CN116300932A - Robot motion path planning method and system based on improved RRT-Connect algorithm - Google Patents
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Abstract
The invention provides a robot motion path planning method based on an improved RRT-Connect algorithm, which comprises the following steps: establishing a free space model of the robot movement, and determining a starting point and a target point of the robot movement in the free space; simultaneously, respectively expanding RRT random trees by taking a starting point and a target point as starting points, searching for new nodes based on a dynamic step strategy and a target deflection strategy in the searching process, adding one new node to the corresponding random tree every time the new node is searched for until the two random trees meet in a free space, selecting an optimal path generated after the two random trees are communicated, and storing the optimal path; and smoothing the generated optimal path by using a greedy algorithm. The invention introduces a dynamic step strategy to control the step in the random tree expansion process, thereby effectively improving the path searching efficiency; meanwhile, a target deflection strategy is introduced to restrict the direction in the random sampling process, so that each sampling can be more close to a target point, and the sampling efficiency of an algorithm is improved.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a robot motion path planning method and system based on an improved RRT-Connect algorithm.
Background
The intelligent robot can not encounter various barriers in the moving process, and the flexible and real-time avoidance of the barriers is a key index for measuring the performance of the intelligent robot. The path planning algorithm is to find a collision-free path between a starting point and a target point within a limited time, which can ensure that the robot cannot collide in the moving process, and is widely applied to the fields of automatic driving, autonomous detection and the like at present.
The RRT algorithm is used as a path planning algorithm widely used at present, sampling points in a state space can be detected, information of the sampling points and obstacles which are possibly collided can be obtained, a space modeling process can be omitted, and the problem of path planning in a high-dimensional space can be directly solved.
Although the RRT algorithm has obvious advantages, the algorithm has obvious defects, and the strong randomness node expansion in the algorithm determines that a large number of iteration times are needed for searching a feasible track by using the algorithm, so that a large amount of time is consumed, and the selected path cannot be guaranteed to be the optimal path. At the same time, the random tree has a limitation in approaching the threat area in view of the fixed expansion step size, and it takes a lot of time to bypass the threat area.
Disclosure of Invention
The invention provides a robot motion path planning method and system based on an improved RRT-Connect algorithm, which solve the problems that the path planning algorithm in the prior art is long in time consumption, the selected path cannot be guaranteed to be an optimal path and the like.
The technical scheme of the invention is realized as follows:
according to one aspect of the invention, there is provided a robot motion path planning method based on an improved RRT-Connect algorithm, comprising the steps of:
establishing a free space model of the robot motion, and determining a starting point Q of the robot motion in the free space init And target point Q goal ；
At the same time at the starting point Q init And target point Q goal Respectively expanding RRT random tree T as starting point 1 And T 2 In the searching process, a new node Q is searched based on a dynamic step strategy and a target bias strategy new Adding a new node into a corresponding random tree every time the new node is searched until the two random trees meet in a free space, selecting an optimal path generated after the two random trees are communicated, and storing the optimal path;
and smoothing the generated optimal path by using a greedy algorithm.
As a preferred scheme of the invention, the method for searching the new node based on the dynamic step size strategy comprises the following steps:
at an initial step length ρ 0 Expanding new nodes of the random tree;
when expanding to a new node, if no collision between the random tree and the obstacle is detected and two random trees are not connected, setting the expanding step length of the random tree as s i ＝s i-1 +ρ', continue expanding new nodes; wherein s is i Representing the current expansion step size, s, of a random tree i-1 Representing the last expansion step length of the random tree, wherein ρ' represents a fixed expansion step length;
if collision between the random tree and the obstacle is detected, discarding the current new node, and setting the expansion step length of the random tree as s i ＝s i-1 - ρ' continuing to expand the new node on the basis of the last expansion;
if the connection of the two random trees is detected, the expansion of the random trees is ended.
As a preferred scheme of the invention, the method for searching the new node based on the target bias strategy comprises the following steps:
in the random sampling process of the random tree, randomly generating a probability value p according to uniform probability;
if the probability value p is smaller than the set threshold p bias Sampling point Q rand Selected as the target point Q goal ；
If the probability value p is greater than the set threshold p bias The sampling points are randomly expanded in the free space, and the generation direction of the new node is constrained, and the calculation process of the new node is as follows:
Q new ＝Q near +ρ·cosθ
wherein Q is near Representing the node closest to the random sampling point on the random tree, wherein ρ is the current expansion step length of the random tree.
