CN115755908B - JPS guided Hybrid A-based mobile robot path planning method - Google Patents
JPS guided Hybrid A-based mobile robot path planning method Download PDFInfo
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Abstract
The invention discloses a path planning method of a mobile robot based on JPS guided hybrid A, which optimizes by utilizing the expansion characteristic of the JPS algorithm on the basis of the classical hybrid A algorithm and considers h under a two-dimensional grid collision_avoidance1 When inspiring the value, a JPS algorithm is used for replacing an A algorithm to calculate the length; in calculating g extend Adding p to the function value bias And (5) punishing items, traversing by using a JPS algorithm to obtain a guide path point set. Compared with the classical Hybrid A algorithm, the method has remarkable improvement effect on the traversal speed, iteration times and path length.
Description
Technical Field
The invention relates to the field of mobile robot path planning, in particular to a mobile robot path planning method based on JPS guiding Hybrid A.
Background
Path planning research is an important component in the field of mobile robot research, and its purpose is to enable a mobile robot to find a collision-free path from a starting state to a target state in a known or unknown environment. Most of the traditional path planning algorithms only consider the pose space of the robot, however, in reality, the robot is constrained not only by the pose space, but also by various external forces. The path planning technology is an important core technology of the mobile robot technology, and is even more focused by vast researchers.
The conventional mobile robot path planning algorithm is to perform path planning by using a graph searching algorithm such as an A-x algorithm or a Dijkstra algorithm after the map is rasterized, and the algorithms do not consider the robot kinematics constraint when generating the path, so that the planned path is mostly discrete and does not meet the robot incompleteness constraint; the Hybrid A algorithm is used as an expansion of the A algorithm, a robot kinematics constraint condition is added in the algorithm, and discrete expansion points are modified into continuous expansion points, so that the robot can obtain a continuous executable path.
However, when the Hybrid a algorithm expands in a wide free space area and the end point is located in a narrow area, a large number of alternative nodes are added in the list, and only a small number of nodes can guide the path into the narrow area, so that the iteration times are increased, and even a feasible path cannot be found.
Disclosure of Invention
The invention aims to: aiming at the prior art, a path planning method of a mobile robot based on JPS guiding Hybrid A is provided, and under a large-scale map, the node traversing speed is improved, the iteration times are reduced, and the output path length is improved.
The technical scheme is as follows: a mobile robot path planning method based on JPS guiding Hybrid A comprises the following steps:
step 1: initializing a global map; the method comprises the following steps: performing environment modeling and establishing a two-dimensional coordinate system, discretizing all barriers in the environment and representing by uniformly distributed point sets, wherein the number of barriers is k, and the composition barrier set is { OBS } i "OBS", i.e. OBS 1 ，i＝1,…,k；
Step 2: initializing parameters; the method comprises the following steps: setting initial pose Start (x) in map init ,y init ,θ init ) Final point pose gold (x end ,y end ,θ end ) Constructing a node sampling function Expansion_pattern, a list to be explored Openlist, an explored list Closedlist and a mobile robot kinematic model; wherein, (x) * ,y * ) The mass center of the kinematic model of the mobile robot is absolute under the condition thatFor the coordinates in the coordinate system, θ * Representing the heading angle of the mobile robot under the condition;
step 3: selecting a node with the minimum cost value from the Openlist to be explored as a current node, searching for the next expansion node through forward simulation, judging whether the distance between the expansion node and the terminal point is smaller than a threshold value, if so, continuing the step 6, otherwise, continuing the step 4;
step 4: calculating cost value g from starting point to expansion node extend Expanding the node pose to the end pose and simultaneously considering the heuristic value h of the kinematic constraint and obstacle avoidance condition of the mobile robot extend And calculates the total cost value f of the expansion node extend ；
Step 5: the expansion node and the total cost value f extend Putting the search result into an Openlist to be explored, and returning to the step 3 for continuing;
step 6: connecting the expansion node with the end point by using a Reeds-sheet curve, judging whether an obstacle exists on the curve or not, and if yes, returning to the step 3 to continue; otherwise, returning to the path point set;
step 7: resampling the path point set and outputting an optimized path.
