CN114353814A - Improved JPS path optimization method based on Angle-Propagation Theta algorithm - Google Patents
Improved JPS path optimization method based on Angle-Propagation Theta algorithm Download PDFInfo
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Abstract
The invention discloses an Angle-Propagation Theta algorithm-based improved JPS path optimization method, which comprises the following steps: s1, modifying forced neighbors of the JPS algorithm and neighbor pruning rules; s2, optimizing the generation path by selecting a succession search point by adopting a visual field triangle judgment method; s3, modifying the sight line accessibility detection between two nodes by adopting an Angle-Propagation Theta algorithm; s4, adopting bidirectional optimization algorithm for continuously updating the successive search point and the discrete function, and reducing iterative computation and complexity. Based on four different types of sizes, the path-finding simulation comparison is carried out on the grid map with the same obstacle proportion on an A-algorithm, a JPS path optimization algorithm improved based on an Angle-Propagation Theta algorithm and a double-calculation optimization algorithm, and the experimental result shows that the bidirectional optimization method provided by the invention can shorten the whole path length by 4-14% on the basis of keeping the path search efficiency.
Description
Technical Field
The invention belongs to the field of robot path planning, and particularly relates to an Angle-Propagation Theta algorithm-based improved JPS path optimization method.
Background
Path planning of a mobile robot is one of key technologies of an intelligent robot autonomous navigation technology, and is gradually a hotspot of research in the robot field. The intelligent robot aims to rapidly plan a walking route from an initial position to a target area in a complex working scene, and the walking route is short in path, high in efficiency and strong in safety. The current commonly used path planning algorithms include geometric model grid method, intelligent search algorithm, artificial intelligence algorithm and the like. The geometric model grid method is widely used because of its advantages of simplicity, high efficiency, strong adaptability, etc.
In the geometric grid method, the A-x algorithm is used as a heuristic algorithm for traversing the non-obstacle neighbors of the current node by using the valuation function to find the shortest path, and has the advantages of simplicity, easiness in operation, high accuracy and the like. However, the a-algorithm is in the process of finding the shortest path, because the algorithm runs too long due to the search and calculation of a large number of useless neighbor nodes. And secondly, the path obtained by the calculation of the A-star algorithm is actually the shortest path of the discrete unit under the given map model, and has larger difference with the actual optimal path. The JPS algorithm proposed by Daniel Harabor et al in 2011 further optimizes the operation of the a algorithm to find subsequent nodes while retaining the original a algorithm framework. The JPS algorithm occupies less memory than A, and has higher operation efficiency than A. In 2016 Jason Traish et al, proposed a JPS improved algorithm based on boundary search, which eliminates the number of iterations in the JPS search process by recording the position information of the obstacles and directly by searching for these positions. Based on the collision problem which can occur to the robot in a complex environment, the Xue Zheng et al in 2019 provides a safe distance jumping point search algorithm based on a node domain matrix, and the flexibility of the robot motion is improved. In 2021, Ben Zhang et al proposed a path planning algorithm based on jumping point search and Bezier curve, and optimized the generation path of JPS algorithm by using Bezier curve while reducing the cost of the algorithm by improving the heuristic function of JPS algorithm. Compared with the A-star algorithm, the JPS algorithm and the optimization algorithm thereof reduce the number of nodes needing traversal search, improve the retrieval efficiency, but the obtained path still has a difference compared with the optimal path of the actual space.
Disclosure of Invention
In order to solve the problems, the invention provides an improved JPS path optimization method based on Angle-Propagation Theta algorithm. Firstly, forced neighbors of a JPS algorithm and neighbor pruning rules are modified, so that the JPS algorithm can be better operated and adapted on a discretization map taking grid vertexes as paths; secondly, based on a JPS algorithm, the invention provides a visual field triangle judgment method, which is characterized in that a generation path is further optimized by selecting a succession search point, and the idea of node visual Angle in an Angle-Propagation Theta algorithm is introduced to modify visual line accessibility detection between two nodes; and finally, in order to further reduce a large amount of iterative computation and complexity generated by an Angle-Propagation Theta-based path optimization algorithm, a bidirectional optimization algorithm for continuously updating successive search points and discrete functions is provided. The bidirectional optimization method provided by the invention can shorten the whole path length on the basis of keeping the path searching efficiency.
