CN114353814B - JPS path optimization method based on Angle-Propagation Theta algorithm improvement - Google Patents
JPS path optimization method based on Angle-Propagation Theta algorithm improvement Download PDFInfo
- Publication number
- CN114353814B CN114353814B CN202111477785.8A CN202111477785A CN114353814B CN 114353814 B CN114353814 B CN 114353814B CN 202111477785 A CN202111477785 A CN 202111477785A CN 114353814 B CN114353814 B CN 114353814B
- Authority
- CN
- China
- Prior art keywords
- node
- parent
- path
- algorithm
- jps
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 98
- 238000005457 optimization Methods 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 45
- 230000006872 improvement Effects 0.000 title claims description 11
- 230000006870 function Effects 0.000 claims abstract description 20
- 230000000007 visual effect Effects 0.000 claims abstract description 19
- 238000013138 pruning Methods 0.000 claims abstract description 14
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 12
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 230000008569 process Effects 0.000 claims description 11
- 230000004888 barrier function Effects 0.000 claims description 7
- 230000006978 adaptation Effects 0.000 claims description 6
- 230000002452 interceptive effect Effects 0.000 claims description 2
- 238000004590 computer program Methods 0.000 claims 2
- 238000004088 simulation Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 230000009191 jumping Effects 0.000 description 2
- 238000010845 search algorithm Methods 0.000 description 2
- 241001286466 Adeloneivaia jason Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000015654 memory Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
Landscapes
- Navigation (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses an improved JPS path optimization method based on an Angle-Propagation Theta algorithm, which comprises the following steps: s1, modifying a forced neighbor and neighbor pruning rule of a JPS algorithm; s2, optimizing the generated path by selecting a succession search point by adopting a visual field triangle judgment method; s3, modifying the line-of-sight reachability detection between two nodes by adopting an Angle-Propagation Theta algorithm; s4, adopting a bidirectional optimization algorithm for continuously updating successive search points and discrete functions, and reducing iterative computation and complexity. Based on four different types of sizes, the grid map with the same obstacle ratio is subjected to path finding simulation comparison by using an A-algorithm, a JPS path optimization algorithm based on an Angle-Propagation Theta algorithm and a double-algorithm optimization algorithm, and experimental results show that the bidirectional optimization method provided by the invention can shorten the overall path length by 4% -14% on the basis of keeping the path searching efficiency.
Description
Technical Field
The invention belongs to the field of robot path planning, and particularly relates to an improved JPS path optimization method based on an Angle-Propagation Theta algorithm.
Background
The path planning of the mobile robot is one of key technologies of the autonomous navigation technology of the intelligent robot, and gradually becomes a hot spot for the research of the robot field. The intelligent robot planning method aims at realizing the purpose that the intelligent robot quickly plans a walking route with short path, high efficiency and strong safety from the initial position to the target area in a complex working scene. The path planning algorithm commonly used at present comprises a geometric model grid method, an intelligent search algorithm, an artificial intelligent algorithm and the like. The geometric model grid method has the advantages of simplicity, high efficiency, strong adaptability and the like, and is widely used.
In the geometric grid method, the A algorithm is used as a heuristic algorithm for searching the shortest path by traversing the non-obstacle neighbors of the current node by using the valuation function, and has the advantages of simplicity, easiness in operation, high accuracy and the like. But the algorithm is in the process of finding the shortest path because its search and computation for a large number of useless neighbor nodes results in an algorithm that is run-time too long. And secondly, the path obtained by calculation of the A-algorithm is actually the shortest path of the discrete units under the given map model, and has a great 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 has a much higher operation efficiency than the a algorithm while the number of occupied memories is smaller than a. Jason Traish et al in 2016 propose a JPS improved algorithm based on boundary search, by recording the position information of the obstacle, and directly eliminating the iteration number in the JPS search process by means of searching for the positions. In 2019 Xue Zheng et al, based on the problem of collision of a robot under the condition of a complex environment, a safe distance jumping point search algorithm based on a node domain matrix is provided, and the flexibility of robot movement is improved. 2021 Ben Zhang et al propose a path planning algorithm based on jumping point search and bezier curve, which optimizes the generation path of the JPS algorithm by using the bezier curve while reducing the algorithm cost by improving the heuristic function of the JPS algorithm. Compared with the A algorithm, the JPS algorithm and the optimization algorithm thereof reduce the number of nodes needing to be traversed and searched, improve the retrieval efficiency, but the obtained path is still different from the optimal path in the actual space.
