CN113985892A - Intelligent ship path planning method based on improved Ao algorithm - Google Patents
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Abstract
The invention provides an intelligent ship path planning method based on an improved A-fold algorithm, which comprises the steps of firstly establishing a grid map, then reducing a search direction according to an included angle theta, then judging an extension point and storing a search path, setting a current node as a father node, then selecting a new node according to an improved evaluation function, confirming whether an Open linked list is an empty list, and repeating the steps until an optimal path is obtained; compared with the traditional A-x algorithm, the speed of searching the path can be improved in a simple map with fewer obstacles; on the other hand, in a complex map with many obstacles, the obstacle ratio Pz increases,
the search precision is improved at the moment when the search precision is relatively reduced. The intelligent ship path planning method based on the improved A-fold algorithm has the advantages of ensuring the flexibility and the rapidity of the algorithm and improving the searching efficiency in a self-adaptive manner according to the complexity of a map.
Description
Technical Field
The invention relates to the technical field of ship path planning, in particular to an intelligent ship path planning method based on an improved A-fold algorithm.
Background
The intelligent unmanned ship is an unmanned ship and has an autonomous navigation, an intelligent engine room, energy efficiency management, cargo transportation and an intelligent integrated platform; compared with the traditional driving mode of manual control, the autonomous navigation of the ship has the advantages of high safety factor, economy, environmental protection, energy conservation, high automation degree and the like, and the core of the autonomous navigation of the ship is autonomous obstacle avoidance path planning; in the autonomous navigation decision system of the ship, the main task is that when the environment with obstacles or the navigation environment changes, the ship needs to temporarily adjust a local route according to the navigation environment, and searches for a proper motion path from a starting point to a terminal point so as to ensure that all the obstacles are safely and safely bypassed without collision in the autonomous navigation process of the ship, and the path is shortest or the path is the best, and after retrieval, the chinese patent No. CN108225326A discloses an AGV path planning method based on an a-x algorithm, and although the path planning speed is higher, the use scene is limited, the search efficiency cannot be adaptively improved according to the complexity of a map, and the flexibility is lower; the intelligent ship is an automatic driving ship and has an autonomous navigation, an intelligent engine room, energy efficiency management, cargo transportation and an intelligent integrated platform; compared with the traditional driving mode of manual control, the autonomous navigation of the ship has the advantages of high safety factor, economy, environmental protection, energy conservation, high automation degree and the like, and the core of the autonomous navigation of the ship is autonomous obstacle avoidance path planning; in the autonomous navigation decision-making system of the ship, the main task is that when the environment with the obstacles or the navigation environment changes, the ship needs to temporarily adjust a local route according to the navigation environment and search for a proper motion path from a starting point to a terminal point so as to ensure that all the obstacles are safely and safely bypassed without collision in the autonomous navigation process of the ship, and the path is shortest or the path is optimal and the like; at present, an A algorithm is mostly adopted for path planning of an intelligent ship and is an improvement of a Dijkstra algorithm, the Dijkstra algorithm is proposed by Edes, Huaibei and Dijkstra, the algorithm is known as the most classical path searching algorithm and is a complete search algorithm, namely, a shortest path can be found certainly as long as a path exists, but in a complex map and a large-scale map, the algorithm has larger operation amount and low efficiency, and the A algorithm reduces the operation amount, improves the efficiency of the algorithm and still has the condition of poor path planning effect; therefore, it becomes important to invent an intelligent ship path planning method based on the improved a-x algorithm.
The existing intelligent ship path planning method mostly adopts the A-star algorithm to carry out route planning navigation, and compared with the Dijkstra algorithm, although the calculation amount is reduced and the algorithm efficiency is improved, the search efficiency cannot be improved in a self-adaptive mode according to the map complexity, and the flexibility is low.