As a preferred solution of the present invention, the method for smoothing the optimal path includes:
the node set corresponding to the optimal path obtained after the two random trees are connected is as follows:
path(Q 0 ，Q 1 ，·…，Q n )
wherein Q is 0 Representing the starting point Q init ，Q n Representing the target point Q goal ；
Let Q temp ＝Q 0 By using Q together temp Respectively with Q 1 ，Q 2 ，…，Q n Connected when Q temp And Q is equal to i When the connection is bumped against an obstacle, Q i-1 A path buffer array T' is stored, wherein i=1, 2, …, n;
let Q temp ＝Q i-1 By using Q together temp Respectively with Q i ，Q i+1 ，…，Q n Connected when Q temp And Q is equal to m When the connection is bumped against an obstacle, Q m-1 Storing into a path cache array T', wherein m=i, i+1, …, n;
repeating the steps until Q temp And Q is equal to n No connection encounters an obstacle;
sequentially connected to Q 0 Adjacent node in array T' and Q n And generating a path after pruning the random tree.
According to another aspect of the present invention, there is provided a robot motion path planning system comprising:
model building unit: the method comprises the steps of establishing a free space model and determining a starting point and a target point of the robot moving in the free space;
an optimal path determination unit: on the basis of an RRT-Connect algorithm, introducing a dynamic step strategy and a target deflection strategy to expand a random tree, and determining an optimal path from a starting point to a target point;
and a path optimization module: and carrying out smoothing treatment on the optimal path by using a greedy algorithm to obtain an optimized planning path.
According to a further aspect of the present invention there is provided a storage medium storing a computer program which when executed by a processor implements the steps of the path planning method described above.
Advantageous effects
Compared with the prior art, the invention has the beneficial effects that: according to the invention, on the basis of the RRT-Connect algorithm, a dynamic step strategy is introduced to control the step in the random tree expansion process, so that the path searching efficiency is effectively improved; meanwhile, a target deflection strategy is introduced to restrict the direction in the random sampling process, so that each sampling can be more close to a target point, the calculated amount is reduced, and the sampling efficiency of an algorithm is improved; and finally, carrying out smoothing treatment on the paths formed by searching through a greedy algorithm, obviously improving the convergence rate of the improved algorithm, and simultaneously ensuring the quality of the paths planned by the mode.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a robot motion path planning method based on an improved RRT-Connect algorithm;
FIG. 2 is a schematic diagram of simulation results of three path planning algorithms respectively adopted in a simple obstacle environment according to an embodiment of the present invention;
fig. 3 is a schematic diagram of simulation effects of three path planning algorithms respectively adopted in a complex obstacle environment according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present embodiment provides a robot motion path planning method based on an improved RRT-Connect algorithm, including the following steps:
establishing a free space model of the robot motion, and determining a starting point Q of the robot motion in the free space init And target point Q goal ；
At the same time at the starting point Q init And target point Q goal Respectively expanding RRT random tree T as starting point 1 And T 2 ；
In the searching process, since the step length in the RRT-Connect algorithm is fixed, the expansion speed of the random tree is limited by using a given step length in the process of exploring the unobstructed space, so that a dynamic step length strategy is introduced to search for new nodes in the embodiment, and the specific method is as follows:
first with an initial step ρ 0 Expanding new nodes of the random tree;
when expanding to a new node, if no collision between the random tree and the obstacle is detected and two random trees are not connected, setting the expanding step length of the random tree as s i ＝s i-1 +ρ', continue expanding newA node; wherein s is i Representing the current expansion step size, s, of a random tree i-1 Representing the last expansion step length of the random tree, wherein ρ' represents a fixed expansion step length;
if collision between the random tree and the obstacle is detected, discarding the current new node, and setting the expansion step length of the random tree as s i ＝s i-1 - ρ' continuing to expand the new node on the basis of the last expansion;
if the connection of the two random trees is detected, the expansion of the random trees is ended.
The RRT-Connect algorithm can obviously improve the searching speed through the expansion of the bidirectional random tree; however, considering that the problem of larger randomness of sampling points possibly exists in the process of expanding by using the algorithm, and meanwhile, the situation that sampling areas are too scattered is also considered, the searching result is easy to be low in efficiency, and the situation that corresponding paths are relatively coarse is also likely to occur;
based on the above problems, the embodiment introduces a target bias strategy to search for new nodes, and the specific method is as follows:
in the random sampling process of the random tree, randomly generating a probability value p, p≡rand (0, 1) according to uniform probability;
if the probability value p is smaller than the set threshold p bias Sampling point Q rand Selected as the target point Q goal ；
If the probability value p is greater than the set threshold p bias And randomly expanding the sampling points in the free space and restraining the generation direction of the new node.