Disclosure of Invention
In order to solve the above problems, the present invention proposes an improved JPS path optimization method based on Angle-Propagation Theta algorithm. Firstly, modifying the forced neighbor and neighbor pruning rules of the JPS algorithm, so that the method can be better operated and adapted on a discretized map taking grid vertexes as paths; secondly, based on a JPS algorithm, the invention provides a visual field triangle judging method, the generated path is further optimized by selecting a succession search point, and the idea of the node visual Angle in the Angle-Propagation Theta algorithm is introduced to modify the visual line accessibility detection between two nodes; finally, in order to further reduce a great amount of iterative computation and complexity generated by the path optimization algorithm based on Angle-Propagation Theta, a bidirectional optimization algorithm for continuously updating the succession search points and the discrete functions is provided. The bidirectional optimization method provided by the invention can shorten the overall path length on the basis of maintaining the path searching efficiency.
In view of this, the technical scheme adopted by the invention is that the JPS path optimization method based on the improvement of Angle-Propagation Theta algorithm comprises the following steps:
s1, grid vertex map adaptation is carried out on a JPS algorithm, a pruning neighbor rule and a judgment standard of a forced neighbor of the JPS algorithm are improved, and a path finding process of the JPS algorithm is modified, so that the JPS algorithm can be better operated and adapted on a discretized map taking grid vertices as paths. The robot movement path rules are improved for the positions and number of obstacles in the grid vertex map.
S2, improving the traditional JPS path optimization strategy by introducing a field 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 reachable path exists between the new node and the father node or not through the visual Angle range, so that the detection calculation amount of the visual field line in the optimization process is reduced.
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 subsequent search point, so that the iteration times and time consumption of the optimization algorithm are further reduced.
Further, the grid vertex map adaptation for the JPS algorithm is specifically performed by performing grid vertex map adaptation for a neighbor pruning rule and a forced neighbor of the JPS algorithm. A mobile robot is considered a particle moving along the grid vertices. And placing the path starting point and the target point at the grid vertex to search the path.
Specifically, the grid vertex map adaptation includes:
s11, changing the positions of the path starting point and the target point from the grid center to the grid vertex.
S12, changing the neighbor grid into a neighbor node in the algorithm route searching process, and changing the node neighbor domain from 8 grids into 4 grids and 8 vertexes.
S13, the forced neighbor and neighbor pruning rule of the JPS algorithm is changed from rasterization to node-ization, wherein the pruning path is changed from continuous grids to continuous nodes.
Specifically, the robot movement path rule is:
(1) If a single grid obstacle condition exists, the mobile robot is allowed to walk along the obstacle boundary, but is not allowed to obliquely cross the obstacle.
(2) If a multi-joint grid obstacle condition exists, the mobile robot is allowed to walk along the whole boundary of the obstacle, but is not allowed to cross the boundary edge between single grid obstacles and obliquely cross the obstacle.
(3) If there is a situation where the obstacle meets the map boundary, the interactive edge crossing the obstacle and the map boundary is not allowed to appear.
Furthermore, the visual field triangle and the discrete function Route are introduced to improve the JPS path optimization strategy, specifically, whether turning nodes are located in the coverage area of the triangle is judged by introducing the visual field triangle, and the cost value G (x) between the turning points and the father node of the current node is obtained through calculation to be compared, so that the optimal turning points are selected, and the shortest path optimization is realized. The discrete function is the discretized path point position information between two nodes, and the view triangle is formed by a first unreachable path, a former reachable path and an original path which are detected by the view line according to the sequence of the discrete paths.
