CN108444490B - Robot path planning method based on depth fusion of visible view and A-x algorithm - Google Patents
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Abstract
The invention discloses a robot path planning method based on depth fusion of a visible image and an A-star algorithm, which comprises the following steps: (1) determining a starting point s and a target point g, and scanning by using a computer to construct a map environment; (2) initializing each node, setting a default value, and defining current of a current access node; (3) connecting the current access node current with the target point g to construct a crossing line, and carrying out related judgment on the crossing line; (4) adding the nodes meeting the requirements into a node list to be accessed, evaluating by utilizing an evaluation function F (n), and taking out the node n with the minimum F value; (5) connecting the current access node current with the node n to construct a crossing line for relevant judgment; (6) and constructing the robot path by following the father node. The method is simple, the rule is simplified, the response speed is high, and the operation efficiency is high.
Description
Technical Field
The invention relates to a robot path planning method, in particular to a robot path planning method based on visual and A-x algorithm depth fusion.
Background
In the robot path planning method, the combination of the traditional visual map algorithm and the search algorithm is a global planning algorithm, and the optimal path is found by using the search algorithm after the complete model of the environment map is constructed. However, this method has the following drawbacks: (1) as the complexity of the environment increases, the operating speed and efficiency thereof are greatly reduced; (2) the method usually ignores the structure of the robot, and in practical application, the robot is usually collided with the obstacle.
At present, the improvement of the method is mainly to simplify the visual graph, reduce the number of nodes and visual lines and then apply a search algorithm. However, the improved method has strong dependence on the environment, if the environment changes due to an emergency, modeling needs to be carried out again, and then path searching is carried out, so that the method has poor self-adaptive capacity, slow control response and low operation efficiency.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a robot path planning method based on the deep fusion of a visual algorithm and an A-star algorithm aiming at the defects of the prior art, and the robot path planning method is strong in self-adaptive capacity, fast in control response and high in operation efficiency.
The technical scheme is as follows: the invention relates to a robot path planning method based on depth fusion of a visible image and an A-x algorithm, wherein the edge width of a robot walking mechanism is L, and the method is characterized by comprising the following steps:
s1, determining a starting point S and a target point g of the robot, scanning a map environment related to the motion of the robot by using a computer, identifying all obstacles in the map environment, enveloping the obstacles by using a closed polygon, calculating the parallel envelope of the obstacles to be (L/2) × 1.1, and keeping a graph;
s2, initializing each node in the map environment, setting default values, and assigning values to the access start node, and initializing functions and start node assignment functions as follows: (1) g (n) ═ inf identity F (n) ═ inf identity, (2)
G(s) ═ 0, (4) f(s) ═ h(s), (5) current ═ s, where g (n) represents the distance between node n and starting point s, and infinity represents infinity; h (n) is a heuristic function, H (n) represents an estimate of the distance between node n and target point g; f (n) is an evaluation function, f (n) g (n) + h (n), and f (n) represents an estimation of a distance from a start point s to a target point g via a node n;
and S3, if the value of min (F) is not infinity, carrying out overall path planning by adopting a global path planning algorithm.
The technical solution of the present invention is further defined in that when the global path planning algorithm is adopted to perform the overall path planning in step S3, the specific steps are as follows:
q1, if the value of the evaluation function F (N) of the current access node current is not infinite, constructing a current access node current which is a crossing line segment current-g between a starting point s and a target point g, wherein the crossing line segment intersects with N obstacles (N is an integer greater than 0), taking the first obstacle intersected with the crossing line segment current-g, respectively taking the node farthest away from the crossing line on the parallel envelope of the obstacle on the two sides of the crossing line, judging whether the node exceeds the boundary of the map environment, and if so, deleting the node; if not, adding the node into a node list to be accessed, and updating the G value, the F value and the parent node parent of the node;
q2, whether N is larger than 1, if not, executing a step Q3; if yes, continuing to take the farthest point from the crossing line on the two sides of the crossing line of each obstacle, wherein the number of the farthest points is (N-1) × 2, the farthest points are named as Ai (i is 1.. 2. (N-1)), connecting sAi and judging whether the obstacle intersects with the obstacle, and if yes, executing a step Q3; if not, judging whether the node exceeds the processing boundary, and if so, deleting the node; if the node is not added as the node to be accessed, updating the G value, the F value and the parent node parent;
q3, evaluating all the nodes to be accessed by utilizing an evaluation function F (n), and taking the minimum node as n; connecting the current access node current with a node n as a traversing line segment current-n, if the traversing line segment current-n is not intersected with the barrier, updating the node n into the current access node current, moving the node n out of the list of the nodes to be accessed, and continuously executing the step Q1 until the traversing is completed to the list of the nodes to be accessed;
q4, constructing an optimal path between the starting point s and the target point g, and finding the starting point s from the target point g by following the father node to obtain a planned path.
