CN109445444A - A kind of barrier concentrates the robot path generation method under environment - Google Patents
A kind of barrier concentrates the robot path generation method under environment Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
Abstract
The present invention relates to a kind of barriers to concentrate the robot path generation method under environment, and this method uses RRT algorithm to carry out route searching first；It when random point is invalid, is different from traditional RRT algorithm and directly gives up the point, and further judged；A* algorithm is converted into if the longest effective distance on the random point and target point line is less than threshold value to scan for end, otherwise continues RRT algorithm search, and repeat above-mentioned judgement；Final path is searched for gained path and A* search gained path by RRT and is formed.Compared with prior art, the present invention combines A* with the advantage of RRT algorithm in the case where barrier concentrates environment, overcomes respective short slab, so that entire planning algorithm has many advantages, such as that high-efficient, search time is short.
Description
Technical field
The present invention relates to a kind of technical field of robot control, concentrate the machine under environment more particularly, to a kind of barrier
People's path generating method.
Background technique
In order to allow among robot Stepping into daily life, it is also that can not return that the collisionless fixed point movement of robot, which is most basic,
The problem of keeping away, and path planning algorithm then provides possibility to solve this problem.Completeness and optimality are evaluation robot roads
Two important indicators of diameter planning algorithm.Wherein completeness refer under any scene that the algorithm can find one can walking along the street
Diameter, and the path distance that optimality then refers to that the algorithm is found is most short.In addition to this, the rapidity of algorithm to get arrive path institute
The time needed is also an important reference index.
Existing path planning algorithm is broadly divided into two major classes.The first kind is using dijkstra's algorithm and A* algorithm as representative
Graph search algorithm.Dijkstra's algorithm is a kind of method of approximate exhaustion, is radiated around centered on starting point, therefore the calculation
Method is optimal and complete, but as the scale of application scenarios becomes larger, and disadvantage of the dijkstra's algorithm in search time-consuming is just got over
Add obvious.A* algorithm dijkstra's algorithm there are aiming at the problem that improved, by way of introducing heuristic function
Guidance to a certain extent has been carried out to the direction of search of dijkstra's algorithm, thus achieved the purpose that shorten runing time,
But its corresponding path also becomes suboptimum from optimal, so A* algorithm is a kind of complete but non-optimal algorithm.Another kind of algorithm
Be quickly to expand random tree (RRT) and its mutation as the algorithm based on sample mode of representative, such algorithm by with
Machine spreads mode a little and generates a tree, therefrom extracts feasible path, and compared to graph search method, sharpest edges exist
In its rapidity, but the algorithm is that probability is complete and non-optimal, its effect is more especially under the special screnes such as slype
It is to have a greatly reduced quality.
Existing route planning algorithm has respective advantage and applicability under small-scale simple scenario, but multiple on a large scale
It will be applicable in gradually under miscellaneous scene.Consider following practical application scene, it is assumed that robot needs the spacious road along long range
Road reaches the specific position in some house and completes particular task, and schematic diagram is as shown in Figure 1, S is robot initial position, G
For target position, black portions are that can not go together region (i.e. barrier).If path is found using graph search algorithm, due to whole
The range of body scene is huge, will expend a large amount of search time, although may quickly be extended to G point using stochastical sampling class algorithm
Near, but due to the presence of slype, RRT algorithm will be difficult to enter target area (in room), for such barrier collection
Middle environment, existing algorithm are difficult to directly apply.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of barrier collection middle rings
Robot path generation method under border.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of barrier concentrates the robot path generation method under environment, and this method is searched using RRT algorithm first
Random tree is expanded in rope building, when generating invalid random point, judges the longest effective distance on the random point and target point line
Whether it is less than given threshold, scans for if so, being converted into A* algorithm to end, searched if it is not, then continuing RRT algorithm
Rope, and repeat above-mentioned judgement；Final path is searched for gained path and A* search gained path by RRT and is formed.
Further, this method specifically includes the following steps:
1) random tree is established；
2) random point is generated, judges that the random point whether in barrier, if so, giving up the random point, repeats to walk
It is rapid 2), if it is not, thening follow the steps 3)；
3) line for obtaining closest approach in random point and the random tree, judges whether the line passes through barrier, if so,
It thens follow the steps 4), if it is not, then retaining this line, and random point is added in random tree, executes step 5)；
4) judge whether the longest effective distance on random point and target point line is less than given threshold, if so, using
A* algorithm carries out local search to target point, obtains first path, step 6) is executed, if it is not, then return step 2)；
5) judge whether random point is target point, if so, thening follow the steps 6), if it is not, then return step 2)；
6) random tree is optimized and obtains the second path, if it exists first path, then by the first path and second
Path connects to form final path, if it does not exist first path, then directly using the second path as final path.
Further, in the step 4), the longest effective distance on random point and target point line is in the following manner
It obtains:
An alternative point on line is taken in step 3), the alternative point is farthest from the closest approach and is formed between closest approach new
Line is without barrier, using the alternative point and target point line distance as the longest effective distance.