Further, the view accessibility optimization based on Angle-Propagation Theta algorithm visibility Angle is specifically to replace the reachability detection between nodes with the visibility Angle detection. And constraining and adjusting the size of the visual angle through 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 range of the visible angle. And directly judging whether an reachable path exists between the new node and the father node or not through calculating the visible angle range of the road section to be optimized and through the angle range. Since the time to calculate the Angle is essentially constant, the consumption of Angle-Propagation Theta in expanding the vertices is no longer linear with the number of grids, but becomes a constant magnitude.
Further, the bidirectional optimization algorithm is specifically in the process of performing the JPS path optimization. And (3) simultaneously performing visual Angle retrieval based on Angle-Propagation Theta from the starting point and the target point of the path, continuously updating the Route discrete functions of the two parties according to the position information of the succession search point, further improving the retrieval efficiency of the two parties, and further reducing the operation time and complexity of the algorithm. In the bidirectional optimization algorithm, a start point discrete function is first set to Route (< Sstart,., send >), and a path discrete function of a target point is set to Route (< Send,., sstart >). The two-party Route function is updated each time one finds the next inheritance search point. The double-party function only needs to record the discrete paths from the own current search point to the other current search point, and the paths outside the discrete nodes are unidirectional reachable paths without detection.
The invention has the advantages and beneficial effects as follows:
the invention firstly provides a JPS path optimization method based on an Angle-Propagation Theta algorithm, which combines the JPS algorithm adapting to a grid vertex map with the visual Angle proposed in the Angle-Propagation Theta, reduces the path length on the premise of sacrificing efficiency, reduces the efficiency of the unidirectional optimization algorithm along with the increase of the map area and complexity under the same map condition, reduces the path length by 4-14% compared with the JPS algorithm, and reduces the searching efficiency by 16.7-52%.
Aiming at the problems of multiple iterative times and low calculation efficiency of a unidirectional optimization algorithm, the invention provides a bidirectional optimization strategy. The searching is carried out simultaneously from the path starting point and the target point, the searching efficiency is improved compared with the unidirectional optimization algorithm, the time consumption is reduced by 9.2% -24.2%, and meanwhile, the path optimization capability is as excellent as that of the unidirectional optimization algorithm.
Based on four different types of sizes, the grid map with the same obstacle ratio is subjected to path finding simulation comparison by using an A-algorithm, a JPS path optimization algorithm based on an Angle-Propagation Theta algorithm and a double-algorithm optimization algorithm, and experimental results show that the bidirectional optimization method provided by the invention can shorten the overall path length by 4% -14% on the basis of keeping the path searching efficiency.
Drawings
FIG. 1 is a schematic diagram of a JPS algorithm forced neighbor and neighbor pruning algorithm based on a grid vertex map;
FIG. 2 is a view triangle selection and coverage area diagram;
FIG. 3 is a range section within a viewing angle;
FIG. 4 is a diagram of a JPS path optimization procedure based on Angle-Propagation Theta;
FIG. 5 is a process diagram of a bi-directional optimization algorithm;
FIG. 6 is a flow chart of JPS path optimization based on Angle-Propagation Theta;
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 clearly and specifically described below with reference to the drawings in the embodiments of the present invention. The described embodiments are only a few 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 as follows:
s11, if the current node x is a starting point, and the parent node does not exist in the node x, the neighbor pruning rule does not exist, and the search direction is eight directions of the adjacent nodes.
S12, the current node x is not an initial node, parent nodes parent (x) to x of the node x are straight line searching 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 the path is less than or equal to a path from parent (x) to n through x, 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 x node are diagonal search directions, and n is a neighbor node of the x node. If there is a path from parent (x) to n that does not pass through x, and the path is smaller than the path from parent (x) to n through x, the next node must not reach n, i.e. the n node is pruned.
