CN112197778A - Wheeled airport border-patrol robot path planning method based on improved A-x algorithm - Google Patents
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Abstract
The invention discloses a wheeled airport border-patrol robot path planning method based on an improved A-x algorithm, which comprises the following steps of: constructing a grid map according to an airport environment, and planning a stop point and a running path of an airport border patrol robot; according to the inspection task, performing global path planning by using an improved A-x algorithm to obtain a running path of the robot; and performing path smoothing processing. The invention improves the traditional A-path planning algorithm aiming at the inspection situation of the inspection robot of the wheeled airport. On the basis of the path planned by the traditional A-x algorithm, smoothing treatment is carried out by taking the deviation degree and the total path length as evaluation indexes, and the optimal solution is obtained through multiple iterations by using a gradient descent method, so that the path becomes continuous and smooth, the number of inflection points and the total path length are reduced, and the stability and the efficiency of the robot in the driving process are ensured.
Description
Technical Field
The invention belongs to the field of path planning, and particularly relates to a wheel type airport border patrol robot path planning method based on an improved A-x algorithm.
Background
With the continuous development of human society to intellectualization, intelligent robots are increasingly applied to various industries. For the field of mobile robots, path planning has been the focus of research. With the continuous research of people, many excellent algorithms are proposed and applied to solve the practical problems. Such as Dijkstra's algorithm, a-algorithms, ant colony algorithms, genetic algorithms, and the like. With the continuous improvement of the requirements of the engineering practice on the accuracy and the high efficiency of path planning, the use of a certain independent algorithm often cannot meet the requirements due to the existence of larger limitations.
An algorithm A is firstly proposed in the seventies of the last century [ Peter E.Hart, Nils J.Nilsson, Bertram Raphel. Correction to A Formal Basis for the statistical Determination of Minimum Cost Paths ". 1972: 28-29 ], and an Heuristic function is added on the Basis of Dijkstra, so that the algorithm A is a Heuristic algorithm and is also the most effective method for solving the shortest path under a known static map, and the ingenious and efficient method is widely applied in the related fields. Meanwhile, researchers are continuously researching the improvement of the A algorithm and the application combination of the A algorithm, such as the improvement of the A algorithm speed and efficiency [ Ji 21197a ], the improvement of Wen, Zhang, Chui Linhao, Zhang, static indoor path planning A-Star algorithm [ J ] mapping geographic information, 2017,42(05):77-80 ], and the like. However, path optimization is more important for the a-algorithm. In the traditional A-star algorithm, an 8-neighborhood expansion mode is fixedly adopted during node selection, and at most 8 motion directions can be selected around the node, so that the motion angle is limited to integral multiple of pi/4, the robot is not favorable to steering, and the final path turning points are more and unsmooth.
Disclosure of Invention
The invention aims to provide a wheeled airport border-patrol robot path planning method based on an improved A-x algorithm, aiming at the problems in the prior art.
The technical solution for realizing the purpose of the invention is as follows: a method for planning a path of a wheeled airport border robot based on an improved a-x algorithm, comprising the following steps:
step 1, constructing a grid map according to an airport environment, and planning a stop point and a running path of an airport border patrol robot;
and 2, planning a global path by using an improved A-x algorithm according to the inspection task to obtain a driving path of the robot.
Further, step 1, constructing a grid map according to the airport environment, and planning the stop points and the running paths of the border patrol robots of the airport, specifically comprising:
1-1, constructing a two-dimensional map of a characteristic coefficient environment by a laser navigation system by using a laser ranging sensor and a mileometer carried by an airport border patrol robot;
step 1-2, planning a stop point sequence and a running path of an airport border patrol robot;
and 1-3, constructing a grid map according to the two-dimensional map and the stop point information, and setting barrier nodes and accessible nodes in the map.