In view of the above, the invention adopts the technical scheme that the method for optimizing the JPS path based on the Angle-Propagation Theta algorithm improvement comprises the following steps:
s1, carrying out grid vertex map adaptation on the JPS algorithm, improving the judgment standard of the pruning neighbor rule and the forced neighbor of the JPS algorithm, and modifying the path-finding process of the JPS algorithm to enable the JPS algorithm to be better operated and adapted on the discretization map taking grid vertices as paths. And aiming at the positions and the number of the obstacles in the grid vertex map, improving the moving path rule of the robot.
S2, improving the traditional JPS path optimization strategy by introducing a view triangle and a discrete function Route.
And S3, calculating the visual Angle range of the road section to be optimized by using an Angle-Propagation Theta algorithm, and directly judging whether an accessible path exists between a new node and a father node according to the visual Angle range, thereby reducing the detection calculation amount of the sight line in the optimization process.
And S4, performing Angle-Propagation Theta-based visual Angle retrieval from the starting point and the target point of the path by adopting a bidirectional optimization algorithm, and continuously updating Route discrete functions of the two parties according to the position information of the successive search point so as to further reduce the iteration times and time consumption of the optimization algorithm.
Further, the grid vertex map adaptation for the JPS algorithm is specifically to perform grid vertex map adaptation for a neighbor pruning rule of the JPS algorithm and a forced neighbor. The mobile robot is considered as a particle moving along the grid vertices. And placing the starting point and the target point of the path at the top point of the grid to carry out path finding.
Specifically, the grid vertex map adaptation comprises:
and S11, changing the positions of the path starting point and the target point from the center of the grid to the top of the grid.
S12, changing the neighbor grid into neighbor nodes in the algorithm routing process, and changing the node neighbor domain from 8 grids into 4 grids and 8 vertexes.
S13, changing the forced neighbor and neighbor pruning rule of the JPS algorithm from rasterization to nodulation, wherein the pruning path is changed from continuous raster to continuous node.
Specifically, the robot movement path rule is as follows:
(1) if a single grid obstacle exists, the mobile robot is allowed to walk along the boundary of the obstacle, but is not allowed to cross the obstacle in an oblique mode.
(2) If the situation of the multi-grid obstacles exists, the mobile robot is allowed to walk along the whole boundary of the obstacles, but the mobile robot is not allowed to cross the boundary edge between the single-grid obstacles and obliquely cross the obstacles.
(3) If the obstacle is connected with the map boundary, the interactive edge crossing the obstacle and the map boundary is not allowed to appear.
Further, the above-mentioned introduction of the view triangle and the discrete function Route improves the JPS path optimization strategy specifically by introducing the view triangle to determine whether there is a turning node within the coverage of the triangle, and comparing the cost value g (x) between the turning node and the parent node of the current node by calculation, and selecting the optimal turning point to realize the shortest path optimization. The discrete function is discretized path point location information between two nodes, and the view triangle is composed of a first unreachable path, a previous reachable path and an original path, wherein the first unreachable path, the previous reachable path and the original path are subjected to view line detection according to the sequence of the discrete paths.
Further, the visual line reachability optimization based on Angle-Propagation Theta algorithm specifically replaces reachability detection between nodes with visual Angle detection. And constraining and adjusting the size of the visual angle according to the position information of the current node. After the angle constraint is completed, the expansion from the current node to any adjacent node only needs to detect whether the angle of the triangle formed by the current node, the adjacent node and the father node is included in the visual angle range. And directly judging whether a reachable path exists between the new node and the father node or not through calculating the visual angle range of the road section to be optimized. Since the time to compute the Angle is substantially constant, the consumption of Angle-Propagation Theta in expanding the vertices is no longer linear with the number of grids, but becomes constant.