The forced neighbor judgment rule is: and if barriers exist in 8 neighbors of the current node x, n is a non-barrier neighbor node of the current node in a non-search direction, the distance cost of a parent node parent (x) of x to reach n through x is small compared with the distance cost of any path which does not reach n through x, and n is called a forced neighbor of x.
S2, based on the defects of the visual field line method, the invention provides a steering angle judgment rule for optimization. And judging whether a turning node is located within the coverage range of the field triangle by introducing the field triangle, comparing the calculated turning node with a cost value G (x) between the current node and a father node of the current node, and selecting an optimal turning point to realize shortest path optimization. The specific steps are as follows:
s21, the current jump point is set to be the point S, the parent node of the S is set to be the point p (S), and the path sub-jump point of the S is set to be the point e.
S22, detecting LOS reachability between p (S) point and e point, if the LOS reachability is achieved, 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) to p (S)>s->e is changed into p(s)>And e, completing optimization. If not, discretizing the path between the s node and the e node into Route according to the grid vertex<s,1,2,3,...,e>). From the s node, p(s) and Route are calculated according to the sequence<s,1,2,3,...,e>) Reachability of the internal node. If the first unreachable path is encountered, set it to L m Corresponding Route<s,1,2,3,...,e>) Node R in (a) m 。L m-1 For the previous discrete point reachable path, corresponding Route<s,1,2,3,...,e>) Middle node R m-1 。
S23, L m-1 、L m The triangle area generated with the original path from the s node to the e node is set as a field triangle, as shown in fig. 2. Searching the included barrier vertex (the side of the barrier at the path angle less than 180 degrees before optimization) in the triangle area (including triangle boundary) and calculating the cost value G (x) between the barrier vertex 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 m-1 As the S node, the search proceeds to steps S22 and S33.
S3, as shown in FIG. 3, the Angle-Propagation Theta algorithm sets the visibility Angle [ lb (S), ub (S) ] of the current node to [ - ++infinity ], where lb (S) is the lower Angle region and ub (S) is the upper Angle region when expanding from one node to the other. And then, checking other limiting conditions to conduct angle constraint on lb(s) and ub(s). The specific constraint conditions are as follows:
s31, the current node S is a vertex of the obstacle grid, and all other neighbor vertices z of the current grid satisfy one of the following conditions, where lb (S) =0 of the current node.
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 a 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)＝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 a neighboring node S 'of the current node S, if S' meets all the following conditions:
s'∈closedlist (7)
parent(s)＝parent(s') (8)
s'≠Sstart (9)
if lb (s ') +θ (s, parent(s), s ') is less than or equal to 0 between the s node and the s ' node
lb(s)＝max(lb(s),lb(s')+θ(s,parent(s),s')) (10)
If ub (s ') +θ (s, parent(s), s ') is greater than or equal to 0 between the s node and the s ' node
ub(s)＝min(ub(s),ub(s')+θ(s,parent(s),s')) (11)
S34, for a neighboring node S 'of the current node S, if S' meets all the following conditions:
c(parent(s),s')<c(parent(s),s) (12)
parent(s)≠s' (13)
if the viewing angle satisfies θ (s, parameter(s), s) <0
lb(s)＝max(lb(s),θ(s,parent(s),s')) (15)
If the viewing angle satisfies θ (s, parameter(s), s) >0
ub(s)＝min(ub(s),θ(s,parent(s),s')) (16)
And θ (s, parent(s), s ') is the included angle between the straight line formed by the parent node parent(s) of the current node s and the current node and the straight line formed by the parent(s) and the neighbor node s' of the current node. And θ (s, parent(s), z) is the included angle between the straight line formed by the parent node parent(s) of the current node s and the current node and the straight line formed by the parent(s) and the same grid neighbor vertex z 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 a cost value between a neighbor node s' of the current node and a 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(s) of the current node. The close list is a list of close nodes among the JPS algorithms. Sstart is the path initiation node.
After the angle constraint is completed, whether LOS accessibility exists between the adjacent node and the father node of the current node can be judged only by checking whether lb(s) is less than or equal to theta (s, parent(s), s') is less than or equal to ub(s) when the angle constraint is extended from the current node to any adjacent node, as shown in fig. 4.
