOSE table, and ending; if not, return to step S2.5.
Further, the step S3 further includes:
s3.1: taking the global path planning information output by the star A algorithm after the improvement of the step S2 as an S coordinate axis of a lattice algorithm;
s3.2: decomposing the state of the vehicle in a Frenet coordinate system, and respectively acquiring information of the vehicle in the transverse direction and the longitudinal direction;
in the Frenet coordinate system, s represents the distance along the road and is called as the ordinate, and d represents the displacement from the longitudinal line and is called as the abscissa;
s3.3: respectively carrying out motion planning on the vehicle in the transverse direction and the longitudinal direction according to the information acquired in the step S3.2;
s3.4: synthesizing the transverse planning track and the longitudinal planning track obtained in the step S3.3 into a two-dimensional motion planning track;
s3.5: and (4) screening the two-dimensional motion planning track synthesized in the step (S3.4) according to the set constraint conditions, and selecting the optimal track as an output result.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to an intelligent automobile local path planning method based on a fusion algorithm, which is characterized in that an algorithm part is divided into two parts, wherein the first part is an improved A star algorithm, and the second part is a lattice algorithm operation part; compared with the traditional A star algorithm, the improved A star algorithm deletes unnecessary key points in an OPEN table, improves the budget efficiency, and optimizes the heuristic function of the star algorithm by considering the direction attribute and the actual motion constraint of an object; and then taking the global path planning information generated by the improved A star algorithm as a reference line of a lattice algorithm Frenet coordinate system, sampling to generate a plurality of candidate paths, calculating cost functions of different paths by combining other information such as obstacles and the like, and selecting a smooth and barrier-free optimal local path.
Drawings
FIG. 1 is a system structure diagram of a fusion algorithm of an intelligent vehicle local path planning method based on the fusion algorithm according to an embodiment;
FIG. 2 is a flow chart of a lattice algorithm of the present invention;
FIG. 3 is a flow chart of the improved A-star algorithm of the present invention;
FIG. 4 is a diagram of a trajectory effect planned by a classical A-star algorithm;
FIG. 5 is a diagram of the improved fusion algorithm planning trajectory effect in the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Referring to fig. 1 to fig. 3, an intelligent vehicle local path planning method based on a fusion algorithm in an embodiment includes the following steps:
s1: acquiring geographic information through a vehicle-mounted sensor, and using the geographic information as an input value of an improved A star algorithm module;
s2: calculating the geographic information in a global path generation unit through an improved star A algorithm, outputting a global path planning information as an output value, and executing S3;
s3: calculating the global path planning information in a local path generating unit through a lattice algorithm, and outputting a value as local path planning information;
s4: selecting an optimal route from the local path planning information and feeding the optimal route back to a control module;
s5: and (6) ending.
The intelligent automobile local path planning method based on the fusion algorithm is characterized in that an algorithm part is divided into two parts, wherein the first part is an improved star A algorithm, and the second part is a lattice algorithm operation part; compared with the traditional A star algorithm, the improved A star algorithm deletes unnecessary key points in an OPEN table, improves the budget efficiency, and optimizes the heuristic function of the star algorithm by considering the direction attribute and the actual motion constraint of an object; and then taking the global path planning information generated by the improved A star algorithm as a reference line of a lattice algorithm Frenet coordinate system, sampling to generate a plurality of candidate paths, calculating cost functions of different paths by combining other information such as obstacles and the like, and selecting a smooth and barrier-free optimal local path.
Referring to fig. 4, a diagram of a trajectory effect planned according to a classical a-star algorithm is shown, and fig. 5 is a diagram of a trajectory effect planned according to a fusion algorithm of the present invention. The trajectory drawn by the comparison with the visible fusion law is smoother, and the path planning when meeting the obstacle is more consistent with the constraint of non-integrity of the vehicle.
With continuing reference to fig. 1-3, the step S2 further includes:
s2.1: determining and setting a starting point, a target point, obstacle information and a vehicle motion control model of vehicle operation according to the geographic information;
s2.2: establishing an OPEN table and a CLOSE table;
s2.3: establishing an evaluation function F (i) = G (i) + H (i);
wherein i represents the ith node, g (i) represents the cost value from the starting point to the ith node, Hi) represents the cost value from the ith node to the target point;
s2.4: putting a starting point in an OPEN table;
s2.5: unnecessary traversal points are removed from the OPEN table, and the node with the minimum G value is selected as a father node;
s2.6: calculating a cost value G from a father node to a starting point;
s2.7: comparing the cost values of the Reed-Shepp curve, the Dubins curve and the Manhattan distance, taking the maximum value of the Reed-Shepp curve, the Dubins curve and the Manhattan distance as H, and well recording;
s2.8: calculating the minimum value of the evaluation function as F and recording;
s2.9: removing pinch points;
s2.10: judging whether the node corresponding to the minimum value F of the evaluation function is a target point, if so, outputting the node and putting the node into a CLOSE table, and ending; if not, return to step S2.5.
In implementation, the unnecessary points are divided into unnecessary traversal points and pinch points, wherein the unnecessary traversal points are nodes in paths which are obviously more than one path in the paths, and the pinch points are nodes on the straight line in the path of one straight line; eliminating unnecessary points can save calculation amount and improve calculation efficiency.