Further, in step 2, according to the routing inspection task, a global path planning is performed by using an improved a-x algorithm to obtain a driving path of the robot, and the method specifically includes:
step 2-1, establishing the actual cost g (n) of the current node n:
in the formula IiThe actual mileage cost of the mobile unit at the ith node is represented and calculated by using the Euclidean distance;
step 2-2, calculating an estimated cost h (n) from the current node n to the end point:
h(n)＝|Tx-Cx|+|Ty-Cy|
wherein the coordinate of the current node in the grid map is C (C)x,Cy) The coordinate of the target point in the grid map is T (T)x,Ty)；
Step 2-3, establishing a heuristic function according to the actual cost g (n) and the estimated cost h (n):
f(n)＝g(n)+h(n)
step 2-4, establishing an opening list and a closing list, taking a first stop point from the stop point sequence planned in the step 1-2 as an initial node, and taking a second stop point as a target node;
step 2-5, adding the starting node into the opening list;
step 2-6, taking the node with the minimum f value in the opening list as the current node and expanding the node to the periphery to obtain 8 expansion nodes, and moving the current node into the closing list;
2-7, sequentially judging whether the 8 expansion nodes are barrier nodes or exist in a closing list, and if so, ignoring the nodes; otherwise, executing the step 2-8;
step 2-8, judging whether the expansion node is in the opening list, if not, adding the expansion node into the opening list, setting the current node as a father node of the expansion node, and recording f, g and h values of the square; otherwise, judging whether the g value from the current node to the expansion node is smaller than the originally recorded g value, if so, setting the father node of the expansion node as the current node, and updating the f and g values;
step 2-9, after the target node is added into the opening list through the process, the path is searched from the target node to the starting node along the father node in sequence, and a path planning result point sequence { P }is obtainedi1,2, …, n, where n is the number of nodes in the path;
and 2-10, setting the target node in the step 2-9 as a new starting node, setting the next point behind the target node in the step 2-9 in the stop point sequence as a new target node, and repeating the steps 2-5 to 2-10 until all the stop points are added into the path.
Further, the method further comprises the steps of: and step 3, performing path smoothing processing.
Compared with the prior art, the invention has the following remarkable advantages: aiming at the problem of planning the route of the airport boundary inspection robot, the method can plan the traveling route of the robot among a plurality of stop points in sequence, takes the deviation degree and the total route length as evaluation indexes, and carries out smoothing treatment on the basis of the route planned by the traditional A-x algorithm, so that the route becomes continuous and smooth, the number of inflection points and the total route length are reduced, and the stability of the robot in the driving process is ensured.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
Fig. 1 is a flow chart of a wheeled airport border robot path planning method based on an improved a-x algorithm.
FIG. 2 is a two-dimensional grid map in one embodiment.
Fig. 3 is a schematic diagram of a path obtained by the conventional a-algorithm in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, a method for wheeled airport border robot path planning based on the improved a-x algorithm is provided, which comprises the following steps:
step 1, constructing a grid map according to an airport environment, and planning a stop point and a running path of an airport border patrol robot;
and 2, planning a global path by using an improved A-x algorithm according to the inspection task to obtain a driving path of the robot.
Further, in one embodiment, the constructing a grid map according to an airport environment in step 1, and planning a stop point and a travel path of an airport border patrol robot specifically includes:
1-1, constructing a two-dimensional map of a characteristic coefficient environment by a laser navigation system by using a laser ranging sensor and a mileometer carried by an airport border patrol robot;
step 1-2, planning a stop point sequence and a running path of an airport border patrol robot;
and 1-3, constructing a grid map (as shown in figure 2) according to the two-dimensional map and the stop point information, and setting barrier nodes and accessible nodes in the map.
Further, in one embodiment, the stopping points in step 1-2 include a key point and a patrol point, the key point is a point where the robot can change the moving direction and position, and the patrol point is a point where the airport border patrol robot stops and executes a patrol task; the running path is the parking order of the parking points.