S4, as shown in FIG. 5, the bidirectional optimization algorithm performs visual Angle search based on Angle-Propagation Theta from the starting point and the target point of the path at the same time, and continuously updates Route discrete functions of both sides according to the position information of the subsequent search point, so that the search efficiency of both sides is further improved, and the operation time and complexity of the whole algorithm are further reduced. The flow chart of the bidirectional optimization algorithm is shown in fig. 7, and the specific flow is as follows:
s41, selecting a starting point and a target point as initial search points of the two parties.
S42, LOS reachability detection is carried out between the two search points.
S43, if the algorithm is reachable, ending the algorithm, and if the algorithm is not reachable, continuing to step S44.
S44, updating the Route functions of the two parties, and discretizing paths between the search points of the two parties according to grid information contained in the Route functions.
S45, starting from the search points of the two parties, calculating the angle range of the visual angle between each node and the search point according to a discrete sequence. And find the first unreachable path L generated in the two-party searching process m And an unreachable path point R m 。
S46, both need to be in the range L m ，L m-1 And selecting an optimal turning point in the field triangle area formed by the combination of the original path segments, and inheriting the next arbitrary searching point.
S47, the step jumps to S42 to execute the next round of search.
Claims (9)
1. The JPS path optimization method based on the improvement of the Angle-Propagation Theta algorithm is characterized by comprising the following steps of:
s1, performing grid vertex map adaptation on a JPS algorithm, improving a pruning neighbor rule and a judgment standard of a forced neighbor of the JPS algorithm, modifying a path finding process of the JPS algorithm, and improving a robot moving path rule according to positions and the number of obstacles in the grid vertex map;
s2, improving a traditional JPS path optimization strategy by introducing a field triangle and a discrete function Route;
s3, calculating the visible Angle range of the road section to be optimized by utilizing an Angle-Propagation Theta algorithm, and directly judging whether an reachable path exists between a new node and a father node or not through the visible Angle range, wherein the method specifically comprises the following steps:
s31, the current node S is the vertex of the obstacle grid, and other neighbor vertexes z of the current grid meet one of the following conditions, and lb (S) =0 of the current node;
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 a 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 a neighboring node S 'of the current node S, if S' meets all the following conditions:
s'∈closedlist (7)
parent(s)＝parent(s') (8)
s'≠Sstart (9)
if lb (s ') +θ (s, parent(s), s ') is less than or equal to 0 between the s node and the s ' node
lb(s)＝max(lb(s),lb(s')+θ(s,parent(s),s')) (10)
If ub (s ') +θ (s, parent(s), s ') is greater than or equal to 0 between the s node and the s ' node
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 θ (s, parameter(s), s) <0
lb(s)＝max(lb(s),θ(s,parent(s),s')) (15)
If the viewing angle satisfies θ (s, parameter(s), s) >0
ub(s)＝min(ub(s),θ(s,parent(s),s')) (16)
Wherein θ (s, parent(s), s ') is an included angle between a straight line formed by parent(s) of the current node s and a straight line formed by parent(s) and a neighbor (s') of the current node, θ (s, parent(s), z) is an included angle between a straight line formed by parent(s) and the current node and a straight line formed by parent(s) and a same grid neighbor vertex z of the current node, c(s), s) is a cost value between the current node s and its parent(s), c(s), s ') is a cost value between the neighbor (s') of the current node and the parent(s), c (parent(s), z) is a cost value between the same grid neighbor vertex z of the current node and the parent(s), c (parent) is a closed node list of the JPS algorithm, and Sstart is an initial path list;
and S4, performing Angle-Propagation Theta-based visual Angle retrieval simultaneously 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 subsequent search point.
2. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 1, wherein the JPS path optimization method is characterized in that: the grid vertex map adaptation includes:
s11, changing the positions of the path starting point and the target point from the grid center to the grid vertex;
s12, changing the neighbor grid into a neighbor node in the algorithm route searching process, wherein the node neighbor domain is changed from 8 grids into 4 grids and 8 vertexes;
s13, the forced neighbor and neighbor pruning rule of the JPS algorithm is changed from rasterization to node-ization, wherein the pruning path is changed from continuous grids to continuous nodes.
3. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 2, wherein: the robot moving path rule 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 condition of multiple combined grid obstacles exists, allowing the mobile robot to walk along the whole boundary of the obstacle, but not allowing the mobile robot to cross the boundary edges between single grid obstacles and obliquely cross the obstacle;
(3) If there is a situation where the obstacle meets the map boundary, the interactive edge crossing the obstacle and the map boundary is not allowed to appear.
4. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 1, wherein the JPS path optimization method is characterized in that: 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 the adjacent nodes;
(2) The current node x is a non-initial node, parent nodes parent (x) to x of the node x are straight line searching directions, n is a neighbor node of the node x, and if paths from parent (x) to n do not pass through x exist, and the paths are smaller than or equal to paths from parent (x) to n through x, the node n is pruned;
(3) The current node x is a non-initial node, parent nodes parent (x) to x of the node x are diagonal search directions, n is a neighbor node of the node x, and if paths from parent (x) to n do not pass through x exist, the paths are smaller than paths from parent (x) to n through x, then the node n is pruned;
the forced neighbors are: and if barriers exist in 8 neighbors of the current node x, n is a non-barrier neighbor node of the current node in a non-search direction, the distance cost of a parent node parent (x) of x to reach n through x is small compared with the distance cost of any path which does not reach n through x, and n is called a forced neighbor of x.
5. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 1, wherein the JPS path optimization method is characterized in that: the step S2 specifically includes introducing a view triangle to determine whether a turning node is located within the coverage area of the view triangle, calculating a cost value G (x) between the turning node and a parent node of the current node, comparing the calculated value G (x), and selecting a turning point corresponding to the minimum cost value as an optimal turning point.
6. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 5, wherein: the view triangle is composed of a first unreachable path, a previous reachable path and an original path which are used for performing view line detection according to a discrete path sequence.
7. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 6, wherein: the field triangle is specifically determined by the following method:
s21, setting the current jump point as an S point, setting a parent node of the S as a p (S) point, and setting a path sub-jump point of the S as an e point;
s22, detecting the reachability between the p (S) point and the e point, if the reachability is detected, discarding the S node, changing the parent 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 finish optimization; if not, discretizing paths between the s node and the e node into Route (s < 1,2,3,.. E >) according to grid vertexes, and calculating the reachability of the nodes in p(s) and Route (s < 1,2,3,.. E >) in sequence from the s node respectivelySex, if the first unreachable path is encountered, set it to L m Node R in the corresponding Route (< s,1,2,3,., e >) m ，L m-1 For the previous discrete point reachable path, node R in Route (< s,1,2,3,., e >) m-1 ，
S23, L m-1 、L m The triangle area generated with the original path from the s node to the e node is set as a field triangle.