Further, in the step 2, the specific process of constructing the node sampling function expansion_pattern is as follows:
the algorithm is firstly that phi is epsilon { -phi max ,0,φ max }，v∈{-v max ,v max Uniformly sampling in the range of } to obtain sampling value, and then according to the sampling value [ v, phi ] in unit time]Forward simulating the moving robot to judge whether the sampling value accords with the collision condition; wherein v is the forward speed, phi is the deflection angle of the front wheel of the mobile robot, phi max V is the maximum deflection angle of the front wheel of the mobile robot max Is the maximum forward speed.
Further, in the step 3, the specific process of searching the expansion node by forward simulation is as follows:
expanding a current node through a forward simulation calculation formula, and connecting the nodes by using a Reeds-shaep curve; the current node pose is N current (x current ,y current ,θ current ) Expanding the node pose to N expend (x expend ,y expend ,θ expend ) The forward simulation calculation formula is as follows:
x expend ＝x current +cosθ current vdt
y expend ＝y current +cosθ current vdt
wherein L is w Distance between front and rear axes of the kinematic model of the mobile robot; n is a constant.
Further, in the step 4, the total cost value f of the extension node is calculated extend The specific process of (2) is as follows:
f extend ＝g extend +h extend
wherein:
g extend ＝g current +C simu +p bias +p v_change +p v_reverse +p phy_change
in the formula g current C for the cost value of the starting point expansion to the current node simu For forward simulation cost value, p, superimposed each time a child node is extended bias To extend the penalty of a node for deviating from the bootstrap path, p v_change Penalty introduced when the mobile robot forward speed v is changed, p v_reverse Penalty introduced when the travelling speed v direction of the mobile robot is changed, p phy_change Penalty introduced when changing the front wheel deflection angle phi of the mobile robot;
h extend ＝β*max(h nonholonomics ,max(h collision_avoidance1 ,h collision_avoidance2 ))
wherein, beta is a weight coefficient of heuristic value, h nonholonomics To estimate the heuristic value from the extended junction pose to the end pose without considering obstacle avoidance by using the Reeds-shaep curve, h collision_avoidance1 Heuristic value h from expansion node to end point of JPS algorithm under two-dimensional grid collision_avoidance2 To extend the manhattan distance from the node to the endpoint.
Furthermore, in the step 3, a collision detection method is adopted for forward simulation and in the step 6, whether an obstacle exists on the curve or not is judged, and in the collision detection method, each path point is used as the axis of a rear shaft of the mobile robot, and a simple kinematic model is constructed by calculating four vertex coordinates of the mobile robot to detect boundary collision.
The beneficial effects are that: (1) According to the path planning method of the mobile robot based on the JPS guided Hybrid A, provided by the invention, as an improved algorithm of the Hybrid A, certain advantages of the JPS algorithm on node traversing speed and number are combined, so that the generating path of the JPS algorithm is more attached to obstacles and the output path is more simplified but not collided when the generating path of the JPS algorithm is turned over than the generating path of the A algorithm, and therefore, under a large-scale map, the improved algorithm has obvious index lifting effects of node traversing speed, iteration times, output path length and the like.
(2) When the Hybrid a algorithm expands in a wide free space region and the endpoint is located in a narrow region, the iteration times are increased due to the traversal characteristics of the Hybrid a algorithm, and even a feasible path cannot be found. The JPS algorithm is more sensitive to narrow areas with more obstacles because of the existence of forced population, so that the iteration number required for reaching the end point is greatly reduced compared with that of the A-algorithm.
Drawings
FIG. 1 is a flow chart of the present method;
FIG. 2 is a schematic diagram of the JPS algorithm;
FIG. 3 is a schematic diagram of a kinematic model of a mobile robot;
FIG. 4 is an initialization and parameter setting map;
fig. 5 and 6 are schematic diagrams of algorithm simulation results according to the present invention.
Detailed Description
The invention is further explained below with reference to the drawings.
Referring to fig. 1, a method for planning a path of a mobile robot based on JPS guidance Hybrid a in this embodiment mainly includes the following steps:
step 1: global map initialization. The method comprises the following steps: performing environment modeling and establishing a two-dimensional coordinate system, discretizing all barriers in the environment and representing the barriers by using a uniformly distributed point set, and assuming that the number of the barriers is k, and forming the barrier set as { OBS } i "OBS", i.e. OBS 1 ，i＝1,…,k。
Step 2: and initializing parameters. The method comprises the following steps: setting initial pose Start (x) in map init ,y init ,θ init ) Final point pose gold (x end ,y end ,θ end ) Constructing a node sampling function Expansion_pattern, a list to be explored Openlist, an explored list Closedlist and a mobile robot kinematic model; wherein, (x) * ,y * ) For the coordinates of the centroid of the kinematic model of the mobile robot in an absolute coordinate system under the condition of θ * The heading angle of the mobile robot is represented under the condition of initial pose init and end-point pose end.