Further, in one embodiment, in step 2, according to the inspection task, the global path planning is performed by using an improved a-x algorithm to obtain the travel path of the robot, which specifically includes:
step 2-1, establishing the actual cost g (n) of the current node n:
in the formula IiThe actual mileage cost of the mobile unit at the ith node is represented and calculated by using the Euclidean distance;
step 2-2, calculating an estimated cost h (n) from the current node n to the end point:
h(n)＝|Tx-Cx|+|Ty-Cy|
wherein the coordinate of the current node in the grid map is C (C)x,Cy) The coordinate of the target point in the grid map is T (T)x,Ty)；
Step 2-3, establishing a heuristic function according to the actual cost g (n) and the estimated cost h (n):
f(n)＝g(n)+h(n)
step 2-4, establishing an opening list and a closing list, taking a first stop point from the stop point sequence planned in the step 1-2 as an initial node, and taking a second stop point as a target node;
step 2-5, adding the starting node into the opening list;
step 2-6, taking the node with the minimum f value in the opening list as the current node and expanding the node to the periphery to obtain 8 expansion nodes, and moving the current node into the closing list;
2-7, sequentially judging whether the 8 expansion nodes are barrier nodes or exist in a closing list, and if so, ignoring the nodes; otherwise, executing the step 2-8;
step 2-8, judging whether the expansion node is in the opening list, if not, adding the expansion node into the opening list, setting the current node as a father node of the expansion node, and recording f, g and h values of the square; otherwise, judging whether the g value from the current node to the expansion node is smaller than the originally recorded g value, if so, setting the father node of the expansion node as the current node, and updating the f and g values;
step 2-9, after the target node is added into the opening list through the process, the path is searched from the target node to the starting node along the father node in sequence, and a path planning result point sequence { P }is obtainedi1,2, …, n, where n is the number of nodes in the path;
and 2-10, setting the target node in the step 2-9 as a new starting node, setting the next point behind the target node in the step 2-9 in the stop point sequence as a new target node, and repeating the steps 2-5 to 2-10 until all the stop points are added into the path.
Illustratively, a certain robot travel path obtained through the above-described process is shown in fig. 3.
In one embodiment, the method further comprises the steps of:
and step 3, performing path smoothing processing.
Further, in one embodiment, the performing the path smoothing processing in step 3 specifically includes:
step 3-1, assuming that the path planning point sequence after the smoothing processing is { P }i'|i＝1,2,…,n}；
The constructed path smoothing objective function is:
min(α||Pi-Pi'||2+β||Pi'-Pi+1'||)
wherein, | | Pi-Pi'||2The deviation degree is the deviation degree of the smoothed path point and the original path point; i Pi'-Pi+1' I is the path length, namely the distance between each point and the adjacent point after smoothing; alpha and beta are respectively smoothing coefficients corresponding to the offset degree and the path length;
here, preferably, α is 0.6 and β is 0.6.
Step 3-2, iteratively solving the objective function by using a gradient descent method, and continuously adjusting Pi' minimize the objective function.
The invention improves the traditional A-path planning algorithm aiming at the inspection situation of the inspection robot of the wheeled airport. On the basis of the path planned by the traditional A-x algorithm, smoothing processing is carried out by taking the offset and the total path length as evaluation indexes, and an optimal solution is obtained through multiple iterations by using a gradient descent method, so that the path becomes continuous and smooth, and the number of inflection points and the total path length are reduced. The improved algorithm mainly aims at the problem of planning the route of the airport boundary inspection robot, the traveling route of the robot among a plurality of target stop points is better planned in sequence, an optimized target function is designed as an evaluation index, smoothing is carried out, and the stability and the efficiency of the robot in the driving process are guaranteed.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A wheeled airport border robot path planning method based on an improved A-x algorithm is characterized by comprising the following steps:
step 1, constructing a grid map according to an airport environment, and planning a stop point and a running path of an airport border patrol robot;
and 2, planning a global path by using an improved A-x algorithm according to the inspection task to obtain a driving path of the robot.