The present embodiment is implemented by introducing a random point Q obtained from random sampling rand To target point Q goal To make the random node Q rand Is generated toward the target point Q goal Is a directional expansion of (a). Introducing direction vectors, i.e. the selection of the new node position during expansion, not only in relation to random points, but also to target points, in the original direction
Add direction at the top->
Is a component of (a). The position of the new node is affected after the component in the direction of the target point is added, when the new node is not in Q near And Q is equal to rand But the growth direction is biased toward the target point Q goal The method comprises the steps of carrying out a first treatment on the surface of the The calculation process of the new node is as follows:
Q new ＝Q near +ρ·cosθ
wherein Q is near Representing the node closest to the random sampling point on the random tree, wherein ρ is the current expansion step length of the random tree; the sampling process is restrained to ensure that each sampling tends to the target point, so that the directionality of the sampling can be ensured while the random sampling point is prevented from searching reversely, and the efficiency of a feasible path is improved.
Adding a new node into a corresponding random tree every time the new node is searched until the two random trees meet in a free space, selecting an optimal path generated after the two random trees are communicated, and storing the optimal path;
and finally, carrying out smoothing treatment on the generated optimal path by using a greedy algorithm, wherein the specific method comprises the following steps of:
the node set corresponding to the optimal path obtained after the two random trees are connected is as follows:
path(Q 0 ，Q 1 ，…，Q n )
wherein Q is 0 Representing the starting point Q init ，Q n Representing the target point Q goal ；
Let Q temp ＝Q 0 By using Q together temp Respectively with Q 1 ，Q 2 ，…，Q n Connected when Q temp And Q is equal to i When the connection is bumped against an obstacle, Q i-1 A path buffer array T' is stored, wherein i=1, 2, …, n;
let Q temp ＝Q i-1 By using Q together temp Respectively with Q i ，Q i+1 ，…，Q n Connected when Q temp And Q is equal to m When the connection is bumped against an obstacle, Q m-1 Storing into a path cache array T', wherein m=i, i+1, …, n;
repeating the steps until Q temp And Q is equal to n No connection encounters an obstacle;
sequentially connected to Q 0 Adjacent node in array T' and Q n And generating a path after pruning the random tree.
Corresponding to the path planning method, the embodiment also provides a robot motion path planning system, which comprises:
model building unit: the method comprises the steps of establishing a free space model and determining a starting point and a target point of the robot moving in the free space;
an optimal path determination unit: on the basis of an RRT-Connect algorithm, introducing a dynamic step strategy and a target deflection strategy to expand a random tree, and determining an optimal path from a starting point to a target point;
and a path optimization module: and carrying out smoothing treatment on the optimal path by using a greedy algorithm to obtain an optimized planning path.
The present embodiment also provides a storage medium storing a computer program which, when executed by a processor, implements the steps of the path planning method described above.
As shown in fig. 2, a schematic diagram of simulation effects of three path planning algorithms respectively adopted in a simple obstacle environment is shown, fig. 2 (a) shows an effect diagram of path planning adopting a conventional RRT algorithm, fig. 2 (b) shows an effect diagram of path planning adopting a conventional RRT-Connect algorithm, fig. 2 (c) shows an effect diagram of path planning adopting the improved RRT-Connect algorithm of the present embodiment, and table 1 below shows a simulation data table of three path planning algorithms in a simple obstacle environment:
table 1 simulation data sheet of three path planning algorithms in simple obstacle environment
As can be seen from fig. 2 and table 1 above, the improved RRT-Connect algorithm of the present embodiment saves 91% in time relative to the RRT algorithm and 40% in time relative to the RRT-Connect algorithm; there is also a significant saving in path length over the RRT algorithm and the RRT-Connect algorithm.
As shown in fig. 3, a schematic diagram of simulation effects of three path planning algorithms in a complex obstacle environment is shown, in fig. 3 (a) shows an effect diagram of path planning using a conventional RRT algorithm, in fig. 3 (b) shows an effect diagram of path planning using a conventional RRT-Connect algorithm, in fig. 3 (c) shows an effect diagram of path planning using the modified RRT-Connect algorithm of the present embodiment, and table 1 below shows a simulation data table of three path planning algorithms in a complex obstacle environment:
table 2 simulation data table of three path planning algorithms in complex obstacle environment
| Algorithm | Time/s | Path length |
| RRT | 21.7249 | 9.728123e+02 |
| RRT-Connect | 5.1772 | 9.425457e+02 |
| Improved RRT-Connnect | 1.7475 | 9.217032e+02 |
As can be seen from fig. 3 and table 2 above, the improved RRT-Connect algorithm of the present embodiment saves 91% in time relative to the RRT algorithm and 66% in time relative to the RRT-Connect algorithm; there is also a significant saving in path length over the RRT algorithm and the RRT-Connect algorithm. Compared with the original RRT algorithm and the RRT-Connect algorithm, the improved RRT-Connect algorithm reduces unnecessary expansion in the random tree expansion process, and greatly shortens the search time.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. The robot motion path planning method based on the improved RRT-Connect algorithm is characterized by comprising the following steps of:
establishing a free space model of the robot motion, and determining a starting point Q of the robot motion in the free space init And target point Q goal ；
At the same time at the starting point Q init And target point Q goal Is divided into starting pointsSpread RRT random tree T 1 And T 2 In the searching process, a new node Q is searched based on a dynamic step strategy and a target bias strategy new Adding a new node into a corresponding random tree every time the new node is searched until the two random trees meet in a free space, selecting an optimal path generated after the two random trees are communicated, and storing the optimal path;
and smoothing the generated optimal path by using a greedy algorithm.