Further, the bidirectional optimization algorithm is specifically in the process of performing JPS path optimization. And simultaneously performing Angle-Propagation Theta-based visual Angle retrieval from the starting point and the target point of the path, and continuously updating Route discrete functions of the two parties according to the position information of the successive search point, thereby further improving the retrieval efficiency of the two parties and further reducing the calculation time and the complexity of the algorithm. In the bidirectional optimization algorithm, a starting point discrete function is set to be Route (< Sstart.,. Send >), and a path discrete function of a target point is set to be Route (< Send.,. Sstart >). The two-party Route function is updated each time one party finds the next inherited search point. The two-party function only needs to record a discrete path from the current search point of the own party to the current search point of the other party, and the path outside the discrete node is a one-way reachable path and does not need to be detected.
The invention has the following advantages and beneficial effects:
the invention firstly provides a JPS path optimization method based on Angle-Propagation Theta algorithm, which combines the JPS algorithm adapted to grid vertex map with the visual Angle provided by Angle-Propagation Theta, reduces the path length on the premise of sacrificing efficiency, reduces the efficiency of the one-way optimization algorithm along with the improvement of the map area and the complexity under the same map condition, reduces the path length by 4 to 14 percent relative to the JPS algorithm, and reduces the search efficiency by 16.7 to 52 percent.
The invention provides a bidirectional optimization strategy aiming at the problems of multiple iteration times and low calculation efficiency of a unidirectional optimization algorithm. The search is carried out from the starting point and the target point of the path at the same time, the search efficiency is improved compared with the one-way optimization algorithm, the time consumption is reduced by 9.2-24.2%, and the path optimization capability is as excellent as that of the one-way optimization algorithm.
Based on four different types of sizes and the same obstacle proportion, path finding simulation comparison is carried out on an A-algorithm, a JPS path optimization algorithm improved based on an Angle-Propagation Theta algorithm and a double-calculation optimization algorithm on a grid map, and experimental results show that the overall path length can be shortened by 4% -14% on the basis of keeping the path searching efficiency by the bidirectional optimization method provided by the invention.
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FIG. 1 is a schematic diagram of a grid vertex map-based JPS algorithm forced neighbor and neighbor pruning rule;
FIG. 2 is a view of a field of view triangle selection and coverage area;
FIG. 3 is a range of visible angles;
FIG. 4 is a diagram of a visual Angle-Propagation Theta-based JPS path optimization process;
FIG. 5 is a process diagram of a bi-directional optimization algorithm;
FIG. 6 is a flow chart of Angle-Propagation Theta-based visual Angle-based JPS path optimization;
FIG. 7 is a flow chart of a bi-directional optimization algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
As shown in fig. 6, the Angle-Propagation Theta-based visual Angle JPS path optimization includes the following steps:
s1, as shown in fig. 1, the neighbor pruning rule of the JPS algorithm based on the grid vertex map is:
s11, if the current node x is a starting point and the node x has no father node, no neighbor pruning rule exists, and the searching direction is eight directions of the adjacent nodes.
S12, the current node x is not the initial node, the parent (x) to x of the node x is the straight line searching direction, and n is the neighbor node of the node x. If there is a path from parent (x) to n that does not pass through x and this path is less than or equal to the path from parent (x) to n that passes through x, then the next node must not reach n, i.e. the n node is pruned.
S13, the current node x is a non-initial node, parent nodes (x) to x of the node x are diagonal search directions, and n is a neighbor node of the node x. If there is a path from parent (x) to n that does not pass through x and this path is smaller than the path from parent (x) to n that passes through x, then the next node must not reach n, i.e. the n node is pruned.
The forced neighbor judgment rule is: obstacles exist in 8 neighbors of a current node x, n is a neighbor node in a non-obstacle and non-search direction of the current node, and the distance cost of a parent node (x) of x reaching n through x is smaller than the distance cost of any path which does not reach n through x, so that n is called a forced neighbor of x.
S2, based on the defects of the sight line method, the invention provides a steering angle judgment rule for optimization. Whether a turning node is positioned in the coverage range of the view triangle is judged by introducing the view triangle, and the cost value G (x) between the turning node and the father node of the current node is obtained through calculation and is compared, so that the optimal turning node is selected to realize the shortest path optimization. The specific steps are as follows:
s21, setting the current jumping point as S point, the father node of S as p (S) point, the path child jumping point of S as e point.