In this step, the specific process of constructing the node sampling function expansion_pattern is as follows: the algorithm is firstly that phi is epsilon { -phi max ,0,φ max }，v∈{-v max ,v max Uniformly sampling in the range of } to obtain sampling value, and then according to the sampling value [ v, phi ] in unit time]Forward simulating the moving robot to judge whether the sampling value accords with the collision condition; wherein v is the forward speed, phi is the deflection angle of the front wheel of the mobile robot, phi max Is the most front wheel of the mobile robotLarge deflection angle v max Is the maximum forward speed.
Step 3: selecting a cost value f from an Openlist to be explored extend And (4) searching the next expansion node by forward simulation by taking the smallest node as the current node, judging whether the distance between the expansion node and the end point is smaller than a threshold value, if so, continuing the step (6), otherwise, continuing the step (4).
In the step, the specific process of searching the expansion node through forward simulation is as follows: and expanding the current node through a forward simulation calculation formula, and connecting the nodes by using a Reeds-shaep curve. The current node pose is N current (x current ,y current ,θ current ) Expanding the node pose to N expend (x expend ,y expend ,θ expend ) The forward simulation calculation formula is as follows:
x expend ＝x current +cosθ current vdt
y expend ＝y current +cosθ current vdt
wherein L is w The distance between the front and rear axes of the kinematic model of the mobile robot is constant, and the unit time is equally divided.
Step 4: calculating cost value g from starting point to expansion node extend Expanding the node pose to the end pose and simultaneously considering the heuristic value h of the kinematic constraint and obstacle avoidance condition of the mobile robot extend And calculates the total cost value f of the expansion node extend 。
In this step, the total cost value f of the extension node is calculated extend The specific process of (2) is as follows:
f extend ＝g extend +h extend
wherein:
g extend ＝g current +C simu +p bias +p v_change +p v_reverse +p phy_change
in the formula g current C for the cost value of the starting point expansion to the current node simu For forward simulation cost value, p, superimposed each time a child node is extended bias To extend the penalty of a node for deviating from the bootstrap path, p v_change Penalty introduced when the mobile robot forward speed v is changed, p v_reverse Penalty introduced when the travelling speed v direction of the mobile robot is changed, p phy_change A penalty introduced when changing the mobile robot front wheel yaw angle phi.
h extend ＝β*max(h nonholonomics ,max(h collision_avoidance1 ,h collision_avoidance2 ))
Wherein, beta is a weight coefficient of heuristic value, h nonholonomics To estimate the heuristic value from the extended junction pose to the end pose without considering obstacle avoidance by using the Reeds-shaep curve, h collision_avoidance1 Heuristic value h from expansion node to end point of JPS algorithm under two-dimensional grid collision_avoidance2 To extend the manhattan distance from the node to the endpoint. Taking the maximum value means that the larger of the two categories of heuristics will be used to represent a degree of difficulty in traveling from the expansion node to the endpoint.
Step 5: extension node and its cost value f extend And (3) putting the search result into an Openlist list of the list to be searched, and returning to the step (3) to continue.
Step 6: connecting the expansion node with the end point by using a Reeds-sheet curve, judging whether an obstacle exists on the curve or not, and if yes, returning to the step 3 to continue; otherwise, the set of path points is returned.
Step 7: resampling the path point set and outputting an optimized path.
In the method, a collision detection method is adopted in both forward simulation in the step 3 and judgment in the step 6 of whether an obstacle exists on a curve, and the algorithm is considered to carry out path planning based on a mobile robot kinematic model, so that each path point is used as the axis of a rear axis of the mobile robot, and a simple kinematic model is constructed by calculating four vertex coordinates of the mobile robot to carry out boundary collision detection.