Further, in step Q1, if N is 0, the crossing line between the start point s and the target point g is the planned path.
Further, in step Q3, if the crossing segment current-n intersects with the obstacle, then: if the crossing line segment current-N intersects with N barriers, moving N out of the node to be accessed, taking two points of the first barrier from the line segment current to the N in the direction, which are farthest from two sides of the line segment current-N, as nodes, adding two nodes of the first barrier from the line segment current to the N in the direction into a node list to be accessed, and updating the G value, the F value and the father node of the nodes;
respectively connecting the current access node current with the two farthest points of the 2-Nth barrier from the two sides of the off-line segment current-N in the direction from the line segment current to N to form 2 x (N-1) line segments, and judging whether the 2 x (N-1) line segments are intersected with the barrier in the direction from the line segment current to N; if all the intersections, jumping to step Q3; if the two nodes are not intersected, judging whether the node of the 2-N barriers from the current to the N-star direction of the line segment exceeds the physical boundary of the map environment or not; if the physical boundary of the map environment is exceeded, deleting the node; and if the physical boundary of the map environment is not exceeded, adding the corresponding node into the node list to be accessed, updating the G value, the F value and the father node of the node, and then jumping to the step Q3.
Further, the function of updating the G value, F value and parent node of node n is:
an evaluation function f (n) ═ h (n) + g (n), in which,
x n and y n Respectively representing the abscissa and ordinate, x, of the node n g And y g Respectively representing the abscissa and ordinate of the target point g; if G (n) > G (current) + distance (current to n), G (current) represents the G value of the current access node, and distance (current to n) represents the distance from the current access node to the node n; parent node n.parent of node n is curr ent.
Has the advantages that: the method generates a necessary visual image by setting rules and simultaneously introduces an enlightening function H (n) for searching, simplifies the rules, deeply fuses the visual image and an A-algorithm, searches and continuously updates nodes to be accessed in the process of establishing the image, improves the response speed of the path planning of the robot, greatly reduces the nodes needing to be accessed, and enhances the self-adaption capability and the intelligent degree of the nodes; the invention searches according to the dynamic establishment path of the starting point, the establishment of the visual is dynamic, and the influence of the environment change on the visual is little; according to the invention, the judgment rule is set, so that the visual graph is simplified, unnecessary nodes are deleted, and the robot searching efficiency is improved; according to the invention, by setting the parallel envelope of the barrier, the collision between the robot and the barrier in the moving process is avoided, the collision probability is reduced, and the reliability is enhanced.
Drawings
FIG. 1 is a schematic diagram of a parallel envelope of the obstacles of the present invention;
FIG. 2 is a first pictorial view of the present invention;
FIG. 3 is a second pictorial view of the present invention;
FIG. 4 is a third pictorial view of the present invention;
FIG. 5 is a fourth pictorial view of the present invention;
FIG. 6 is a fifth pictorial view of the present invention;
fig. 7 is a sixth view of the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the embodiments.
Example 1: a robot path planning method based on visual and A-algorithm depth fusion is characterized in that a global path planning algorithm is adopted to calculate an overall path, if the path exists, the robot is guided into the path and advances according to the planned path until the end point is reached, and if the path does not exist, the robot path planning method is ended.
The robot walking module is mounted in a four-wheel drive structure, the mechanical arm module is mounted on the robot walking module and is a serial-type six-degree-of-freedom mechanical arm, the edge structure of the robot depends on walking parts, and the width of the robot is L. In this embodiment, the detailed operation steps illustrated in the maps in fig. 2 to 7 are as follows:
s1, determining a starting point S and a target point g of the robot, scanning a map environment related to the motion of the robot by using a computer, identifying all obstacles in the map environment, enveloping the obstacles by using a closed polygon, calculating the parallel envelope of the obstacles to be (L/2) × 1.1, and keeping a graph; a schematic diagram of a parallel envelope of the obstacle is shown in fig. 1.
