CN108762270B - Improved path planning algorithm for variable probability bidirectional fast search random tree - Google Patents
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Abstract
The invention relates to a variable probability bidirectional fast search random tree improved path planning algorithm. Secondly, a variable probability target selection strategy based on a node environment is adopted to accelerate the convergence speed, and finally, the generated path is subjected to curve removing and straightening, namely whether an obstacle exists between a connecting line with a first node as a starting point and a subsequent node is judged, redundant nodes are deleted, the path is optimized, and the number of turns and the total path length in the driving process of the trolley are reduced. The variable probability optimization algorithm is realized on the original bidirectional RRT algorithm, the searching speed is improved by using a target pointing mode, and the calculated amount is reduced; meanwhile, the path length and the number of nodes are reduced, and the feasibility of communication is ensured.
Description
Technical Field
The invention relates to an intelligent vehicle path planning technology, in particular to a variable-probability bidirectional fast search random tree improved path planning algorithm.
Background
The intelligent vehicle path planning means that the intelligent vehicle finds a continuous collision-free path from an initial pose point to a target pose point in the pose space, and the path also meets the environmental constraints and the motion characteristics of the intelligent vehicle.
Aiming at the situation, an RRT (fast search random tree) algorithm is provided, the core of the algorithm is a random search mode, the path search can be realized in a high-dimensional non-convex space, due to the adoption of a planning mode of random sampling, the preprocessing is not needed, the search speed is high, the advantage of the speed in the high-dimensional space is particularly obvious, and the RRT algorithm is widely applied and researched in the field of robot motion planning in recent years. But it also has some drawbacks in itself: (1) sampling is random sampling, and the convergence speed of the algorithm is low due to a large number of invalid samples; (2) metric functions (nearest neighbor algorithms) may have difficulty obtaining a valid solution in the presence of complex constraints; and (3) the randomness of the algorithm can cause the generated path to be unsmooth, and a large number of turning and invalid curve paths can occur when the path is directly used. For the above-mentioned deficiency of the basic RRT algorithm, scholars at home and abroad have also continuously improved the algorithm to adapt to different application environments. Kuffner and LaValle propose Bi-RRT (bidirectional RRT), two trees are generated in parallel from an initial state and a target state, and convergence can be realized quickly; then, the RRT-connect algorithm is provided, the algorithm adopts directional expansion during node expansion, and the probability of directional expansion needs to be selected according to the situation in practical application. For the problem of unsmooth path caused by the randomness of the RRT algorithm, Fraichard and Scheuer propose that a clothoid is used for smoothing, but the computation amount of solving the clothoid is large; lau et al used 5 bezier curves, but did not consider the continuity of the path curvature and the characteristics of the robot itself, and the amount of calculation was also large.
Disclosure of Invention
The invention provides a variable-probability bidirectional rapid random tree search improved path planning algorithm aiming at solving the problems of robot motion planning by a rapid random tree search algorithm, and ensures the trafficability at the search speed.
The technical scheme of the invention is as follows: a variable probability bidirectional fast search random tree improved path planning algorithm specifically comprises the following steps:
1) and (3) preprocessing a state space according to the setting of the volume of the running vehicle: namely, the barrier area is enlarged, and a safety distance is reserved;
2) and (3) carrying out path generation on the variable probability bidirectional RRT algorithm: generating a path L by a bidirectional RRT algorithm, adding a variable probability algorithm in the generation process of an iterative growth path, increasing constraint conditions of a surrounding environment, after generating a node, detecting whether the node is used as the center of a driving vehicle and whether shielding exists in an area circle required by the driving vehicle, namely whether the area circle is intersected with an obstacle region, if so, randomly obtaining a probability value P by uniform probability distribution according to the intersection, comparing the probability value P with a set target deviation probability value Pset, if Pset is less than 10 percent and P is greater than Pset, abandoning the node, and generating the node by adopting a random function again; when P is smaller than Pset, the node is approved and put into the corresponding set;
3) optimizing a path:
3.1) initialization: the generated path L node path is given Q, Q ═ Q0，q1，q2…qn) And q is0Giving q atemp，i＝1；
3.2) sequentially judging q by a checkPath () functiontempWith its succeeding node qiWhether barriers exist among the E-Q connecting line segments, and if i is less than n, judging to execute 3.3); when i > ═ n, go to 3.4);
3.3) if checkPath () returns a value of 1, i.e. qtempWith its succeeding node qiE, leaving no obstacles among the things of Q, if i is i +1, and returning to 3.2 for continuous judgment; if the checkPath () returns a value of 0, i.e., qtempWith its succeeding node qiE, if there is an obstacle between Q, then Q istempAnd q isi-1Deleting the nodes in between, and qi-1Giving q atempAnd returning to 3.2 for continuous judgment;
3.4) connecting the remained nodes which are not deleted through a link () function to generate an optimized path.
