CN110032182B - Visual graph method and stable sparse random fast tree robot planning algorithm are fused - Google Patents
Visual graph method and stable sparse random fast tree robot planning algorithm are fused Download PDFInfo
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- 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
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- 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
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
Abstract
The invention relates to a robot planning algorithm for fusing a visual graph method and a stable sparse random fast tree. The method comprises the following steps: s1, constructing a topological graph based on a visual graph method to model an environment; s2, obtaining a shortest path by using a dijkstra algorithm and using the shortest path as a reference path; s3, dividing the reference path, and randomly sampling the reference path within a certain range by combining an SST algorithm and utilizing an average sampling strategy; s4, improving algorithm efficiency by using the Bias-coarse; s5, selecting the nearest tree node in the area range from the current sampling point according to the Dubins distance in the current expansion range; s6, selecting control quantity by adopting a transverse control strategy to perform integral on a system model, and preferentially expanding the nodes with optimal dissipation; s7, if the expansion process is collision-free, whether the generated new node is an optimal node in a local neighborhood is examined; and if the current area is optimal, adding the tree structure, and trimming the leading node of the current area. The method optimizes the path generated by the visual graph method by using a stable sparse random fast tree algorithm to obtain the optimal path conforming to the constraint of the non-integrity constraint robot.
Description
Technical Field
The invention belongs to the field of artificial intelligence automatic control, and particularly relates to a robot planning algorithm integrating a visual graph method and a stable sparse random fast tree.
Background
The visual Graph method (Visibility Graph) is proposed by Lozano and Wesley. The visual map method equates all real obstacles to a set of polygons projected into a plane. And expanding the points corresponding to the starting point and the target point in the space into a polygon set, then connecting the vertexes of all obstacles (V0 is a set formed by the vertexes of all the obstacles), the starting point s and the target point G by using a straight line combination, and simultaneously requiring that the connecting lines between the 3 people cannot pass through the obstacles, namely the straight line is visible, giving weight values to the edges in the graph to construct a graph G (V, E), wherein the node set V is V0U (s, G), E is a set of all arc sections (Pi, Pj), namely the edges, and the line segment connecting the ith and jth nodes in the G is not intersected with any obstacle. G (V, E) is called a visible view because adjacent vertices in the figure can see each other. Then an algorithm (e.g. Dijkstra algorithm) is used to search for an optimal path from the start point s to the target point g. The problem of planning an optimal path translates into the problem of the shortest distance from the starting point to the target point through these visible straight lines.
The visual graph method has the advantages that the concept is visual and simple, and the nearest path from the starting point to the target point can be obtained; the disadvantage is that the flexibility is poor and it is cumbersome to reconstruct the visual representation when the target point or obstacle or starting point changes, but an optimal path can be obtained at any time.
The Random-expanding Random Tree (RRT) is an algorithm for efficiently searching a non-convex, high-dimensional space by randomly constructing a space filling Tree. The tree is built up step by step from samples taken randomly in the search space and inherently tends to grow towards most of the unexplored area. The advantage of the algorithm is mainly embodied in the aspects of good model compatibility, no influence of grid resolution, compatibility with dynamic environment and the like, however, the application of the RRT algorithm is limited by the problem of path quality and cannot be optimized along with the increase of the number of samples.
A Stable Sparse random rapid tree algorithm (SST) is a gradually-optimized random sampling algorithm, and is obtained by improving and enhancing a process of selecting a parent node and expanding of the RRT algorithm and adding a tree pruning process. The SST selects a parent node process is an optimization selection process that follows an optimization-first policy. Within the current expanse, at random point δBNWithin the range of the area (2), the node with the optimal dissipation is preferentially expanded. In an expansion link, the SST randomly selects a control quantity in a control domain based on a Monte Carlo method, and then integrates a system model. If the expansion process is collision-free, examine xnewWhether or not in the local neighborhood δsThe inner is the optimal node. If it is optimal, it is added to the knotConstruct and trim at δsThe domain occupies the dominant pre-existing node. Pruning is the core process of SST algorithm optimization, which divides C space into a finite small space using the s-domain and checks in each iteration whether nodes in the s-domain are locally optimized. By this process, SST maintains the optimized properties of the nodes in the s-domain at the existing iteration scale. The optimization can deny the addition of the suboptimal node, and meanwhile, the optimization node is encouraged to expand more, so that the aim of global optimization is fulfilled.