S2, initializing each node in the map environment, setting default values, and assigning values to the access start node, and initializing functions and start node assignment functions as follows: (1) g (n) ═ inf identity F (n) ═ inf identity, (2)
G(s) ═ 0, (4) f(s) ═ h(s), (5) current ═ s, where g (n) represents the distance between node n and starting point s, and infinity represents infinity; h (n) is a heuristic function, H (n) represents an estimate of the distance between node n and target point g; f (n) is an evaluation function, f (n) ═ g (n) + h (n), and f (n) represents the distance from the starting point s to the target point g via the node nEstimating the distance; current currently accesses the node.
And S3, if the min (F) value is infinity, reporting that the path is empty, and finishing path planning.
And if the value of min (F) is not infinity, performing overall path planning by adopting a global path planning algorithm.
When the global path planning algorithm carries out overall path planning, the specific steps are as follows:
q1, if the value of the evaluation function F (n) of the current access node current is not infinite, constructing the current access node current, i.e. the crossing line segment current-g between the starting point s and the target point g.
And the crossing line segment intersects with the N obstacles, and if N is 0, the crossing line between the starting point s and the target point g is a planned path. If (N is an integer larger than 0), taking a first barrier intersected with the current-g of the crossing line segment, respectively taking nodes which are farthest away from the crossing line on the parallel envelope of the barrier at two sides of the crossing line, judging whether the nodes exceed the boundary of the map environment, and if so, deleting the nodes; and if not, adding the node into the node list to be accessed, and updating the G value, the F value and the parent node parent of the node.
In this embodiment, the crossing line segment current-g intersects with the obstacles, that is, the line segment current-g intersects with N obstacles (N is an integer greater than 0), in this embodiment, N is 3, that is, the crossing line segment intersects with 3 obstacles, which are 2, 3, and 4, and on both sides of the crossing line, each obstacle is respectively located at the farthest point from the crossing line, which is B1, B2, C1, C2, D1, and D2.
Taking a first barrier intersected with the crossing line current-g, respectively taking nodes (B1 and B2) which are farthest from the crossing line on the parallel envelope of the barriers on two sides of the crossing line, judging whether the nodes exceed the boundary of the map environment, and if so, deleting the nodes; if not, adding the node into the list of nodes to be accessed, and as a result, two node Bs 1 、B 2 And adding a node list to be accessed, and updating the G value, the F value and the parent node parent of the node. Connection of sC 1 ，sC 2 ，sD 1 ，sD 2 Judging whether the four connecting lines intersect with the barrier or not, and obtaining the whole resultThe portions intersect as shown in fig. 2.
G(n)＝infinityF(n)＝inf inity
G(s)＝0 F(s)＝H(s)
current＝s
At this time, it is determined whether N is greater than 1, and if N is not greater than 1, Q3 is skipped, and if N is greater than 1, step Q2 is executed, where N is 3 in this embodiment, so that step Q2 is continuously executed.
Q2, whether N is larger than 1, if not, executing a step Q3; if yes, continuing to take the farthest point from the crossing line on the two sides of the crossing line of each obstacle, wherein the number of the farthest points is (N-1) × 2, the farthest points are named as Ai (i is 1.. 2. (N-1)), connecting sAi and judging whether the obstacle intersects with the obstacle, and if yes, executing a step Q3; if not, judging whether the node exceeds a processing boundary, and if so, deleting the node; if the node is not added as the node to be accessed, updating the G value, the F value and the parent node parent;
q3, using evaluation function F (n) to evaluate all the nodes to be accessed, taking the minimum node as n, and obtaining the node n ═ B with the minimum evaluation function value 1 . The current access node current is connected to node n as a line segment sB1, as shown in FIG. 3.
And judging whether the crossing line intersects with the barrier, if the crossing line segment current-n does not intersect with the barrier, updating the node n to be the current access node current, and moving the node n out of the list of the nodes to be accessed, wherein the current is n. And continuing to execute the step Q1 until the traversal is completed and the node list to be accessed is obtained.