Further, in the step 4), when carrying out local search using A* algorithm, using the alternative point as starting point, and
Random tree is added in the alternative point.
Compared with prior art, method proposed by the present invention is then that will judge whether to reach to that can not connect random point and be used as
The foundation of small-scale complex region, if threshold decision result thinks to be substantial access to target area, convert A* algorithm into
Row search, is rapidly achieved target point, finally extracts and integrate the path of two steps, have with following the utility model has the advantages that
1) for the path planning problem concentrated barrier under environment, the present invention first extensive simply under environment into
Row fast search, and judge whether to reach small-scale complex environment with a threshold condition, A* is used in small-scale complex environment
Algorithm is quickly searched for, and preferably track is obtained, and effectively improves trajectory planning efficiency.
2) present invention be for that can not connect random point, as the foundation for judging whether to reach small-scale complex region,
If threshold decision result thinks to be substantial access to target area, A* algorithm of converting is scanned for, and is rapidly achieved target point,
The path for finally extracting and integrating two steps, improves efficiency.
3) this method takes full advantage of rapidity and A* algorithm of the RRT algorithm in large-scale area to slype
Adaptability, and RRT algorithm is compensated for for the inadaptable of the environment such as slype, while also avoiding A* algorithm in extensive field
Search under scape, shortens the time.
Detailed description of the invention
Fig. 1 is that a kind of barrier concentrates environment schematic；
Fig. 2 is the method for the present invention flow diagram；
Fig. 3 is that random point of the present invention chooses schematic diagram；
Fig. 4 is that random tree of the present invention generates schematic diagram；
Fig. 5 is that close-proximity target zone of the present invention judges schematic diagram；
Fig. 6 is the search schematic diagram of close-proximity target zone of the present invention；
Fig. 7 is the optimization in path of the present invention and integrates schematic diagram.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
The present invention provides a kind of barrier and concentrates robot path generation method under environment, and this method uses RRT first
Algorithm scans for building and expands random tree, when generating invalid random point, judges on the random point and target point line most
Whether long effective distance is less than given threshold, scans for if so, being converted into A* algorithm to end, if it is not, then continuing
RRT algorithm search, and repeat above-mentioned judgement；Final path is searched for gained path and A* search gained path by RRT and is formed.Such as figure
Shown in 2, this method specifically includes the following steps:
In step s101, selection barrier concentrate environment in generate a random point, judge the random point whether
In barrier, if so, giving up the random point, step S101 is repeated, if it is not, thening follow the steps S102.
Step S102, obtains the line of closest approach in random point and random tree, closest approach be in random tree with random point away from
From nearest point, judge whether the line passes through barrier, if so, S104 is thened follow the steps, if it is not, then follow the steps S103.
Step S103 retains this line, and random point is added in random tree, using the closest approach as father node, judgement
Whether the random point is target point, if so, S107 is executed, if it is not, then return step S101.
Step S104 takes an alternative point on line, which forms farthest and between closest approach from the closest approach
New line executes step S105 without barrier.
Step S105, judges whether the line distance of the alternative point and target point is less than given threshold, if so, executing
Step S106, if it is not, then return step S101.
Step S106 carries out local search to target point using A* algorithm using the alternative point as starting point, obtains the first via
Diameter executes step S107.
Step S107 optimizes random tree and obtains the second path, if it exists first path, then by the first path
It connects to form final path with the second path, if it does not exist first path, then directly using the second path as final path.To with
Machine tree optimizes specifically: available path is extracted on random tree, using shortest path as the second path.
As Fig. 3-Fig. 7 show the robot path using the above method, from robot initial position S to target position G
Generating process.
As shown in figure 3, generating a random point, such as R1, R2 give up if random point falls in barrier (such as R2 point)
This point.
As shown in figure 4, the point (being at this time S point) on the random tree nearest apart from effective random point (by taking R1 as an example) is found,
If without barrier, which is added in random tree for the two line, and using its closest approach (S point) as
Its father node, if line enters in next step by barrier (such as random point R3 and the upper closest approach R1 of tree).
As shown in figure 5, path generating method of the present invention is not given up directly when random point is connected through barrier
It abandons, but has done extra process.Take on random point R3 and closest approach R1 line apart from closest approach R1 it is farthest and not in barrier
On alternative point T3, if it is at a distance from target point | GT3 | greater than the threshold value being previously set, then it is assumed that the random point is not in mesh
Areas adjacent is marked, is directly cast out, and step before repetition；If its distance is less than threshold value, such as | TmG |, wherein Rm is random point,
Nm is corresponding closest approach, and Tm is alternative point, then random tree is added in alternative point Tm using closest approach Nm as father node, and think
Through reaching close-proximity target zone.
As shown in fig. 6, being with alternative point Tm if thinking to have arrived at close-proximity target zone in step before
Point, target point G are terminal, are scanned on map using A* algorithm, seek first path, if no longer close-proximity target zone,
Step before then repeating.