2. The method for planning the path of the wheeled airport border patrol robot based on the improved a-x algorithm according to claim 1, wherein the step 1 of constructing the grid map according to the airport environment and planning the stop points and the travel path of the airport border patrol robot comprises:
1-1, constructing a two-dimensional map of a characteristic coefficient environment by a laser navigation system by using a laser ranging sensor and a mileometer carried by an airport border patrol robot;
step 1-2, planning a stop point sequence and a running path of an airport border patrol robot;
and 1-3, constructing a grid map according to the two-dimensional map and the stop point information, and setting barrier nodes and accessible nodes in the map.
3. The improved A algorithm-based wheeled airport border robot path planning method according to claim 2, wherein the stopping points in the step 1-2 comprise key points and patrol points, wherein the key points are points at which the robot can change the motion direction and position, and the patrol points are points at which the airport border robot stops and executes patrol tasks; the running path is the parking order of the parking points.
4. The method for planning the route of the wheeled airport border patrol robot based on the improved a-algorithm according to claim 3, wherein the step 2 is to perform global route planning by using the improved a-algorithm according to the patrol task to obtain the driving route of the robot, and specifically comprises:
step 2-1, establishing the actual cost g (n) of the current node n:
in the formula IiThe actual mileage cost of the mobile unit at the ith node is represented and calculated by using the Euclidean distance;
step 2-2, calculating an estimated cost h (n) from the current node n to the end point:
h(n)＝|Tx-Cx|+|Ty-Cy|
in which the current node is at the grid groundThe coordinate in the figure is C (C)x,Cy) The coordinate of the target point in the grid map is T (T)x,Ty)；
Step 2-3, establishing a heuristic function according to the actual cost g (n) and the estimated cost h (n):
f(n)＝g(n)+h(n)
step 2-4, establishing an opening list and a closing list, taking a first stop point from the stop point sequence planned in the step 1-2 as an initial node, and taking a second stop point as a target node;
step 2-5, adding the starting node into the opening list;
step 2-6, taking the node with the minimum f value in the opening list as the current node and expanding the node to the periphery to obtain 8 expansion nodes, and moving the current node into the closing list;
2-7, sequentially judging whether the 8 expansion nodes are barrier nodes or exist in a closing list, and if so, ignoring the nodes; otherwise, executing the step 2-8;
step 2-8, judging whether the expansion node is in the opening list, if not, adding the expansion node into the opening list, setting the current node as a father node of the expansion node, and recording f, g and h values of the square; otherwise, judging whether the g value from the current node to the expansion node is smaller than the originally recorded g value, if so, setting the father node of the expansion node as the current node, and updating the f and g values;
step 2-9, after the target node is added into the opening list through the process, the path is searched from the target node to the starting node along the father node in sequence, and a path planning result point sequence { P }is obtainedi1,2, …, n, where n is the number of nodes in the path;
and 2-10, setting the target node in the step 2-9 as a new starting node, setting the next point behind the target node in the step 2-9 in the stop point sequence as a new target node, and repeating the steps 2-5 to 2-10 until all stop points are added into the path.
5. The method for planning the path of the wheeled airport border robot based on the improved a-algorithm according to any one of claims 1 to 4, further comprising the following steps:
and step 3, performing path smoothing processing.
6. The method for planning the path of the wheeled airport border robot based on the improved a-algorithm according to claim 5, wherein the step 3 of performing the path smoothing process specifically comprises:
step 3-1, assuming that the path planning point sequence after the smoothing processing is { P }i'|i＝1,2,…,n}；
The constructed path smoothing objective function is:
min(α||Pi-Pi'||2+β||Pi'-Pi+1'||)
wherein, | | Pi-Pi'||2The deviation degree is the deviation degree of the smoothed path point and the original path point; i Pi'-Pi+1' I is the path length, namely the distance between each point and the adjacent point after smoothing; alpha and beta are respectively smoothing coefficients corresponding to the offset degree and the path length;
step 3-2, iteratively solving the objective function by using a gradient descent method, and continuously adjusting Pi' minimize the objective function.
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