For the planning problem of the robot with incomplete constraint in the unstructured road environment, no completely mature technology exists at present, and the existing technology has the following problems: 1. the shortest possible path generated cannot be guaranteed; 2. the generated path does not satisfy the non-integrity constraint of the robot; 3. the algorithm has a slow convergence speed.
The RRT algorithm construction process cannot accurately reflect the constraint of the system. For non-integrity-constrained systems, for example, vehicle systems are required to roll along the direction of wheel travel during movement and not be able to slide laterally. Due to the rolling contact, the wheels are subjected to speed constraints, and the constraints cannot reduce the degree of freedom of the system, so that the control variable of the system is less than the dimension of the system. The main impact of such system constraints on the RRT algorithm is nearest node determination. The euclidean distance does not reflect the true distance between two points subject to differential constraints. One important factor affecting the performance of the RRT, the choice of distance metric, is involved here. The RRT connects the tree nodes closest to the random point each time to reduce the probability of connection failure (the probability of obstacles existing in a short distance is lower than that in a long distance), so that the tree structure can quickly cover all barrier-free spaces. Generally, Euclidean distance is preferred. But for vehicle systems that are subject to non-integrity differential constraints, euclidean distance is not applicable. At two points in the european style that are close to each other, the robot may need to make a large turn to reach from one point to another.
The application of the RRT algorithm is limited by the path quality problem and cannot be optimized as the number of samples increases; the path generated by the RRT algorithm is not optimal and is subject to random sampling, and the path generated each time is different, and the path quality cannot be guaranteed.
Disclosure of Invention
In order to overcome at least one defect in the prior art, the invention provides a planning algorithm for a robot which integrates a visual graph method and a stable sparse random fast tree, and a stable sparse random fast tree algorithm (SST) is used for optimizing a path generated by the visual graph method so as to obtain an optimal path conforming to the constraint of a non-integrity constrained robot.
In order to solve the technical problems, the invention adopts the technical scheme that: a robot planning algorithm for fusing a visual graph method and a stable sparse random fast tree comprises the following steps:
s1, constructing a topological graph based on a visual graph method to model an environment;
s2, obtaining a shortest path by using a dijkstra algorithm, and taking the path as a reference path;
s3, dividing the reference path, and gradually sampling the reference path within a certain range by combining an SST algorithm and utilizing an average sampling strategy;
s4, using the Bias-coarse to improve the algorithm efficiency, and judging whether the current target point is a random point on the reference path or a final target point by a random function and a parameter Bias-coarse in order to improve the sampling efficiency and the number of generated paths, so that the target point can be found more efficiently; the parameter Bias-coarse is selected to represent the probability that the algorithm determines that the current target point is the final target point, if the Bias-coarse is larger than the random value generated by the algorithm, the target point is selected as the final target point, otherwise, a random point is selected; the method can greatly improve the speed of the algorithm on the premise of not influencing the probability integrity;
s5, selecting delta from the current sampling point according to the Dubins distance in the current expansion rangeBNNearest (dissipation-optimal) tree nodes within the territory; the Dubins curve is the shortest path connecting two-dimensional planes (i.e., X-Y planes) under the condition that curvature constraints and specified tangential directions of the starting end and the ending end are satisfied, and it is assumed that the road on which the vehicle is traveling can only travel forward; for wheeled robots, the maximum turning rate corresponds to a certain minimum turning radius (and equivalently maximum curvature); the specified initial and terminal tangential directions corresponding to the initialAnd terminal coordinates; the Dubins path gives the shortest path of two orientation points, which is a practical operation route for the wheeled robot model;
s6, selecting control quantity by adopting a transverse control strategy to perform integral on a system model, and preferentially expanding the nodes with optimal dissipation;
s7, if the expansion process is collision-free, the generated new node x is inspectednewWhether it is the optimal node within the local neighborhood; if optimal, it is added to the tree structure and prunes the existing nodes that dominate the current domain.