As shown in fig. 7, extracting available path on random tree and carrying out certain optimization processing, with the second tunnel of shortest path
Diameter, as shown in the A in figure, obtained line segment is connect with first path can be obtained final path.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (4)
1. a kind of barrier concentrates the robot path generation method under environment, which is characterized in that this method is calculated using RRT first
Method scans for building and expands random tree, when generating invalid random point, judges the longest on the random point and target point line
Whether effective distance is less than given threshold, scans for if so, being converted into A* algorithm to end, if it is not, then continuing RRT
Algorithm search, and repeat above-mentioned judgement；Final path is searched for gained path and A* search gained path by RRT and is formed.
2. barrier according to claim 1 concentrates the robot path generation method under environment, which is characterized in that the party
Method specifically includes the following steps:
1) random tree is established；
2) random point is generated, judges that the random point whether in barrier, if so, giving up the random point, repeats step 2),
If it is not, thening follow the steps 3)；
3) line for obtaining closest approach in random point and the random tree, judges whether the line passes through barrier, if so, holding
Row step 4) if it is not, then retaining this line, and random point is added in random tree, executes step 5)；
4) judge whether the longest effective distance on random point and target point line is less than given threshold, if so, calculating using A*
Method carries out local search to target point, obtains first path, step 6) is executed, if it is not, then return step 2)；
5) judge whether random point is target point, if so, thening follow the steps 6), if it is not, then return step 2)；
6) random tree is optimized and obtains the second path, if it exists first path, then by the first path and the second path
Connection forms final path, if it does not exist first path, then directly using the second path as final path.
3. barrier according to claim 2 concentrates the robot path generation method under environment, which is characterized in that described
In step 4), random point obtains in the following manner with the longest effective distance on target point line:
Take in step 3) alternative point on line, the alternative point is farthest from the closest approach and the new line that is formed between closest approach
Without barrier, using the alternative point and target point line distance as the longest effective distance.
4. barrier according to claim 3 concentrates the robot path generation method under environment, which is characterized in that described
In step 4), when carrying out local search using A* algorithm, using the alternative point as starting point, and random tree is added in the alternative point.
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| CN110426053A (en) * | 2019-07-12 | 2019-11-08 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method and mobile robot |
| CN110794832A (en) * | 2019-10-21 | 2020-02-14 | 同济大学 | Mobile robot path planning method based on reinforcement learning |
| CN111707264A (en) * | 2020-05-30 | 2020-09-25 | 同济大学 | Improved and extended RRT path planning method, system and device |
| CN111722924A (en) * | 2020-05-30 | 2020-09-29 | 同济大学 | Parallel path searching method, system and device under narrow channel environment |
| CN112711256A (en) * | 2020-12-23 | 2021-04-27 | 杭州电子科技大学 | Automatic target search observation point generation method for mobile robot |
| CN113031599A (en) * | 2021-03-02 | 2021-06-25 | 珠海市一微半导体有限公司 | Robot rapid seat finding method with dynamically changed reference point, chip and robot |
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| CN110275528A (en) * | 2019-06-04 | 2019-09-24 | 合肥工业大学 | For the method for optimizing route of RRT algorithm improvement |
| CN110275528B (en) * | 2019-06-04 | 2022-08-16 | 合肥工业大学 | Improved path optimization method for RRT algorithm |
| CN110345959A (en) * | 2019-06-10 | 2019-10-18 | 同济人工智能研究院(苏州)有限公司 | Paths planning method of the one kind based on " door " point |
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| CN110426053A (en) * | 2019-07-12 | 2019-11-08 | 深圳市银星智能科技股份有限公司 | A kind of paths planning method and mobile robot |
| CN110794832B (en) * | 2019-10-21 | 2021-11-09 | 同济大学 | Mobile robot path planning method based on reinforcement learning |
| CN110794832A (en) * | 2019-10-21 | 2020-02-14 | 同济大学 | Mobile robot path planning method based on reinforcement learning |
| CN111722924A (en) * | 2020-05-30 | 2020-09-29 | 同济大学 | Parallel path searching method, system and device under narrow channel environment |
| CN111707264A (en) * | 2020-05-30 | 2020-09-25 | 同济大学 | Improved and extended RRT path planning method, system and device |
| CN111722924B (en) * | 2020-05-30 | 2022-09-16 | 同济大学 | Parallel path searching method, system and device under narrow channel environment |
| CN112711256A (en) * | 2020-12-23 | 2021-04-27 | 杭州电子科技大学 | Automatic target search observation point generation method for mobile robot |
| CN113031599A (en) * | 2021-03-02 | 2021-06-25 | 珠海市一微半导体有限公司 | Robot rapid seat finding method with dynamically changed reference point, chip and robot |
| CN113219975A (en) * | 2021-05-08 | 2021-08-06 | 珠海市一微半导体有限公司 | Route optimization method, route planning method, chip and robot |
| CN113219975B (en) * | 2021-05-08 | 2024-04-05 | 珠海一微半导体股份有限公司 | Route optimization method, route planning method, chip and robot |
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