S22, detecting LOS accessibility between the p (S) point and the e point, if yes, discarding the S node, and setting the father node of the e nodeThe point is changed from s to p(s), and the path is changed from the original p(s)>s->e change to p(s)>e, completing the optimization. If not, discretizing the path between the s node and the e node into Route (c) according to the grid vertex<s,1,2,3,...,e>). From the s node, p(s) and Route(s) are calculated in sequence<s,1,2,3,...,e>) Reachability of internal nodes. If the first unreachable path is encountered, it is set to LmCorresponding Route (c) ((b))<s,1,2,3,...,e>) Node R in (1)m。Lm-1Route (for the previous discrete point reachable path) corresponds to<s,1,2,3,...,e>) Middle node Rm-1。
S23, mixing Lm-1、LmThe triangle area generated from the original path from the s node to the e node is set as the view triangle, as shown in fig. 2. Searching the included barrier vertex (the barrier is at the side with the optimal path angle less than 180 degrees) in the triangular area (including the triangular boundary) and calculating the cost value G (x) between the barrier and p(s), selecting the node with the minimum G (x) as the next searching starting point p(s), and taking R as the next searching starting point p(s)m-1As the S node, the search is continued in steps S22 and S33.
S3, as shown in fig. 3, when expanding from one node to other nodes, the Angle-Propagation Theta algorithm first sets the visible Angle [ lb (S), ub (S) ] of the current node to [ - ∞, + ], where lb (S) is the lower Angle region and ub (S) is the upper Angle region. And then, by checking other limiting conditions, carrying out angle constraint on lb(s) and ub(s). The specific constraints are as follows:
s31, if the current node S is the vertex of the obstacle grid and all other neighboring vertices z of the current grid satisfy one of the following conditions, lb (S) of the current node is 0.
parent(s)＝z (1)
θ(s,parent(s),z)<0 (2)
θ(s,parent(s),z)＝0 AND c(parent(s),z)≤c(parent(s),s) (3)
S32, if the current node S is the vertex of the obstacle grid and all other neighboring vertices z of the current grid satisfy one of the following conditions, ub (S) of the current node is 0.
parent(s)＝s' (4)
θ(s,parent(s),z)>0 (5)
θ(s,parent(s),z)＝0 AND c(parent(s),z)≤c(parent(s),s) (6)
S33, for the neighbor node S 'of the current node S, if S' satisfies all of the following conditions:
s'∈closedlist (7)
parent(s)＝parent(s') (8)
s'≠Sstart (9)
if lb (s ') + theta (s, parent(s) is satisfied between the s node and the s ' node, and s ') is less than or equal to 0
lb(s)＝max(lb(s),lb(s')+θ(s,parent(s),s')) (10)
If ub (s ') + theta (s, parent(s) is satisfied between the s node and the s ' node, s ') is greater than or equal to 0
ub(s)＝min(ub(s),ub(s')+θ(s,parent(s),s')) (11)
S34, for the neighbor node S 'of the current node S, if S' satisfies all the following conditions:
c(parent(s),s')<c(parent(s),s) (12)
parent(s)≠s' (13)
if the viewing angle satisfies theta (s, part(s), s) <0
lb(s)＝max(lb(s),θ(s,parent(s),s')) (15)
If the viewing angle satisfies theta (s, part(s), s) >0
ub(s)＝min(ub(s),θ(s,parent(s),s')) (16)
Wherein theta (s, parent(s), and s ') is an included angle between a straight line formed by the parent(s) of the current node s and the current node and a straight line formed by the parent(s) and the neighbor node s' of the current node. Wherein theta (s, parent(s), and z) is an included angle between a straight line formed by parent(s) of the current node s and the current node and a straight line formed by parent(s) and vertex z of the same grid neighbor of the current node. c (parent(s), s) is the cost value between the current node s and its parent(s). c (parent(s), s ') is the cost value between the neighbor node s' of the current node and the parent node parent(s) of the current node. c (parent(s), z) is the cost value between the co-grid neighbor vertex z of the current node and the parent node parent(s) of the current node. The closedlist is a list of close nodes in the JPS algorithm. Sstart is the path initial node.