Therefore, there is a need to provide an intelligent ship path planning method based on the improved a-algorithm to solve the above technical problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent ship path planning method based on an improved A-fold algorithm, which ensures the flexibility and the rapidity of the algorithm and can also adaptively improve the searching efficiency according to the complexity of a map.
The invention provides an intelligent ship path planning method based on an improved A-fold algorithm, which comprises the following steps:
s1, establishing a grid map, and respectively placing the starting point S and the obstacle point into an Open linked list and a Closed linked list;
s2, confirming the starting point and the target point and abandoning 3 searching directions in 8 searching directions according to the included angle theta;
s3, checking whether an extended node in an Open linked list has a target point or not, and if not, jumping to the step four; if the target point exists, optimizing the path smoothness by using a bidirectional Folyd algorithm, adding the target point into a Closed linked list, storing the search path and finishing the algorithm;
s4, setting the current node as a father node and transferring the father node to a Closed linked list;
s5, formula according to formula
For computing individual expansion nodes
Value and select
The minimum point is used as a new node, and other extension nodes are stored in a Closed linked list;
and S6, determining whether the Open linked list is an empty list, if not, returning to the step three, and if the Closed linked list is empty, the path does not exist and the algorithm is ended.
In order to achieve the effect of conveniently storing the generated but undetected nodes, in step S1, the Open linked list is used to store the generated but undetected nodes.
To facilitate storing the detected nodes that are not considered because the requirements are not met, the Closed linked list is used to store the detected nodes that are not considered because the requirements are not met in step S1.
In order to achieve the effect of conveniently calculating the included angle θ, the specific calculation process of the included angle θ in the second step is as follows:
1: connecting the current node and the target node, and measuring and calculating an included angle theta of the current node and the target node;
2: discarding 3 search directions according to the included angle θ and a relationship table in step S1, where the relationship table is specifically as follows:
3: and introducing the included angle theta into heuristic information during path searching.
In order to achieve the effect of conveniently performing level division, in the step S3, the extension nodes are extended by adopting a method of sequencing child nodes according to level, the current node is set to be (O), the surrounding nodes are respectively set to be (A, B, C, D, E, F, G, H), the level division is performed, and nodes which are adjacent to the current node in 4 directions of east, north, west and south are divided into high-level groups; nodes adjacent to the current node in the diagonal direction are divided into common groups, in the process of generating the child nodes, the child nodes in the high-level group are searched first, and then the child nodes of the common groups are generated according to a selection rule.
In order to achieve the effect of optimizing the path smoothness conveniently, the specific process of the bidirectional foyd optimized path smoothness in step S3 is as follows:
1.1: deleting middle redundant points on the same straight line in the path, and only keeping a starting point, an inflection point and a target point;
1.2: starting from the starting point S, at the reservation node pi，pjTaking a node every k steps, judging whether an obstacle exists between the taken node and the previous path node, and if so, not changing the path node; if not, calculating the obstacle and the node pj，pkThe distance of the connecting lines between the connecting lines;
1.3: introducing a bidirectional smoothing concept into a Floyd algorithm, and judging a safety distance by taking points from a target point T in a reverse direction, wherein a specific judgment method is the same as that in the step SS 2;
1.4: and (5) outputting a path and finishing the algorithm.
In order to achieve the effect of convenient calculation, the formula in step five
To improve the merit function, its heuristic function
Introducing a barrier rate Pz, said heuristic function
The specific formula is as follows:
dx＝∣xs-xr∣
dy＝∣ys-yr∣
to achieve the effect of convenient calculation, the heuristic function
In the formula: xs and ys are horizontal and vertical coordinates of the starting point S; xr, yr are the horizontal and vertical coordinates of the target point T, and Pz is the failure rate.
In order to achieve the effect of conveniently calculating the failure rate, the formula of the obstacle rate Pz is as follows:
in order to achieve the effect of substituting a convenient formula for calculation, in the formula of the obstacle ratio Pz: m is the number of barrier grids, and Pz ∈ (0, 1).
Compared with the related art, the intelligent ship path planning method based on the improved A-fold algorithm has the following beneficial effects:
1. compared with the traditional A-x algorithm, the method has the advantages that the generated search space can be reduced in a simple map with few obstacles, and the speed of searching the path is increased; on the other hand, in a complex map with many obstacles, the obstacle ratio Pz increases,
the generated search space is increased, the search precision is improved, the flexibility and the rapidity of the algorithm are guaranteed through improvement of the heuristic function, the search efficiency can be improved in a self-adaptive mode according to the complexity of the map, and the problems that the traditional intelligent ship path planning method mostly adopts the A-algorithm to plan and navigate the route, compared with the Dijkstra algorithm, although the calculation amount is reduced, the algorithm efficiency is improved, the search efficiency cannot be improved in a self-adaptive mode according to the complexity of the map, and the flexibility is low are solved;
drawings
Fig. 1 is a flow chart of a method of a preferred embodiment of the intelligent ship path planning method based on the improved ajo algorithm provided by the invention.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Referring to fig. 1, fig. 1 is a flow chart of a method of an embodiment of the present invention for intelligent ship path planning based on the improved ajo algorithm. The intelligent ship path planning method based on the improved A-Algorithm comprises the following steps:
s1, establishing a grid map, and respectively placing the starting point S and the obstacle point into an Open linked list and a Closed linked list;
s2, confirming the starting point and the target point and abandoning 3 searching directions in 8 searching directions according to the included angle theta;
s3, checking whether an extended node in an Open linked list has a target point or not, and if not, jumping to the step four; if the target point exists, optimizing the path smoothness by using a bidirectional Folyd algorithm, adding the target point into a Closed linked list, storing the search path and finishing the algorithm;
s4, setting the current node as a father node and transferring the father node to a Closed linked list;
s5, formula according to formula
For computing individual expansion nodes
Value and select
The minimum point is used as a new node, and other extension nodes are stored in a Closed linked list;
and S6, determining whether the Open linked list is an empty list, if not, returning to the step three, and if the Closed linked list is empty, the path does not exist and the algorithm is ended.
In a specific implementation process, as shown in fig. 1, in step S1, the Open linked list is used to store nodes that have been generated but not detected.
In step S1, the Closed linked list is used to store nodes that have been detected but not considered because the requirements have not been met.
The specific calculation process of the included angle theta in the second step is as follows:
1: connecting the current node and the target node, and measuring and calculating an included angle theta of the current node and the target node;
2: discarding 3 search directions according to the included angle θ and the relation table in step S1, wherein the relation table is as follows:
3: and introducing the included angle theta into heuristic information during path searching.
In the step S3, expanding the expansion nodes by adopting a method of sequencing child nodes according to the level, setting the current node as (O), setting the surrounding nodes as (A, B, C, D, E, F, G, H), dividing the level, and dividing the nodes which are adjacent to the current node in 4 directions of true east, true north, true west and true south into high-level groups; nodes adjacent to the current node in the diagonal direction are divided into common groups, in the process of generating the child nodes, the child nodes in the high-level group are searched first, and then the child nodes of the common groups are generated according to a selection rule.
The specific process of step S3 bidirectional foyd optimizing path smoothness is as follows:
1.1: deleting middle redundant points on the same straight line in the path, and only keeping a starting point, an inflection point and a target point;
1.2: starting from the starting point S, at the reservation node pi，pjTaking a node every k steps, judging whether an obstacle exists between the taken node and the previous path node, and if so, not changing the path node; if not, calculating the obstacle and the node pj，pkThe distance of the connecting lines between the connecting lines;
1.3: introducing a bidirectional smoothing concept into a Floyd algorithm, and judging a safety distance by taking points from a target point T in a reverse direction, wherein a specific judgment method is the same as that in the step SS 2;
1.4: and (5) outputting a path and finishing the algorithm.