If the crossing line segment current-n intersects with the barrier, the embodiment intersects with the barrier 1, then n x is moved out of the node to be accessed, two points of the barrier from the current segment to the n x direction, which are farthest from two sides of the crossing line segment current-n x, are taken as nodes, two nodes of the barrier from the current segment to the n x direction are added into the node list to be accessed, and the G value, the F value and the father node of the nodes are updated. And namely, moving the node list to be accessed out of the node list to be accessed by the node B1, taking the farthest nodes A1 and A2 at two sides of the crossing line for the barrier 1, adding the nodes into the node list to be accessed, and updating the G value, the F value and the parent node parent of the nodes.
In this embodiment, the execution of step Q1 is continued, and the result is n ═ a 2. Connecting current and constructing a crossing line for n ×, namely sA2, judging whether the crossing line intersects with the barrier or not as shown in FIG. 4, if not, updating the node as the current access node and moving out the list of the nodes to be accessed: current is n, namely A 2 And jumps to Q1. Connecting node current and end point g, i.e. connection starting point A 2 And an end point g, FIG. 5, configured to cross line A 2 g, result B1 is added to the list of nodes to be accessed, and B 1 .parent＝A 2 (ii) a Repeating the step Q3: results n ═ B 1 (ii) a Connection A 2 And B 1 In FIG. 6, the result current is B 1 And skipping to step 3; crossing line B 1 g intersects with the obstacles 3 and 4, and is judged to be C 1 、C 2 、D 1 Adding a node list to be accessed and updating each value; repeating the step Q3, and judging that current is equal to C 1 Then, jumping to step 3, and repeating the above actions.
Respectively connecting the current access node current with the two farthest points of the 2-Nth barrier from the two sides of the off-line segment current-N in the direction from the line segment current to N to form 2 x (N-1) line segments, and judging whether the 2 x (N-1) line segments are intersected with the first barrier in the direction from the line segment current to N; if all the intersections, jumping to step Q3; if the two nodes are not intersected, judging whether the node of the 2-N barriers from the current to the N-star direction of the line segment exceeds the physical boundary of the map environment or not; if the physical boundary of the map environment is exceeded, deleting the node; and if the physical boundary of the map environment is not exceeded, adding the corresponding node into the node list to be accessed, updating the G value, the F value and the father node of the node, and then jumping to the step Q3.
Q4, g.parent ═ current, the optimal path between the starting point s and the target point g is constructed, and the starting point s is found from the target point g by following the parent node, thereby obtaining the planned path.
In the above step, the functions of updating the G value, F value and parent node of node n are:
an evaluation function f (n) ═ h (n) + g (n), in which,
x n and y n Respectively representing the abscissa and ordinate, x, of the node n g And y g Respectively representing the abscissa and ordinate of the target point g; if G (n) > G (current) + distance (current n), G (current) represents the G value of the current access node, and distance (current to n) represents the distance from the current access node to the node n; parent node n of node n is current.
In fig. 2 to 6, the thick solid line represents a crossing line, the dotted line represents an auxiliary connecting line, and the final g.parent is D through successive iterations 2 Starting from the g node, each node is continuously moved to its parent node until the starting point is reached, so that the construction of the optimal perfect path is completed, and the result is shown in fig. 7.