After the angle constraint is completed, when the current node expands to any adjacent node, only whether lb(s) is less than or equal to theta (s, parent(s), s') is less than or equal to ub(s) is needed to be checked, and whether LOS reachability exists between the adjacent node and the parent node of the current node can be judged, as shown in fig. 4.
S4, as shown in fig. 5, the bidirectional optimization algorithm performs Angle-Propagation Theta-based visual Angle retrieval from the start point and the target point of the path at the same time, and continuously updates Route discrete functions of both parties according to the position information of the successor search point, thereby further improving the retrieval efficiency of both parties and further reducing the computation time and complexity of the entire algorithm. The flow chart of the bidirectional optimization algorithm is shown in fig. 7, and the specific flow is as follows:
and S41, selecting the starting point and the target point as the initial searching points of the two parties.
S42, LOS reachability detection is performed between both search points.
And S43, if yes, ending the algorithm, and if not, continuing to perform the step S44.
And S44, updating the Route functions of the two parties, and discretizing the path between the two parties according to the raster information contained in the Route functions.
And S45, sequentially calculating the visual angle range between each node and the search point according to the discrete order from the search points of both parties. And find the first unreachable path L generated in the searching process of the two partiesmAnd an unreachable path point Rm。
S46, both of them need to be in the group Lm，Lm-1And vision formed by combining original path segmentsAnd selecting an optimal turning point in the field triangular region, and enabling the optimal turning point to inherit a next search point.
S47, the step jumps to S42 to execute the next round of search.
Claims (10)
1. The improved JPS path optimization method based on Angle-Propagation Theta algorithm is characterized by comprising the following steps:
s1, carrying out grid vertex map adaptation on the JPS algorithm, improving the judgment standard of a pruning neighbor rule and a forced neighbor of the JPS algorithm, modifying the route searching process of the JPS algorithm, and improving the moving path rule of the robot aiming at the position and the number of obstacles in the grid vertex map;
s2, improving the traditional JPS path optimization strategy by introducing a view triangle and a discrete function Route;
s3, calculating the visual Angle range of the road section to be optimized by using an Angle-Propagation Theta algorithm, and directly judging whether an reachable path exists between a new node and a father node according to the visual Angle range;
and S4, performing Angle-Propagation Theta-based visual Angle retrieval from the starting point and the target point of the path simultaneously by adopting a bidirectional optimization algorithm, and continuously updating Route discrete functions of the two parties according to the position information of the successive search point.
2. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 1, wherein: the grid vertex map adaptation comprises:
s11, changing the positions of the path starting point and the target point from the center of the grid to the top of the grid;
s12, changing the neighbor grid into neighbor nodes in the routing process of the algorithm, and changing the node neighbor domain from 8 grids into 4 grids and 8 vertexes;
s13, changing the forced neighbor and neighbor pruning rule of the JPS algorithm from rasterization to nodulation, wherein the pruning path is changed from continuous raster to continuous node.
3. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 2, wherein: the moving path rule of the robot is as follows:
(1) if a single grid obstacle exists, allowing the mobile robot to walk along the boundary of the obstacle, but not allowing the mobile robot to obliquely cross the obstacle;
(2) if the situation of the multi-grid obstacles exists, the mobile robot is allowed to walk along the whole boundary of the obstacles, but the mobile robot is not allowed to cross the boundary edge between the single-grid obstacles and obliquely cross the obstacles;
(3) if the obstacle is connected with the map boundary, the interactive edge crossing the obstacle and the map boundary is not allowed to appear.
4. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 1, wherein: the improved JPS algorithm pruning neighbor rule and forced neighbor are respectively as follows:
the pruning neighbor rule is as follows:
(1) if the current node x is a starting point and the node x does not have a father node, a neighbor pruning rule does not exist, and the searching direction is eight directions of adjacent nodes;
(2) if a path from parent (x) to n which does not pass through x exists and is less than or equal to a path from parent (x) to n which passes through x, the n node is pruned;
(3) if a path from parent (x) to n which does not pass through x exists and is smaller than a path from parent (x) to n which passes through x, the n node is pruned;
the forced neighbor judgment rule is as follows: obstacles exist in 8 neighbors of a current node x, n is a neighbor node in a non-obstacle and non-search direction of the current node, and the distance cost of a parent node (x) of x reaching n through x is smaller than the distance cost of any path which does not reach n through x, so that n is called a forced neighbor of x.
5. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 1, wherein: the step S2 specifically includes introducing a view triangle to determine whether there is a turning point located within the coverage of the view triangle, and calculating a cost value g (x) between the turning point and a parent node of the current node, and comparing the cost values, and selecting the turning point corresponding to the minimum cost value as the optimal turning point.
6. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 5, wherein: the view triangle is composed of a first unreachable path, a previous reachable path and an original path, wherein the first unreachable path, the previous reachable path and the original path are used for carrying out view line detection according to the sequence of discrete paths.
7. The improved JPS path optimization method based on Angle-Propagation Theta algorithm according to claim 6, wherein: the view triangle is specifically determined by the following method:
s21, setting the current jumping point as S point, the father node of S as p (S) point, the path child jumping point of S as e point;
s22, detecting the accessibility between the p (S) point and the e point, if the accessibility is up, discarding the S node, changing the father node of the e node from S to p (S), and changing the path from the original p (S) > S- > e to p (S) > e to complete the optimization; if the node is unreachable, discretizing the path between the s node and the e node into Route (< s,1,2, 3., e >) according to grid vertices, calculating the reachability of the nodes in p(s) and Route (< s,1,2, 3., e >) from the s node respectively according to the sequence, and setting the reachability as L if a first unreachable path is metmNode R in corresponding Route (< s,1,2, 3., e >)m，Lm-1The reachable path of the previous discrete point corresponds to the node R in Route (< s,1,2, 3., e >)m-1，
S23, mixing Lm-1、LmAnd setting a triangular area generated by the original path from the s node to the e node as a view triangle.
8. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 1, wherein: the step S3 specifically includes:
s31, if the current node S is the vertex of the barrier grid and all other neighboring vertices z of the current grid satisfy one of the following conditions, lb (S) of the current node is 0;
parent(s)＝z (1)
θ(s,parent(s),z)＜0 (2)
θ(s,parent(s),z)＝0 AND c(parent(s),z)≤c(parent(s),s) (3)
s32, if the current node S is the vertex of the obstacle grid and all other neighboring vertices z of the current grid satisfy one of the following conditions, ub (S) ═ 0 of the current node;
parent(s)＝z (4)
θ(s,parent(s),z)＞0 (5)
θ(s,parent(s),z)＝0 AND c(parent(s),z)≤c(parent(s),s) (6)
s33, for the neighbor node S 'of the current node S, if S' satisfies all of the following conditions:
s'∈closedlist (7)
parent(s)＝parent(s') (8)
s'≠Sstart (9)
if lb (s ') + theta (s, parent(s) is satisfied between the s node and the s ' node, and s ') is less than or equal to 0
lb(s)＝max(lb(s),lb(s')+θ(s,parent(s),s')) (10)
If ub (s ') + theta (s, parent(s) is satisfied between the s node and the s ' node, s ') is greater than or equal to 0
ub(s)＝min(ub(s),ub(s')+θ(s,parent(s),s')) (11)
S34, for the neighbor node S 'of the current node S, if S' satisfies all of the following conditions:
c(parent(s),s')＜c(parent(s),s) (12)
parent(s)≠s' (13)
if the viewing angle satisfies theta (s, part(s), s) <0
lb(s)＝max(lb(s),θ(s,parent(s),s')) (15)
If the viewing angle satisfies theta (s, part(s), s) >0
ub(s)＝min(ub(s),θ(s,parent(s),s')) (16)
Wherein θ (s, parent(s), s ') is an angle between a straight line formed by a parent node parent(s) of the current node s and the current node and a straight line formed by the parent(s) and a neighbor node s' of the current node, θ (s, parent(s), z) is an angle between a straight line formed by the parent node parent(s) of the current node s and the current node and a straight line formed by the parent(s) and a grid-shared neighbor vertex z of the current node, c (parent(s), s) is a cost value between the current node s and the parent node parent(s), c (parent(s), s ') is a cost value between a neighbor node s' of the current node and the parent node parent(s) of the current node, c (parent(s), z) is a cost value between the grid-shared neighbor vertex z of the current node and the parent node parent(s) of the current node, and closed list is a JPS node algorithm, sstart is the path initial node.
9. The improved JPS path optimization method based on Angle-Propagation Theta algorithm as claimed in claim 1, wherein: the step S4 specifically includes the steps of,
s41, selecting a starting point and a target point as initial searching points of the two parties;
s42, LOS reachability detection is carried out between the two search points;
s43, if yes, ending the algorithm, if not, continuing to step S44;
s44, updating Route functions of both parties, and discretizing the path between the two parties according to the raster information contained in the Route functions;
s45, starting from the searching points of both parties, sequentially calculating the visual angle range between each node and the searching points according to the discrete sequence, and searching the products produced in the searching process of both partiesThe first unreachable path L of the birthmAnd an unreachable path point Rm；
S46, both of them need to be in the group Lm，Lm-1Selecting an optimal turning point in a view triangular region formed by combining the original path segments, and enabling the optimal turning point to inherit a next search point;
s47, the step jumps to S42 to execute the next round of search.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed, may implement the path optimization method of any of claims 1-9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120283948A1 (en) * | 2011-05-03 | 2012-11-08 | University Of Southern California | Hierarchical and Exact Fastest Path Computation in Time-dependent Spatial Networks |
| CN110887484A (en) * | 2019-10-14 | 2020-03-17 | 重庆邮电大学 | Mobile robot path planning method based on improved genetic algorithm and storage medium |
| US20200143224A1 (en) * | 2018-11-06 | 2020-05-07 | Microsoft Technology Licensing, Llc | Automated Personalized Classification of Journey Data Captured by One or More Movement-Sensing Devices |
| CN111811514A (en) * | 2020-07-03 | 2020-10-23 | 大连海事大学 | Path planning method based on regular hexagon grid jumping point search algorithm |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| US20210270629A1 (en) * | 2020-02-27 | 2021-09-02 | Here Global B.V. | Method and apparatus for selecting a path to a destination |
| CN113485369A (en) * | 2021-08-03 | 2021-10-08 | 浙江大学 | Indoor mobile robot path planning and path optimization method for improving A-x algorithm |
-
2021
- 2021-12-06 CN CN202111477785.8A patent/CN114353814B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120283948A1 (en) * | 2011-05-03 | 2012-11-08 | University Of Southern California | Hierarchical and Exact Fastest Path Computation in Time-dependent Spatial Networks |
| US20200143224A1 (en) * | 2018-11-06 | 2020-05-07 | Microsoft Technology Licensing, Llc | Automated Personalized Classification of Journey Data Captured by One or More Movement-Sensing Devices |
| CN110887484A (en) * | 2019-10-14 | 2020-03-17 | 重庆邮电大学 | Mobile robot path planning method based on improved genetic algorithm and storage medium |
| US20210270629A1 (en) * | 2020-02-27 | 2021-09-02 | Here Global B.V. | Method and apparatus for selecting a path to a destination |
| CN111811514A (en) * | 2020-07-03 | 2020-10-23 | 大连海事大学 | Path planning method based on regular hexagon grid jumping point search algorithm |
| CN112229419A (en) * | 2020-09-30 | 2021-01-15 | 隶元科技发展(山东)有限公司 | Dynamic path planning navigation method and system |
| CN113485369A (en) * | 2021-08-03 | 2021-10-08 | 浙江大学 | Indoor mobile robot path planning and path optimization method for improving A-x algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 余文凯 等: "基于地图预处理及改进A~_算法的路径规划", 高技术通讯, vol. 30, no. 4 * |
| 王琼 等: "一种基于地形方向通行性的改进Theta_算法", 空间科学学报, vol. 36, no. 3 * |
| 罗元 等: "基于迭代无迹H_∞滤波的移动机器人SLAM", 计算机科学, vol. 44, no. 8 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
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