The algorithm ensures the optimization attribute of the current neighborhood while maintaining the existing iteration scale, the addition of suboptimal nodes can be denied through the optimization, and the optimization nodes are encouraged to expand, so that the aim of global optimization is fulfilled.
Further, the step S3 specifically includes: firstly, randomly selecting a point on a reference path, taking the point as a local target point, randomly sampling the periphery of the point within a range taking r as a radius by using an average sampling strategy, and selecting a sampling point without collision.
Further, the step S6 specifically includes: in the expanding process, firstly, a control quantity is selected according to a generated track, a transverse control strategy is adopted, the robot is orthogonally projected to the closest point of a reference track, and the yaw of the virtual robot is aligned with the tangent line of the projection point of the track at the center of the rear shaft of the robot; the projected front wheel is turned so that its turning radius coincides with the curvature of the reference trajectory at the projected point of the centre of the rear axle of the robot, which ensures that it is always on the reference trajectory, while the control speed selects a random quantity depending on the actual situation.
Compared with the prior art, the beneficial effects are: the invention provides a robot planning algorithm integrating a visual graph method and a stable sparse random rapid tree, and creates a planning method under a non-structured environment and a shortest path planning method suitable for a robot with non-integrity constraint; a heuristic acceleration method is provided for the application of a gradual optimization random sampling algorithm in unmanned driving; the method adopts a parent node selection strategy integrating near point priority and optimization priority to ensure the gradual optimization characteristic of the algorithm, simultaneously improves the tree expansion efficiency by combining the expansion strategy of the Dubins curve optimization, prunes the generated tree to ensure the sparsity of the tree and reduces the algorithm complexity.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a parametric illustration of a non-integrity constrained system.
FIG. 3 is a topological diagram of a visualization method for constructing an obstacle environment.
Fig. 4 is a shortest path graph found from a topology graph using dijkstra.
Fig. 5 is a schematic diagram of randomly selecting a reference path partition point to uniformly sample a certain range of the reference path partition point.
FIG. 6 is a schematic diagram of reference path sampling points.
Fig. 7 is a Dubins distance diagram.
FIG. 8 is a schematic diagram of a lateral control strategy model.
Fig. 9 is a schematic diagram of an extended trimming process.
Fig. 10 is a comparison graph of the final optimized traffic road and the reference route.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
Example 1:
the invention combines a visual graph method and an SST method to carry out path planning on the incomplete constraint robot under the unstructured road.
In the system of incomplete condition constraint, the constraint of the obstacle and the parameter constraint related to the incomplete constraint need to be considered.
As shown in fig. 2, the state of the robot can be represented by q (x, y, θ). The input (V, phi) is used to represent the input control variable. The non-integrity constraints experienced by the system can be expressed as:
dxsinθ-dycosθ＝0
wherein theta is an included angle between the robot and the X axis; phi is a first direction angle; v is the front wheel speed; l is the distance between the front wheel and the rear wheel; delta is the positive angle of V relative to the X axis.
In consideration of the incomplete constraint of the robot, the invention adopts the Dubins curve to approximately represent the steering limit motion of the robot. And planning the path by adopting the following method:
step 1, firstly, a topological graph is constructed based on a visual graph method to model the environment, and the topological graph is shown in figure 3.
Step 2, obtaining the shortest path by utilizing dijkstra algorithm, and taking the path as a reference path; as shown in fig. 4.
And 3, taking the shortest path as a reference path, dividing the reference path at intervals of 30 meters, and gradually sampling the reference path within a certain range by combining an SST algorithm and utilizing an average sampling strategy: firstly, randomly selecting a point on a reference path, taking the point as a local target point, randomly sampling the periphery of the point within a range taking r as a radius by using an average sampling strategy, and performing collision detection on a sampling point to judge whether the current sampling point has no collision; as shown in fig. 5, the thick solid line in the figure is used as the shortest path to serve as the reference path.