The invention has the beneficial effects that: the invention discloses a variable probability bidirectional fast search random tree improved path planning algorithm, which firstly utilizes state space preprocessing to avoid collision of vehicles on a path; then, a variable probability optimization algorithm is realized on the original bidirectional RRT algorithm, the searching speed is improved by using a target pointing mode, and the calculated amount is reduced; and finally, reducing the path length and the number of nodes through post-processing. Simulation experiments show that the algorithm can improve the searching speed, reduce the path length and the number of nodes and ensure the feasibility.
Drawings
FIG. 1 is a diagram of the original state space of the present invention;
FIG. 2 is a state space diagram after state space edge processing in accordance with the present invention;
FIG. 3 is a graph of the original RRT bi-directional algorithm simulation results as follows;
FIG. 4 is a simulation diagram of the path planning result improved by adding a variable probability algorithm on Matlab according to the present invention;
FIG. 5 is a graph of the path results after processing by the method of the present invention.
Detailed Description
In actual use, the distance between the state space edge and the vehicle center (X, Y) needs to be kept in practice due to the volume of the vehicle, so that when the state space diagram is introduced, the state space preprocessing is required according to the setting of the vehicle volume, the state space edge is protected in an expansion mode, and the node is prevented from being too close to the state space edge to cause collision.
A variable probability target selection strategy based on a node environment is adopted, namely after a node is generated, a function detect (q) is adopted to detect whether shielding exists in a peripheral single distance interval (namely, a point which is not contained in an available state space exists), and if the shielding does not exist, a terminal point is selected as a target point to be expanded once, so that a tree node is obtained; otherwise, a set target deviation probability value Pset (generally set to be less than 10%) is adopted, when the probability P of random generation is greater than Pset, random function generation is adopted, and when the probability P of random generation is less than Pset, the terminal point is also adopted as the target point.
In view of the sampling randomness of the RRT algorithm, the generated path is usually unnatural and tortuous despite the adoption of the target-oriented selection strategy. In actual use, the break point can bring the intelligent vehicle to repeatedly operate and turn, travel unnecessary paths, and accelerate vehicle wear. Therefore, a path post-processing flow based on the shortest path is provided based on practical application, the generated path is subjected to bending removal and straightening, unnecessary nodes are deleted, and the path length is shortened, namely, firstly, path nodes obtained in the previous path generation need to be introduced, then, whether a connecting line with a subsequent node forms intersection with an obstacle space is checked by taking the first node as a starting point, if not, the node is deleted, otherwise, the connecting condition of the subsequent node is continuously checked by taking a new node as the starting point.
The invention relates to a variable probability bidirectional fast search random tree improved path planning algorithm, which comprises the following specific steps:
the first step is as follows: the test platform is an Intel i5, 3.2GHz processor and 12GB memory, the test scene state space size is set to 500 × 500, the vehicle width is 10, the length is 16, the starting point and the ending point are set to [1, 1] and [499, 499], and the barrier area is black, as shown in fig. 1. Firstly, state space preprocessing is carried out according to the setting of the vehicle volume, and the edges of the state space are expanded and protected, namely, the distances from the vehicle center point (x, y) to the left and right sides of the vehicle and the distances from the vehicle head and the vehicle tail are respectively x 'to 5 and y' to 8, and a safe distance 1 is reserved during processing, namely, the distance from the vehicle center point (x, y) to an obstacle is at least x 'to 6 and y' to 7, so that the situation that the node is too close to the edges of the state space and the collision is caused is prevented. Therefore, I increase the length and width of the barrier by 1 respectively during simulation, and reserve a safe distance, as shown in FIG. 2. If the distance between the obstacles is small and the vehicles cannot pass through the obstacle after expansion, the obstacle is considered to be unable to pass through the obstacle. (Note that in FIG. 3 the barrier has been expanded and the expansion is not shown in black, but it can be seen that the line is already a distance from the barrier.)