Formula of step five
To improve the merit function, its heuristic function
Introducing barrier rate Pz, heuristic function
The specific formula is as follows:
dx＝∣xs-xr∣
dy＝∣ys-yr∣
it should be noted that: heuristic function
In the formula: xs and ys are horizontal and vertical coordinates of the starting point S; xr, yr are the horizontal and vertical coordinates of the target point T, and Pz is the failure rate.
The obstacle ratio Pz is expressed as follows:
it should be noted that: in the formula of the obstacle ratio Pz: m is the number of barrier grids, and Pz ∈ (0, 1).
In particular, the heuristic function
The values of (a) are analyzed as follows:
1. when h (n) is 0, f (n) is g (n), i.e., Dijkstra algorithm.
2. If h (n) < g (n), the heuristic information is less, and at the moment, the number of nodes expanded by the A-x algorithm is increased, and the operation speed of the algorithm is reduced.
3. If h (n) is g (n), then the algorithm a will not expand the un-articulated points and the algorithm will operate at a fast speed.
4. If h (n) > g (n), there is more heuristic information, and at this time, the number of nodes expanded by the a-algorithm is reduced, and it is difficult to find the optimal path.
5. If h (n) > g (n), the influence of g (n) on f (n) is negligible, i.e., g (n) ═ 0, in this case, the algorithm runs fast, but it is difficult to find the shortest path.
Step six: and (4) determining whether the Open linked list is an empty list, if not, returning to the step three, and if the Closed linked list is empty, the path does not exist, and ending the algorithm.
In this embodiment, data comparison is performed for the improved a algorithm and the conventional a algorithm of the present application through simulation experiments, and specific data thereof are shown in the following table:
the simulation experiment data in the table shows that the operation speed of the A-algorithm after various improvements is higher than that of the traditional A-algorithm; compared with the traditional A-star algorithm, the planned path solves the problem of obliquely crossing the top point of the obstacle, meanwhile, the turning times are reduced by 35.42%, the node searching number is reduced by 65.86%, and the advantage of the improved algorithm is proved.
The working principle of the intelligent ship path planning method based on the improved A-fold algorithm provided by the invention is as follows:
firstly, establishing a grid map, and respectively placing a starting point S and an obstacle point into an Open linked list and a Closed linked list; then confirming the starting point and the target point and abandoning 3 searching directions in 8 searching directions according to the included angle theta; then checking whether an extended node in an Open linked list has a target point or not, and jumping to the step four if the extended node does not have the target point; if the target point exists, optimizing the path smoothness by utilizing a bidirectional Folyd algorithm, and simultaneously optimizing the target pointAdding punctuation into a Closed linked list, storing a search path and finishing the algorithm; then setting the current node as a father node and transferring the father node into a Closed linked list; then according to the formula
For computing individual expansion nodes
Value and select
The minimum point is used as a new node, and other extension nodes are stored in a Closed linked list; finally, whether the Open linked list is an empty list or not is confirmed, if not, the step three is returned, if the Closed linked list is empty, the path does not exist, the algorithm is ended, and compared with the traditional A-x algorithm, in a simple map with few obstacles, the generated search space can be reduced, and the speed of searching the path is improved; on the other hand, in a complex map with many obstacles, the obstacle ratio Pz increases,
the method has the advantages that the method is relatively reduced, the generated search space is increased at the moment, the search precision is improved, the flexibility and the rapidity of the algorithm are guaranteed through improvement of the heuristic function, the search efficiency can be improved in a self-adaptive mode according to the complexity of the map, and the problems that the traditional intelligent ship path planning method mostly adopts the A-algorithm to conduct route planning navigation, the calculation amount is reduced and the algorithm efficiency is improved compared with the Dijkstra algorithm, the search efficiency cannot be improved in a self-adaptive mode according to the complexity of the map, and the flexibility is low are solved.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An intelligent ship path planning method based on an improved A-fold algorithm is characterized by comprising the following steps:
s1, establishing a grid map, and respectively placing the starting point S and the obstacle point into an Open linked list and a Closed linked list;
s2, confirming the starting point and the target point and abandoning 3 searching directions in 8 searching directions according to the included angle theta;
s3, checking whether an extended node in an Open linked list has a target point or not, and if not, jumping to the step four; if the target point exists, optimizing the path smoothness by using a bidirectional Folyd algorithm, adding the target point into a Closed linked list, storing the search path and finishing the algorithm;
s4, setting the current node as a father node and transferring the father node to a Closed linked list;
s5, formula according to formula
For computing individual expansion nodes
Value and select
The minimum point is used as a new node, and other extension nodes are stored in a Closed linked list;
and S6, determining whether the Open linked list is an empty list, if not, returning to the step three, and if the Closed linked list is empty, the path does not exist and the algorithm is ended.
