CN109445444B - Robot path generation method under barrier concentration environment - Google Patents
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Abstract
The invention relates to a robot path generating method under an obstacle concentration environment, which comprises the steps of firstly, carrying out path search by using an RRT algorithm; when the random point is invalid, the point is directly abandoned by the traditional RRT algorithm to be further judged; if the longest effective distance between the random point and the connection line of the target point is smaller than a threshold value, converting the random point and the connection line of the target point into an A-algorithm to search until the end, otherwise, continuing to search the RRT algorithm and repeating the judgment; the final path is composed of the path obtained by RRT search and the path obtained by A-search. Compared with the prior art, the method combines the advantages of A and RRT algorithms in the environment of concentrated obstacles, overcomes respective short boards, and ensures that the whole planning algorithm has the advantages of high efficiency, short search time and the like.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to a robot path generation method in an obstacle concentration environment.
Background
In order to make the robot walk in daily life, collision-free fixed-point movement of the robot is the most basic and irrecoverable problem, and the path planning algorithm provides possibility for solving the problem. Completeness and optimality are two important indexes for evaluating a robot path planning algorithm. The completeness refers to that the algorithm can find a feasible path in any scene, and the optimality refers to that the distance of the path found by the algorithm is shortest. In addition, the rapidity of the algorithm, i.e. the time required to obtain the path, is also an important reference index.
Existing path planning algorithms are mainly classified into two categories. The first type is a graph search algorithm represented by Dijkstra algorithm and a-x algorithm. The Dijkstra algorithm is an approximately exhaustive method, radiates around a starting point, and is optimal and complete, but as the scale of an application scene becomes larger, the disadvantage of the Dijkstra algorithm in the time consumption of searching becomes more obvious. The A algorithm is improved aiming at the problems of the Dijkstra algorithm, and the searching direction of the Dijkstra algorithm is guided to a certain extent by introducing a heuristic function mode, so that the aim of shortening the running time is fulfilled, but the corresponding path is changed from optimal to suboptimal, so that the A algorithm is a complete but non-optimal algorithm. The other type of algorithm is an algorithm based on a sampling mode represented by a fast-expansion random tree (RRT) and a variety thereof, the algorithm generates a tree structure in a random point scattering mode, and a feasible path is extracted from the tree structure.
The existing path planning algorithm has respective advantages and applicability in a small-scale simple scene, but is gradually not applicable in a large-scale complex scene. Consider the following practical application scenario, assuming that the robot needs to reach a specific location in a certain house along a long-distance open road to complete a specific task, the schematic diagram is shown in fig. 1, S is the starting location of the robot, G is the target location, and the black part is an area of different rows (i.e. an obstacle). If a graph search algorithm is adopted to search for a path, a large amount of search time is consumed due to the large range of the whole scene, although the random sampling algorithm can be rapidly expanded to the vicinity of a G point, the RRT algorithm is difficult to enter a target area (in a room) due to the existence of a narrow channel, and the existing algorithm is difficult to directly apply to the barrier concentration environment.
Disclosure of Invention
The present invention is directed to a method for generating a robot path in an obstacle concentrated environment, which overcomes the above-mentioned drawbacks of the prior art.
The purpose of the invention can be realized by the following technical scheme:
a robot path generating method under the concentrated environment of barrier, said method uses RRT algorithm to search and construct and expand the random tree at first, while producing the invalid random point, judge whether the longest effective distance on the connecting line of the random point and target point is smaller than the threshold value, if yes, convert into A algorithm and search to finish, if no, continue RRT algorithm search, and repeat the above-mentioned judgement; the final path is composed of the path obtained by RRT search and the path obtained by A-search.
Further, the method specifically comprises the following steps:
1) establishing a random tree;
2) generating a random point, judging whether the random point is in the barrier, if so, discarding the random point, repeating the step 2), and if not, executing the step 3);
3) obtaining a connecting line between a random point and the nearest point in the random tree, judging whether the connecting line passes through an obstacle, if so, executing the step 4), otherwise, reserving the connecting line, adding the random point into the random tree, and executing the step 5);
4) judging whether the longest effective distance between the random point and the target point on the connection line is smaller than a set threshold, if so, using an A-star algorithm to perform local search to the target point to obtain a first path, executing the step 6), and if not, returning to the step 2);
5) judging whether the random point is a target point, if so, executing the step 6), and if not, returning to the step 2);
6) and optimizing the random tree to obtain a second path, if the first path exists, connecting the first path and the second path to form a final path, and if the first path does not exist, directly taking the second path as the final path.