The second step is that: the method is used for simulating the variable probability bidirectional RRT algorithm on Matlab, and the basic idea is as follows: with the starting point qinitAnd target point qgoalFor the initial points of the two search trees T1 and T2, the search trees are constructed separately, first qinitPut into set E1(E1Is q successfully generated for each searchnewSet of q) ofgoalPut into set E2(E2Is q 'successfully generated per search'newSet of) with one of the trees T1, first search, qinit＝qneaWith qnearTaking the step length epsilon as a distance as a starting point, and constructing a state node q at a barrier-free position in a random sampling moderandSuch as qnearNode q in this staterandIf there is no obstacle between the connecting lines, the node q of the state is connectedrandGiving q anew1，qnew1Placing in E1Find the first leaf node q on T1new1At this time, set E1In which is qinitAnd q isnew1Two points are included; then a second search is performed, qnew1＝qnearWith qnearTaking the step size epsilon as a distance as a starting point, and constructing a state node q again at a barrier-free position in a random sampling moderandSuch as qnearNode q in this staterandIf there is no obstacle between the connecting lines, the node q of the state is connectedrandGiving q anew2，qnew2Placing in E1Find on T1Second leaf node q ofnew2(ii) a In turn, each node on T1 is generated. In the same way as qgoalVarious nodes on T2 are generated for the initial point.
Search E1And E2Whether there is a same point in, i.e. qnewi＝q’newjOr at E1And E2In each case one point qnewiAnd q'newjIf the distance between the two points is less than or equal to the step length epsilon, setting the two points as qnewiAnd q'newjIs q1endAnd q is2end. If the condition is satisfied, the search is considered to be completed. When the search is completed, at E1And E2In the formula q1endAnd q is2endBacktracking to q as a starting pointinitAnd q isgoalAt E1To obtain a path l1, at E2A path L2 is obtained, and finally, all the nodes in L1 and L2 are connected to obtain a final path L, which is the path generating method of the bidirectional RRT algorithm.
In order to ensure the path trafficability, a variable probability algorithm is added, in the iterative growth path generation process, constraint conditions of the surrounding environment are added, after a node is generated, a function detect (q) is adopted to detect whether the node is used as the center of a driving vehicle and whether shielding exists in an area circle required by the driving vehicle, namely whether the area circle intersects with an obstacle region, if so, a set target deviation probability value Pset (generally set to be less than 10%) is adopted to be compared with a random number P generated by the uniform probability distribution, when the random number P generated by the uniform probability distribution is greater than Pset, the node is abandoned, the random function is adopted again to generate the node, and when the P is less than Pset, the node is approved and put into a corresponding set. The area circle required by the running vehicle is internally shielded, the random number P can be automatically generated from uniform probability distribution according to the position distribution and the shielding size condition of the shielding, and the target deviation refers to the random sampling point qrandTime exceeds q as much as possiblegoalThat direction takes points rather than growing child leaf nodes in the other direction, so that a faster path is generated, which is set when the path is generated.
The original RRT bi-directional algorithm simulation results are shown in fig. 3. The simulation of the path planning result improved by adding the variable probability algorithm on Matlab is shown in FIG. 4.
The third step: importing path nodes obtained in the previous path generation and then taking a starting point qinitSequentially connecting with the following 1 st node, 2 nd node and 3 rd node as a starting point to obtain a line segment L01、L02、L03And checking L01、L02、L03Whether or not to intersect with an obstacle space, e.g. L03Has intersection, L01、L02If no intersection exists, deleting the 1 st node and reserving the 2 nd node; then, with the 2 nd node as a starting point, sequentially connecting each node behind the 2 nd node to obtain a line segment L23、L24、L25、L26Checking L23、L24、L25、L26Whether or not to intersect with an obstacle space, e.g. L26Has intersection, L23、L24、L25And if no intersection exists, deleting the 3 rd and 4 th nodes, reserving the 5 th node, and so on to generate an optimized path. The method comprises the following specific steps:
3.1 initialization: assigning the generated path node path to Q (Q)0，q1，q2…qn) And q0 is given to qtemp，i＝1；
3.2 judging q sequentially by the checkPath () functiontempWith its succeeding node qiWhether obstacles exist among the connection line segments belonging to the element Q, and if i is less than n, judging to execute 3.3; when i > ═ n, 3.4 is entered.