2. The robot motion path planning method based on the improved RRT-Connect algorithm of claim 1, wherein the method for searching for new nodes based on the dynamic step size strategy is as follows:
at an initial step length ρ 0 Expanding new nodes of the random tree;
when expanding to a new node, if no collision between the random tree and the obstacle is detected and two random trees are not connected, setting the expanding step length of the random tree as s i ＝s i-1 +ρ', continue expanding new nodes; wherein s is i Representing the current expansion step size, s, of a random tree i-1 Representing the last expansion step length of the random tree, wherein ρ' represents a fixed expansion step length;
if collision between the random tree and the obstacle is detected, discarding the current new node, and setting the expansion step length of the random tree as s i ＝s i-1 - ρ' continuing to expand the new node on the basis of the last expansion;
if the connection of the two random trees is detected, the expansion of the random trees is ended.
3. The robot motion path planning method based on the modified RRT-Connect algorithm of claim 1, wherein the method for searching for new nodes based on the target bias strategy is as follows:
in the random sampling process of the random tree, randomly generating a probability value p according to uniform probability;
if the probability value p is smaller than the set threshold p bias Sampling point Q rand Selected as the target point Q goal ；
If the probability value p is greater than the set threshold p bias The sampling points are randomly expanded in the free space, and the generation direction of the new node is constrained, and the calculation process of the new node is as follows:
Q new ＝Q near +ρ·cosθ
wherein Q is near Representing the node closest to the random sampling point on the random tree, wherein ρ is the current expansion step length of the random tree.
4. The method for planning a motion path of a robot based on an improved RRT-Connect algorithm of claim 1, wherein the method for smoothing the optimal path is as follows:
the node set corresponding to the optimal path obtained after the two random trees are connected is as follows:
path(Q 0 ，Q 1 ，…，Q n )
wherein Q is 0 Representing the starting point Q init ，Q n Representing the target point Q goal ；
Let Q temp ＝Q 0 By using Q together temp Respectively with Q 1 ，Q 2 ，…，Q n Connected when Q temp And Q is equal to i When the connection is bumped against an obstacle, Q i-1 A path buffer array T' is stored, wherein i=1, 2, …, n;
let Q temp ＝Q i-1 By using Q together temp Respectively with Q i ，Q i+1 ，…，Q n Connected when Q temp And Q is equal to m When the connection is bumped against an obstacle, Q m-1 Storing into a path cache array T', wherein m=i, i+1, …, n;
repeating the steps until Q temp And Q is equal to n No connection encounters an obstacle;
sequentially connected to Q 0 Adjacent node in array T' and Q n And generating a path after pruning the random tree.
5. A robot motion path planning system based on the path planning method according to any one of claims 1 to 4, characterized by comprising:
model building unit: the method comprises the steps of establishing a free space model and determining a starting point and a target point of the robot moving in the free space;
an optimal path determination unit: on the basis of an RRT-Connect algorithm, introducing a dynamic step strategy and a target deflection strategy to expand a random tree, and determining an optimal path from a starting point to a target point;
and a path optimization module: and carrying out smoothing treatment on the optimal path by using a greedy algorithm to obtain an optimized planning path.
6. A storage medium storing a computer program which, when executed by a processor, implements the steps of the path planning method of any one of claims 1 to 4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117260735A (en) * | 2023-11-01 | 2023-12-22 | 广东工业大学 | Path planning method for robot deep frame grabbing |
| CN117387633A (en) * | 2023-12-13 | 2024-01-12 | 山东常林智能装备科技有限公司 | Path planning method for unmanned excavator, electronic equipment and medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117260735A (en) * | 2023-11-01 | 2023-12-22 | 广东工业大学 | Path planning method for robot deep frame grabbing |
| CN117387633A (en) * | 2023-12-13 | 2024-01-12 | 山东常林智能装备科技有限公司 | Path planning method for unmanned excavator, electronic equipment and medium |
| CN117387633B (en) * | 2023-12-13 | 2024-05-14 | 山东常林智能装备科技有限公司 | Path planning method for unmanned excavator, electronic equipment and medium |
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