Step 4, using the Bias-coarse to improve the algorithm efficiency, and judging whether the current target point is a random point on the reference path or a final target point by a random function and a parameter Bias-coarse; if the Bias-coarse is larger than the random value generated by the algorithm, selecting the target point as a final target point, otherwise, selecting a random point; as shown in fig. 6.
And 5, under the condition that the current sampling point is legal, selecting the tree node closest to the current sampling point according to the Dubins distance: the Dubins curve is the shortest path connecting two-dimensional planes (i.e., X-Y planes) under the condition that curvature constraints and prescribed tangential directions of the start and end are satisfied, and assumes that the road on which the vehicle is traveling can only travel forward. For a wheeled robot, the maximum turning rate corresponds to a certain minimum turning radius (and equivalently maximum curvature). The specified initial and terminal tangential directions correspond to initial and terminal coordinates. The Dubins path gives the shortest path of two directional points, which is a realistic runnable route for the wheeled robot model. As shown in fig. 7.
Step 6. delta at sampling point in current expansion rangeBNAnd in the area range, the node with optimal dissipation is preferentially expanded. In the expansion process, firstly, the control quantity is selected according to the generated track, and a transverse control strategy is adopted, as shown in fig. 8; the robot is projected orthogonally to the closest point of the reference trajectory, the rear axis center O is projected onto R, and the yaw of the virtual robot is aligned with the tangent of the trajectory at the beginning of R. The projected front wheel is turned so its turning radius coincides with the curvature of the reference trajectory at R, which ensures that it is always on the reference trajectory. And the control speed selects random quantity according to actual conditions.
Step 7, further integrating the system model, and if the expansion process is collision-free, inspecting the generated new node xnewWhether it is the optimal node within the local neighborhood. If optimal, it is added to the tree structure and prunes the existing nodes that dominate the current domain. The algorithm ensures the optimization attribute of the current neighborhood while maintaining the existing iteration scale, the addition of suboptimal nodes can be denied through the optimization, and the optimization nodes are encouraged to expand, so that the aim of global optimization is fulfilled. As shown in fig. 9 and 10.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (2)
1. A robot planning algorithm for fusing a visual graph method and a stable sparse random fast tree is characterized by comprising the following steps:
s1, constructing a topological graph based on a visual graph method to model an environment;
s2, obtaining a shortest path by using a dijkstra algorithm, and taking the path as a reference path;
s3, dividing the reference path, and gradually sampling the reference path within a certain range by combining an SST algorithm and utilizing an average sampling strategy;
s4, improving algorithm efficiency by using the parameter Bias-coarse, and judging whether the current target point is a random point on the reference path or a final target point by using a random function and the parameter Bias-coarse; if the parameter Bias-coarse is larger than the random value generated by the algorithm, selecting the target point as a final target point, otherwise, selecting a random point;
s5, selecting delta from the current sampling point according to the Dubins distance in the current expansion rangeBNNearest tree nodes within the region;
s6, selecting control quantity by adopting a transverse control strategy to perform integral on a system model, and preferentially expanding the nodes with optimal dissipation; the method specifically comprises the following steps: in the expanding process, firstly, a control quantity is selected according to a generated track, a transverse control strategy is adopted, the robot is orthogonally projected to the closest point of a reference track, and the yaw of the virtual robot is aligned with the tangent line of the projection point of the track at the center of the rear shaft of the robot; the projected front wheel is rotated, and the turning radius of the front wheel is consistent with the curvature of the reference track at the central projection point of the rear shaft of the robot;
s7, if the expansion process is collision-free, the generated new node x is inspectednewWhether it is the optimal node within the local neighborhood; if it is optimal, it is added to the tree structure and pruned atThe existing nodes that dominate the current domain.
2. The fusion visual graph method and stable sparse random rapid tree robot planning algorithm according to claim 1, wherein the step S3 specifically comprises: firstly, randomly selecting a point on a reference path, taking the point as a local target point, randomly sampling the periphery of the point within a range taking r as a radius by using an average sampling strategy, and selecting a sampling point without collision.
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