Wherein the step S3 further includes:
s3.1: taking the global path planning information output by the star A algorithm after the improvement of the step S2 as an S coordinate axis of a lattice algorithm;
s3.2: decomposing the state of the vehicle in a Frenet coordinate system, and respectively acquiring information of the vehicle in the transverse direction and the longitudinal direction;
in the Frenet coordinate system, s represents the distance along the road and is called as the ordinate, and d represents the displacement from the longitudinal line and is called as the abscissa;
s3.3: respectively carrying out motion planning on the vehicle in the transverse direction and the longitudinal direction according to the information acquired in the step S3.2;
s3.4: synthesizing the transverse planning track and the longitudinal planning track obtained in the step S3.3 into a two-dimensional motion planning track;
s3.5: and (4) screening the two-dimensional motion planning track synthesized in the step (S3.4) according to the set constraint conditions, and selecting the optimal track as an output result.
As the heuristic function is defined by using Manhattan distance and Euclidean distance in the traditional A-star algorithm, the heuristic function is optimized by considering the direction attribute and the actual motion constraint of an object, and the maximum value of the cost of a Reeds-Shepp curve, a Dubins curve and the Manhattan distance is taken as the cost estimation cost value of the A-star; the Reeds-Shepp curve is formed by splicing an arc with a fixed extreme radius and a straight line, and the radius of the arc part is the minimum steering radius of the vehicle; the Dubins curve has one more constraint than the Reeds-Shepp that the vehicle can only drive forward and not back.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (3)
1. An intelligent automobile local path planning method based on a fusion algorithm is characterized in that: the method comprises the following steps:
s1: acquiring geographic information through a vehicle-mounted sensor, and taking the geographic information as an input value of an improved A star algorithm module;
s2: calculating the geographic information in a global path generation unit through an improved star A algorithm, outputting a global path planning information as an output value, and executing S3;
s3: calculating the global path planning information in a local path generating unit through a lattice algorithm, and outputting a value as local path planning information;
s4: selecting an optimal route from the local path planning information and feeding the optimal route back to a control module;
s5: and (6) ending.
2. The intelligent automobile local path planning method based on the fusion algorithm is characterized in that: the step S2 further includes:
s2.1: determining and setting a starting point, a target point, obstacle information and a vehicle motion control model of vehicle operation according to the geographic information;
s2.2: establishing an OPEN table and a CLOSE table;
s2.3: establishing an evaluation function F (i) = G (i) + H (i);
wherein i represents the ith node, g (i) represents the cost value from the starting point to the ith node, Hi) represents the cost value from the ith node to the target point;
s2.4: putting a starting point in an OPEN table;
s2.5: unnecessary traversal points are removed from the OPEN table, and the node with the minimum G value is selected as a father node;
s2.6: calculating a cost value G from a father node to a starting point;
s2.7: comparing the cost values of the Reed-Shepp curve, the Dubins curve and the Manhattan distance, taking the maximum value of the Reed-Shepp curve, the Dubins curve and the Manhattan distance as H, and well recording;
s2.8: calculating the minimum value of the evaluation function as F and recording;
s2.9: removing pinch points;
s2.10: judging whether the node corresponding to the minimum value F of the evaluation function is a target point, if so, outputting the node and putting the node into a CLOSE table, and ending; if not, return to step S2.5.
3. The intelligent automobile local path planning method based on the fusion algorithm is characterized in that: the step S3 further includes:
s3.1: taking the global path planning information output by the star A algorithm after the improvement of the step S2 as an S coordinate axis of a lattice algorithm;
s3.2: decomposing the state of the vehicle in a Frenet coordinate system, and respectively acquiring information of the vehicle in the transverse direction and the longitudinal direction;
in the Frenet coordinate system, s represents the distance along the road and is called as the ordinate, and d represents the displacement from the longitudinal line and is called as the abscissa;
s3.3: respectively carrying out motion planning on the vehicle in the transverse direction and the longitudinal direction according to the information acquired in the step S3.2;
s3.4: synthesizing the transverse planning track and the longitudinal planning track obtained in the step S3.3 into a two-dimensional motion planning track;
s3.5: and (4) screening the two-dimensional motion planning track synthesized in the step (S3.4) according to the set constraint conditions, and selecting the optimal track as an output result.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676939A (en) * | 2022-05-26 | 2022-06-28 | 之江实验室 | Multi-vehicle-type parameter self-adaptive reference line smoothing method and system |
| CN115273457A (en) * | 2022-06-16 | 2022-11-01 | 重庆长安汽车股份有限公司 | Global optimal path planning method considering dynamic change of travel time of urban road network |
| CN115639827A (en) * | 2022-12-22 | 2023-01-24 | 华北科技学院(中国煤矿安全技术培训中心) | Robot path planning method and system in structured environment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333659A (en) * | 2019-07-15 | 2019-10-15 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of pilotless automobile local paths planning method based on improvement A star search |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333659A (en) * | 2019-07-15 | 2019-10-15 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of pilotless automobile local paths planning method based on improvement A star search |
Non-Patent Citations (1)
| Title |
|---|
| 吴正，赵怀林: "基于改进A*算法和lattice算法的路径规划方法", 《计算机应用与软件》, vol. 38, no. 6, 30 June 2021 (2021-06-30), pages 288 - 293 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676939A (en) * | 2022-05-26 | 2022-06-28 | 之江实验室 | Multi-vehicle-type parameter self-adaptive reference line smoothing method and system |
| CN114676939B (en) * | 2022-05-26 | 2022-09-02 | 之江实验室 | Multi-vehicle-type parameter self-adaptive reference line smoothing method and system |
| CN115273457A (en) * | 2022-06-16 | 2022-11-01 | 重庆长安汽车股份有限公司 | Global optimal path planning method considering dynamic change of travel time of urban road network |
| CN115639827A (en) * | 2022-12-22 | 2023-01-24 | 华北科技学院(中国煤矿安全技术培训中心) | Robot path planning method and system in structured environment |
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