Further, in the step 4), the longest effective distance between the random point and the connection line of the target point is obtained by:
taking a replacing point on the connecting line in the step 3), wherein the replacing point is farthest from the closest point and a new connecting line formed between the replacing point and the closest point does not pass through an obstacle, and taking the connecting line distance between the replacing point and the target point as the longest effective distance.
Further, in the step 4), when the local search is performed by using the a-x algorithm, the substitute point is used as a starting point, and the substitute point is added to the random tree.
Compared with the prior art, the method provided by the invention takes the unconnected random points as a basis for judging whether the unconnected random points reach a small-scale complex area, if the threshold judgment result is that the unconnected random points are close to the target area, the A-star algorithm is used for searching, the target point is quickly reached, and finally two steps of paths are extracted and integrated, so that the method has the following beneficial effects:
1) aiming at the path planning problem under the environment of concentrating the obstacles, the invention firstly carries out rapid search under a large-scale simple environment, judges whether the small-scale complex environment is reached or not according to a threshold condition, and carries out rapid search by an A-x algorithm in the small-scale complex environment to obtain a better track, thereby effectively improving the track planning efficiency.
2) According to the invention, the unconnected random points are used as a basis for judging whether the unconnected random points reach a small-scale complex area, if the threshold judgment result is that the unconnected random points are close to the target area, the algorithm A is used for searching, the target point is quickly reached, and finally two steps of paths are extracted and integrated, so that the efficiency is improved.
3) The method makes full use of the rapidity of the RRT algorithm in a large-scale area and the adaptability of the A-x algorithm to narrow channels, makes up the inadaptation of the RRT algorithm to the environments such as the narrow channels, avoids the search of the A-x algorithm in a large-scale scene, and shortens the time.
Drawings
FIG. 1 is a schematic view of an obstacle focusing environment;
FIG. 2 is a schematic flow chart of the method of the present invention;
FIG. 3 is a schematic diagram of random point selection according to the present invention;
FIG. 4 is a schematic diagram of a random tree generation according to the present invention;
FIG. 5 is a schematic diagram illustrating the determination of the vicinity of a target area according to the present invention;
FIG. 6 is a schematic diagram of a search near a target area according to the present invention;
FIG. 7 is a schematic diagram of the optimization and integration of the path of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a robot path generation method under a concentrated environment of obstacles, which comprises the steps of firstly, searching by using an RRT algorithm to construct and expand a random tree, judging whether the longest effective distance between a random point and a connecting line of a target point is smaller than a set threshold value when an invalid random point is generated, if so, converting the longest effective distance into an A-x algorithm to search until the end, otherwise, continuing to search by using the RRT algorithm, and repeating the judgment; the final path is composed of the path obtained by RRT search and the path obtained by A-search. As shown in fig. 2, the method specifically includes the following steps:
in step S101, a random point is generated in the selected obstacle concentration environment, whether the random point is in the obstacle or not is determined, if yes, the random point is discarded, step S101 is repeated, and if not, step S102 is executed.
Step S102, obtaining a connecting line between the random point and a nearest point in the random tree, wherein the nearest point is a point in the random tree closest to the random point, judging whether the connecting line passes through an obstacle, if so, executing step S104, and if not, executing step S103.
Step S103, reserving the connection line, adding a random point into a random tree, taking the closest point as a father node, judging whether the random point is a target point, if so, executing step S107, and if not, returning to step S101.
Step S104, a replacing point on the connecting line is taken, the replacing point is farthest from the closest point, and a new connecting line formed between the replacing point and the closest point does not pass through an obstacle, and step S105 is executed.
Step S105, judging whether the distance between the substitute point and the target point is smaller than a set threshold, if so, executing step S106, and if not, returning to step S101.
And step S106, local searching is carried out to a target point by using the alternative point as a starting point and adopting an A-x algorithm, a first path is obtained, and step S107 is executed.