Fig. 2 is a simplified schematic diagram of the JPS algorithm, fig. 3 is a kinematic model of the mobile robot built under an absolute coordinate system, the model is replaced by a simple rectangle in the algorithm, the model is replaced by only the mobile robot centroid during operation, fig. 4 is the initial environment and the starting and ending positions before the algorithm is planned, and fig. 5 and 6 are simulation results of the algorithm of the present invention in two different maps, respectively.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (2)
1. The path planning method of the mobile robot based on JPS guiding hybrid A is characterized by comprising the following steps:
step 1: initializing a global map; the method comprises the following steps: performing environment modeling and establishing a two-dimensional coordinate system, discretizing all barriers in the environment and representing by uniformly distributed point sets, wherein the number of barriers is k, and the composition barrier set is { OBS } i "i.e.)i＝1,…,k；
Step 2: initializing parameters; the method comprises the following steps: setting initial pose Start (x) in map init ,y init ,θ init ) Final point pose gold (x end ,y end ,θ end ) Constructing a node sampling function Expansion_pattern, a list to be explored Openlist, an explored list Closedlist and a mobile robot kinematic model; wherein, (x) * ,y * ) For the coordinates of the centroid of the kinematic model of the mobile robot in an absolute coordinate system under the condition of θ * Expressed under the following conditionsHeading angle of lower mobile robot;
step 3: selecting a node with the minimum cost value from the Openlist to be explored as a current node, searching for the next expansion node through forward simulation, judging whether the distance between the expansion node and the terminal point is smaller than a threshold value, if so, continuing the step 6, otherwise, continuing the step 4;
step 4: calculating cost value g from starting point to expansion node extend Expanding the node pose to the end pose and simultaneously considering the heuristic value h of the kinematic constraint and obstacle avoidance condition of the mobile robot extend And calculates the total cost value f of the expansion node extend ；
Step 5: the expansion node and the total cost value f extend Putting the search result into an Openlist to be explored, and returning to the step 3 for continuing;
step 6: connecting the expansion node with the end point by using a Reeds-sheet curve, judging whether an obstacle exists on the curve or not, and if yes, returning to the step 3 to continue; otherwise, returning to the path point set;
step 7: resampling the path point set and outputting an optimized path;
in the step 2, the specific process of constructing the node sampling function expansion_pattern is as follows:
the algorithm is firstly that phi is epsilon { -phi max ,0,φ max }，v∈{-v max ,v max Uniformly sampling in the range of } to obtain sampling value, and then according to the sampling value [ v, phi ] in unit time]Forward simulating the moving robot to judge whether the sampling value accords with the collision condition; wherein v is the forward speed, phi is the deflection angle of the front wheel of the mobile robot, phi max V is the maximum deflection angle of the front wheel of the mobile robot max Is the maximum forward speed;
in the step 3, the specific process of searching the expansion node by forward simulation is as follows:
expanding a current node through a forward simulation calculation formula, and connecting the nodes by using a Reeds-shaep curve; the current node pose is N current (x current ,y current ,θ current ) Expanding the node pose to N expend (x expend ,y expend ,θ expend ) The forward simulation calculation formula is as follows:
x expend ＝x current +cosθ current vdt
y expend ＝y current +cosθ current vdt
wherein L is w Distance between front and rear axes of the kinematic model of the mobile robot; n is a constant;
in the step 4, the total cost value f of the extension node is calculated extend The specific process of (2) is as follows:
f extend ＝g extend +h extend
wherein:
g extend ＝g current +C simu +p bias +p v_change +p v_reverse +p phy_change
in the formula g current C for the cost value of the starting point expansion to the current node simu For forward simulation cost value, p, superimposed each time a child node is extended bias To extend the penalty of a node for deviating from the bootstrap path, p v_change Penalty introduced when the mobile robot forward speed v is changed, p v_reverse Penalty introduced when the travelling speed v direction of the mobile robot is changed, p phy_change Penalty introduced when changing the front wheel deflection angle phi of the mobile robot;
h extend ＝β*max(h nonholonomics ,max(h collision_avoidance1 ,h collision_avoidance2 ))
wherein, beta is a weight coefficient of heuristic value, h nonholonomics Estimating the secondary expansion junction without considering obstacle avoidance for using Reeds-Sheep curveHeuristic value from point pose to end pose, h collision_avoidance1 Heuristic value h from expansion node to end point of JPS algorithm under two-dimensional grid collision_avoidance2 To extend the manhattan distance from the node to the endpoint.
2. The method for planning a path of a mobile robot based on JPS guided hybrid a of claim 1, wherein: in the step 3, a collision detection method is adopted in forward simulation and in the step 6, whether an obstacle exists on the curve or not is judged, each path point is used as the axis of a rear axle of the mobile robot in the collision detection method, and a simple kinematic model is built by calculating four vertex coordinates of the mobile robot to detect boundary collision.
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