3.3 if checkPath () returns a value of 1, i.e., qtempWith its succeeding node qiE, leaving no obstacles among the things of Q, if i is i +1, and returning to 3.2 for continuous judgment; if the checkPath () returns a value of 0, i.e., qtempWith its succeeding node qiE, if there is an obstacle between Q, then Q istempAnd q isi-1Deleting the nodes in between, and qi-1Giving q atempAnd returns to 3.2 to continue the judgment.
3.4 connecting the remained nodes which are not deleted through a link () function to generate an optimized path.
The processed path result is shown in fig. 5 below, where the dotted line is the search result in the variable probability algorithm, and the solid line is the path in the post-processing.
Claims (1)
1. A variable probability bidirectional fast search random tree improved path planning algorithm is characterized by specifically comprising the following steps:
1) and (3) preprocessing a state space according to the setting of the volume of the running vehicle: namely, the barrier area is enlarged, and a safety distance is reserved;
2) and (3) carrying out path generation on the variable probability bidirectional RRT algorithm: generating a path L by a bidirectional RRT algorithm, adding a variable probability algorithm in the generation process of an iterative growth path, increasing constraint conditions of a surrounding environment, after generating a node, detecting whether the node is used as the center of a driving vehicle and whether shielding exists in an area circle required by the driving vehicle, namely whether the area circle is intersected with an obstacle region, if so, randomly obtaining a probability value P by uniform probability distribution according to the intersection, comparing the probability value P with a set target deviation probability value Pset, if Pset is less than 10 percent and P is greater than Pset, abandoning the node, and generating the node by adopting a random function again; when P is smaller than Pset, the node is approved and put into the corresponding set;
3) optimizing a path:
3.1) initialization: the nodes on the generated path L are given Q (Q)0，q1，q2...qn) N is a non-negative integer, and q is0Giving q atemp，i＝1；
3.2) sequentially judging q by a checkPath () functiontempWith its succeeding node qiWhether barriers exist among the E-Q connecting line segments, and if i is less than n, judging to execute 3.3); when i > ═ n, go to 3.4);
3.3) if checkPath () returns a value of 1, i.e. qtempWith its succeeding node qiE, leaving no obstacles among the things of Q, if i is i +1, and returning to 3.2 for continuous judgment; if the checkPath () returns a value of 0, i.e., qtempWith its succeeding node qiE, if there is an obstacle between Q, then Q istempAnd q isi-1Deleting the nodes in between, and qi-1Giving q atempAnd returns to 3.2, continuing to judge;
3.4) connecting the remained nodes which are not deleted through a link () function to generate an optimized path.
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| CN110118559B (en) * | 2019-04-30 | 2021-05-25 | 厦门大学 | Navigation robot dynamic obstacle avoidance method and device based on variable probability strategy |
| CN110275528B (en) * | 2019-06-04 | 2022-08-16 | 合肥工业大学 | Improved path optimization method for RRT algorithm |
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| CN112987799B (en) * | 2021-04-16 | 2022-04-05 | 电子科技大学 | Unmanned aerial vehicle path planning method based on improved RRT algorithm |
| CN113419542B (en) * | 2021-07-16 | 2022-11-11 | 浙江大华技术股份有限公司 | Path planning method, device and storage medium |
| CN113467476B (en) * | 2021-08-02 | 2023-04-28 | 福州大学 | Collision-free detection rapid random tree global path planning method considering corner constraint |
| CN113485375B (en) * | 2021-08-13 | 2023-03-24 | 苏州大学 | Indoor environment robot exploration method based on heuristic bias sampling |
| CN114161416B (en) * | 2021-12-06 | 2023-04-28 | 贵州大学 | Robot path planning method based on potential function |
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