And S107, optimizing the random tree to obtain a second path, if the first path exists, connecting the first path and the second path to form a final path, and if the first path does not exist, directly taking the second path as the final path. The random tree is optimized specifically as follows: and extracting available paths on the random tree, and taking the shortest path as a second path.
Fig. 3-7 show the robot path generation process from the robot start position S to the target position G using the above method.
As shown in fig. 3, a random point is generated, such as R1, R2, etc., and if the random point falls within the obstacle (such as R2), the point is discarded.
As shown in fig. 4, a point (in this case, point S) on the random tree closest to the effective random point (taking R1 as an example) is found, if the connection line between the two points does not pass through the obstacle, the random point (point R1) is added into the random tree, and the closest point (point S) is used as its parent node, and if the connection line passes through the obstacle (such as the random point R3 and the closest point R1) on the tree), the next step is performed.
As shown in fig. 5, the path generation method of the present invention does not directly discard the random point connection when it passes through the obstacle, but performs an additional process. Taking a substitute point T3 which is farthest from the closest point R1 and is not on the obstacle on a connecting line of the random point R3 and the closest point R1, if the distance | GT3| between the substitute point and the target point is larger than a preset threshold value, considering that the random point is not near the target area, directly discarding the substitute point, and repeating the previous steps; if its distance is less than a threshold, e.g., | TmG |, where Rm is a random point, Nm is the corresponding closest point, and Tm is a replacement point, the replacement point Tm is added to the random tree with the closest point Nm as the parent node and it is considered that the vicinity of the target region has been reached.
As shown in fig. 6, if it is considered that the vicinity of the target area has been reached in the previous step, the substitute point Tm is used as a starting point, the target point G is used as an end point, a search is performed on the map using the a-x algorithm to find the first route, and if the vicinity of the target area is no longer reached, the previous steps are repeated.
As shown in fig. 7, an available path is extracted from the random tree and subjected to a certain optimization process, and a final path is obtained by connecting the obtained segment with the first path, as shown in a in the figure, in order to obtain the second path with the shortest path.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (1)
1. A robot path generation method under an obstacle concentration environment is characterized in that firstly, an RRT algorithm is used for searching and constructing an expanded random tree, when invalid random points are generated, whether the longest effective distance between the random points and a connecting line of a target point is smaller than a set threshold value or not is judged, if yes, the algorithm is converted into an A-x algorithm for searching till the end, if not, RRT algorithm searching is continued, and the judgment is repeated; the final path consists of a path obtained by RRT search and a path obtained by A search;
the method specifically comprises the following steps:
1) establishing a random tree;
2) generating a random point, judging whether the random point is in the barrier, if so, discarding the random point, repeating the step 2), and if not, executing the step 3);
3) obtaining a connecting line between a random point and the nearest point in the random tree, judging whether the connecting line passes through an obstacle, if so, executing the step 4), otherwise, reserving the connecting line, adding the random point into the random tree, and executing the step 5);
4) judging whether the longest effective distance between the random point and the target point on the connection line is smaller than a set threshold, if so, using an A-star algorithm to perform local search to the target point to obtain a first path, executing the step 6), and if not, returning to the step 2);
5) judging whether the random point is a target point, if so, executing the step 6), and if not, returning to the step 2);
6) optimizing the random tree to obtain a second path, if the first path exists, connecting the first path and the second path to form a final path, and if the first path does not exist, directly taking the second path as the final path;
in the step 4), the longest effective distance between the random point and the connection line of the target point is obtained by the following method:
taking a replacing point on the connecting line in the step 3), wherein the replacing point is farthest from the closest point and a new connecting line formed between the replacing point and the closest point does not pass through an obstacle, and the connecting line distance between the replacing point and the target point is taken as the longest effective distance;
in the step 4), when the partial search is performed by using the a-x algorithm, the substitute point is used as a starting point, and the substitute point is added to the random tree.
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| CN112711256B (en) * | 2020-12-23 | 2022-05-20 | 杭州电子科技大学 | Automatic generation method for target search observation points of mobile robot |
| CN113031599B (en) * | 2021-03-02 | 2024-05-07 | 珠海一微半导体股份有限公司 | Method for quickly locating robot with dynamically changed reference point, chip and robot |
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