2. The improved ajo algorithm-based intelligent ship path planning method according to claim 1, wherein in step S1, the Open linked list is used for storing nodes that have been generated but not detected.
3. The improved ajo algorithm based intelligent ship route planning method according to claim 1, wherein in step S1, the Closed linked list is used to store nodes that have been detected but not considered because the requirements are not met.
4. An intelligent ship path planning method based on the improved A-fold algorithm according to claim 1, wherein the specific calculation process of the included angle θ in the second step is as follows:
1: connecting the current node and the target node, and measuring and calculating an included angle theta of the current node and the target node;
2: discarding 3 search directions according to the included angle θ and a relationship table in step S1, where the relationship table is specifically as follows:
3: and introducing the included angle theta into heuristic information during path searching.
5. The intelligent ship path planning method based on the improved ajo algorithm as claimed in claim 1, wherein in step S3, the expansion nodes are expanded by a method of sorting sub-nodes according to level, the current node is (O), the surrounding nodes are (A, B, C, D, E, F, G, H), the level division is performed, and the nodes adjacent to the current node in 4 directions of true east, true north, true west and true south are divided into high-level groups; nodes adjacent to the current node in the diagonal direction are divided into common groups, in the process of generating the child nodes, the child nodes in the high-level group are searched first, and then the child nodes of the common groups are generated according to a selection rule.
6. The method for intelligent ship path planning based on improved ajo algorithm as claimed in claim 1, wherein the specific process of bidirectional Folyd optimized path smoothness in step S3 is as follows:
1.1: deleting middle redundant points on the same straight line in the path, and only keeping a starting point, an inflection point and a target point;
1.2: starting from the starting point S, at the reservation node pi，pjTaking a node every k steps, judging whether an obstacle exists between the taken node and the previous path node, and if so, not changing the path node; if not, calculating the obstacle and the node pj，pkThe distance of the connecting lines between the connecting lines;
1.3: introducing a bidirectional smoothing concept into a Floyd algorithm, and judging a safety distance by taking points from a target point T in a reverse direction, wherein a specific judgment method is the same as that in the step SS 2;
1.4: and (5) outputting a path and finishing the algorithm.
7. The improved ajo algorithm based intelligent ship path planning method as claimed in claim 1, wherein the formula of S5
To improve the merit function, its heuristic function
Introducing a barrier rate Pz, said heuristic function
The specific formula is as follows:
dx＝∣xs-xr∣
dy＝∣ys-yr∣。
8. the improved ajo algorithm based intelligent ship path planning method according to claim 7, wherein the heuristic function
In the formula: xs and ys are horizontal and vertical coordinates of the starting point S; xr, yr are the horizontal and vertical coordinates of the target point T, and Pz is the failure rate.
9. An intelligent ship path planning method based on the improved ajo algorithm as claimed in claim 1, wherein the obstacle ratio Pz is expressed as follows:
10. an improved ajo algorithm based intelligent ship path planning method according to claim 9, characterized in that the obstacle ratio Pz is expressed by the formula: m is the number of barrier grids, and Pz ∈ (0, 1).
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