As noted above, while the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A robot path planning method based on depth fusion of a visual image and an A-x algorithm is characterized in that the robot path planning method comprises the following steps:
s1, determining a starting point S and a target point g of the robot, scanning a map environment related to the motion of the robot by using a computer, identifying all obstacles in the map environment, enveloping the obstacles by using a closed polygon, calculating the parallel envelope of the obstacles to be (L/2) × 1.1, and keeping a graph;
s2, initializing each node in the map environment, setting default values, and assigning values to the access start node, and initializing functions and start node assignment functions as follows: (1) g (n) ═ inf identity, f (n) ═ inf identity, (2)
(3) G(s) ═ 0, (4) f(s) ═ h(s), (5) current ═ s, where g (n) represents the distance between node n and starting point s, and infinity represents infinity; h (n) is a heuristic function, h (n) represents an estimation of the distance between the node n and the target point g, x and y are respectively the abscissa and the ordinate of the map environment; f (n) is an evaluation function, f (n) ═ g (n) + h (n), f (n) represents an estimation of the distance from the starting point s to the target point g via the node n;
s3, if the value of min (F) is not infinity, the overall path planning is carried out by adopting a global path planning algorithm;
when the global path planning algorithm is adopted to perform the overall path planning in step S3, the specific steps are as follows:
q1, if the value of the evaluation function F (N) of the current access node current is not infinite, constructing a current access node current which is a crossing line segment current-g between a starting point s and a target point g, wherein the crossing line segment intersects with N obstacles, N is an integer greater than 0, taking the first obstacle intersected with the crossing line segment current-g, respectively taking the node farthest away from the crossing line on the parallel envelope of the obstacle on the two sides of the crossing line, judging whether the node exceeds the boundary of the map environment, and if so, deleting the node; if not, adding the node into a node list to be accessed, and updating the G value, the F value and the parent node parent of the node;
q2, whether N is larger than 1, if not, executing a step Q3; if yes, continuing to take the farthest point from the crossing line on each obstacle on two sides of the crossing line, wherein the farthest points are (N-1) × 2 and named as Ai, wherein i is 1.. 2 × (N-1), connecting sAi and judging whether the obstacle intersects with the obstacle, and if yes, executing a step Q3; if not, judging whether the node exceeds the processing boundary, and if so, deleting the node; if the node is not added as the node to be accessed, updating the G value, the F value and the parent node parent;
q3, evaluating all the nodes to be accessed by utilizing an evaluation function F (n), and taking the minimum node as n; connecting the current access node current with a node n as a traversing line segment current-n, if the traversing line segment current-n is not intersected with the barrier, updating the node n into the current access node current, moving the node n out of the list of the nodes to be accessed, and continuously executing the step Q1 until the traversing is completed to the list of the nodes to be accessed;
q4, constructing an optimal path between the starting point s and the target point g, and finding the starting point s from the target point g by following the father node to obtain a planned path.
2. The method for planning a robot path based on the depth fusion of the visual map and the a-x algorithm according to claim 1, wherein in the step Q1, if N is 0, the crossing line between the starting point s and the target point g is the planned path.
3. A method for robot path planning based on visual and a-algorithm depth fusion according to claim 1, characterized in that in step Q3, if the crossing segment current-n intersects with the obstacle, then: if the crossing line segment current-N intersects with N barriers, N moves out the node to be accessed, two points of the first barrier in the direction from the line segment current-N to N and the two points farthest from the two sides of the line segment current-N are taken as nodes, two nodes of the first barrier in the direction from the line segment current-N to N are added into the node list to be accessed, and the G value, the F value and the father node of the nodes are updated;
respectively connecting the current access node current with the two farthest points of the 2-N barriers from the two sides of the off-line segment current-N in the direction from the segment current-N to form 2 x (N-1) line segments, and judging whether the 2 x (N-1) line segments are intersected with the barriers in the direction from the segment current-N to N; if all the intersections, jumping to step Q3; if not, judging whether the nodes of 2-N obstacles in the direction from current-N to N of the line segment exceed the physical boundary of the map environment or not; if the physical boundary of the map environment is exceeded, deleting the node; and if the physical boundary of the map environment is not exceeded, adding the corresponding node into the node list to be accessed, updating the G value, the F value and the father node of the node, and then jumping to the step Q3.
4. The method for robot path planning based on visual and A-algorithm depth fusion according to any one of claims 1 to 3, wherein the function for updating the G value, the F value and the parent node of the node n is as follows:
an evaluation function f (n) ═ h (n) + g (n), in which,
x n and y n Respectively representing the abscissa and ordinate of the node n, y g And y g Respectively representing the abscissa and ordinate of the target point g; if G (n)>G (current) + (current to n), then G (n)>G (current) + distance (current to n), G (current) represents the value G of the current access node, and distance (current to n) represents the distance between the current access node and the node n; parent n of node n is current.
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| CN110763247A (en) * | 2019-10-21 | 2020-02-07 | 上海海事大学 | Robot path planning method based on combination of visual algorithm and greedy algorithm |
| CN111047250A (en) * | 2019-11-25 | 2020-04-21 | 中冶南方（武汉）自动化有限公司 | Planning method for running path of crown block |
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