8. The JPS path optimization method based on Angle-Propagation Theta algorithm improvement according to claim 1, wherein the JPS path optimization method is characterized in that: the step S4 specifically includes the steps of,
s41, selecting a starting point and a target point as initial search points of both sides;
s42, LOS reachability detection is carried out between the search points of the two parties;
s43, if the algorithm is reachable, ending the algorithm, and if the algorithm is not reachable, continuing to step S44;
s44, updating Route functions of the two parties, and discretizing paths between the search points of the two parties according to grid information contained in the Route functions;
s45, starting from the search points of both sides, sequentially calculating the angle range of the visual angle between each node and the search point according to discrete order, and searching for a first unreachable path L generated in the process of both sides searching m And an unreachable path point R m ；
S46, both need to be in the range L m ，L m-1 Selecting an optimal turning point in a field triangle area formed by combining the original path sections, and inheriting the optimal turning point to the next arbitrary searching point;
s47, the step jumps to S42 to execute the next round of search.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed, is operable to carry out the path optimization method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111477785.8A CN114353814B (en) | 2021-12-06 | 2021-12-06 | JPS path optimization method based on Angle-Propagation Theta algorithm improvement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111477785.8A CN114353814B (en) | 2021-12-06 | 2021-12-06 | JPS path optimization method based on Angle-Propagation Theta algorithm improvement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114353814A CN114353814A (en) | 2022-04-15 |
| CN114353814B true CN114353814B (en) | 2023-11-17 |
Family
ID=81098012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111477785.8A Active CN114353814B (en) | 2021-12-06 | 2021-12-06 | JPS path optimization method based on Angle-Propagation Theta algorithm improvement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114353814B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8660789B2 (en) * | 2011-05-03 | 2014-02-25 | University Of Southern California | Hierarchical and exact fastest path computation in time-dependent spatial networks |
| US11580351B2 (en) * | 2018-11-06 | 2023-02-14 | Microsoft Technology Licensing, Llc | Automated personalized classification of journey data captured by one or more movement-sensing devices |
| US20210270629A1 (en) * | 2020-02-27 | 2021-09-02 | Here Global B.V. | Method and apparatus for selecting a path to a destination |
-
2021
- 2021-12-06 CN CN202111477785.8A patent/CN114353814B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
|---|
| 一种基于地形方向通行性的改进Theta_算法;王琼 等;空间科学学报;第36卷(第3期);全文 * |
| 基于地图预处理及改进A~_算法的路径规划;余文凯 等;高技术通讯;第30卷(第4期);全文 * |
| 基于迭代无迹H_∞滤波的移动机器人SLAM;罗元 等;计算机科学;第44卷(第8期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114353814A (en) | 2022-04-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110887484B (en) | Mobile robot path planning method based on improved genetic algorithm and storage medium | |
| CN110231824B (en) | Intelligent agent path planning method based on straight line deviation method | |
| CN109116841B (en) | Path planning smooth optimization method based on ant colony algorithm | |
| CN110487279B (en) | Path planning method based on improved A-Algorithm | |
| CN111811514B (en) | Path planning method based on regular hexagon grid jump point search algorithm | |
| CN111174798A (en) | Foot type robot path planning method | |
| CN112034836A (en) | Mobile robot path planning method for improving A-x algorithm | |
| WO2023155371A1 (en) | Stable movement global path planning method for indoor mobile robot | |
| CN111610786B (en) | Mobile robot path planning method based on improved RRT algorithm | |
| CN115079705A (en) | Routing planning method for inspection robot based on improved A star fusion DWA optimization algorithm | |
| CN113485369A (en) | Indoor mobile robot path planning and path optimization method for improving A-x algorithm | |
| CN111323016A (en) | Mobile robot path planning method based on self-adaptive ant colony algorithm | |
| CN112985408B (en) | Path planning optimization method and system | |
| CN112486178A (en) | Dynamic path planning method based on directed D (delta) algorithm | |
| CN110954124A (en) | Adaptive path planning method and system based on A-PSO algorithm | |
| CN114353814B (en) | JPS path optimization method based on Angle-Propagation Theta algorithm improvement | |
| CN110032182A (en) | A kind of fusion Visual Graph method and stablize sparse random quickly tree robot planning algorithm | |
| CN112947486A (en) | Path planning method and chip of mobile robot and mobile robot | |
| CN114779772B (en) | Path planning method and device integrating global algorithm and local algorithm | |
| CN114281084A (en) | Intelligent vehicle global path planning method based on improved A-x algorithm | |
| CN114545921B (en) | Unmanned vehicle path planning algorithm based on improved RRT algorithm | |
| CN117124335B (en) | Improved RRT path planning method based on path marking backtracking strategy | |
| CN117075607A (en) | Unmanned vehicle path planning method suitable for improving JPS in complex environment | |
| CN117249842A (en) | Unmanned vehicle mixed track planning method based on track smooth optimization | |
| CN114564023B (en) | Jumping point search path planning method under dynamic scene |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |