CN113359768A - Path planning method based on improved A-x algorithm - Google Patents
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Abstract
A path planning method based on an improved A-x algorithm belongs to the field of unmanned path planning. The existing path planning method cannot cope with various situations and cannot accurately carry out global path planning due to temporary dynamic obstacles. A route planning method based on improved A-x algorithm, which is characterized in that collected road condition maps are processed by a grid method to obtain grid maps; then, optimizing and improving the A-star algorithm by using the ant colony algorithm; then, local path planning is carried out by adopting an artificial potential field method, and improvement and optimization are carried out aiming at the defects that the local minimum value and the target of the artificial potential field method are inaccessible; then, the improved A-algorithm is combined with an artificial potential field method to design a hybrid path planning algorithm, and a proper control algorithm is provided for subsequent vehicle path planning; and then, a feasible path is automatically planned by the system by adopting a proper path planning algorithm, and the vehicle avoids the barrier according to the path, so that the target position is accurately and quickly reached.
Description
Technical Field
The invention relates to a path planning method, in particular to a path planning method based on an improved A-x algorithm.
Background
Research on vehicle path planning technology in other countries in the world has been started for a long time, so that the research on the vehicle path planning technology in other countries in the world takes strict limitations and privacy measures on the acquired related technologies to prevent the technologies from being acquired by other countries, and especially, the research on the vehicle path planning technology in China is greatly limited in this respect. However, in China, the scientific and technological precipitation is not deep because of late start, and the precipitation also falls in the back of foreign countries. However, after the importance of the research in the direction is recognized, China struggles to pursue, and the research in the aspect of China also provides huge manpower, financial resources and material resources for support. The whole society, from enterprises to colleges and universities to scientific research institutes, forms a research mode of obstetrics and research integration, obtains huge achievements, gradually reduces the gap with developed countries in the research field of intelligent vehicles, and is even more excellent than the research of the intelligent vehicles in some aspects.
Our country's research on smart vehicles began in the 80's of the 20 th century [13 ]. Moreover, many researches are only carried out on one key technology in a certain aspect, and the comprehensiveness is not strong. Shenyang automation is through introducing the buried line formula technique on AGV, guides intelligent car through the guide formula means and navigates [, and its principle is through electromagnetic induction, guides AGV vehicle according to setting up and obtains the route path. Although the wire type path planning technology has low flexibility and low planning efficiency, the technology enables the vehicle to run automatically, which is an exploration research in the aspect of intelligent vehicle path planning in China. The CASIA-1 mobile robot developed by the automation of the Chinese academy of sciences detects the information of obstacles through an infrared sensor carried by the mobile robot, and automatically determines the advancing direction and the advancing speed after fusing the obtained information, thereby avoiding the obstacles encountered in the middle and quickly and accurately reaching the target position. The mobile robot is widely applied to public places such as libraries, hospitals, conference exhibition halls and the like. Hong Kong City university [16] developed a robot vehicle and a service type robot capable of autonomous navigation, which can autonomously reach a designated position through a path planning technology to complete some tasks. Research on intelligent robots is actively carried out by the university of qinghua, and the main results are thmr (tsinghua Mobile robot) series Mobile robots, and sensors carried by the Mobile robots are mainly a color camera and a laser radar. The sensor can collect surrounding environment information and process the information to guide the intelligent vehicle to run like two eyes of a person. After the environmental information is obtained, the feedback information is transmitted to the control system through the operation processing of the computer, and the route planning of the intelligent vehicle is carried out, so that the intelligent vehicle can smoothly reach the target position.
In the prior art, a hybrid algorithm is adopted to plan a path, and because the environment of the path planning is often unknown or partially known in practical application, a single global path planning cannot cope with various situations. For example, it is known for devices distributed in airports to plan a route from the location to the destination based on known information. However, if some dynamic obstacles temporarily appear, the global path planning cannot be accurately performed at this time, so that the vehicle cannot reach the target position.
Disclosure of Invention
The invention aims to solve the problems that the existing path planning method cannot cope with various situations and the global path planning cannot be accurately carried out due to temporary dynamic obstacles, and provides a path planning method based on an improved A-x algorithm.
A route planning method based on improved A-x algorithm is characterized in that collected road condition maps are processed by a grid method to obtain grid maps; then, optimizing and improving the A-star algorithm by using the ant colony algorithm; then, local path planning is carried out by adopting an artificial potential field method, and improvement and optimization are carried out aiming at the defects that the local minimum value and the target of the artificial potential field method are inaccessible; then, the improved A-algorithm is combined with an artificial potential field method to design a hybrid path planning algorithm, and a proper control algorithm is provided for subsequent vehicle path planning; and then, a feasible path is automatically planned by the system by adopting a proper path planning algorithm, and the vehicle avoids the obstacle according to the path so as to reach the target position.
In a preferred implementation of the present invention, the method for designing a hybrid path planning algorithm by combining the improved a-algorithm with the artificial potential field method specifically includes:
step one, carrying out global path planning by using an improved A-x algorithm to obtain an initial path;
step two, detecting whether a dynamic barrier exists in the map environment;
if so, performing local path planning by adopting an improved artificial potential field method, and performing improvement optimization on the local minimum value and the inaccessible defect of the target;
if not, the path planning is considered to be finished, and the final hybrid algorithm planning path is obtained.
Step three, judging whether a destination point is reached or not after local path planning is carried out by adopting an improved artificial potential field method;
if not, returning to the step two;
if so, the path planning is considered to be completed, and the final hybrid algorithm planning path is obtained.
In a preferred implementation of the present invention, the process of local path planning using the improved artificial potential field method is,
the artificial potential field method is improved by a method for correcting potential field and introducing escape force.
In a preferred implementation of the present invention, the processing of the collected road condition map by using a grid method to obtain the grid method of the grid map includes:
the grid method is that the space to be planned where the guide vehicle is located is decomposed into independent cells according to the standard that the guide vehicle can move freely in each cell to obtain grids which are connected with each other but are not overlapped; after rasterization, a traffic factor is given to each grid, and the path planning problem of the mobile robot in a grid map is converted into the problem of finding the shortest path between two grid nodes; if no obstacle exists in the grid, the robot can freely pass through the grid, and the grid is called a free grid, and is called an obstacle grid in the opposite direction; and after the grid identification is finished, the mobile robot searches and displays the path according to the coordinates or the sequence number.
In a preferred embodiment of the present invention, the global path planning based on the modified a-algorithm comprises the steps of,
the optimization improvement of the A-algorithm is carried out through the iterative optimization performance of the ant colony algorithm, and the optimization improvement specifically comprises the following steps:
establishing an ant path-finding model;
assume the following steps are satisfied:
τ1＝τ2＝τ3＝C＞0
in subsequent iterations, the following calculation formula for the pheromone increment is established:
in the formula: the total amount of Q pheromones is a fixed value; num ant population number;
ant k, with the value of 1, 2, 3, … Num, the pheromone quantity delta tau released in the process from i to jij(t) total pheromone concentration increased over segment ij after t cycles; when the ant colony algorithm is iterated, pheromones on the route can volatilize once at the end time of different periods;
establishing a transition probability formula:
in the formula: the rho pheromone volatilization coefficient is generally between 0 and 1; tau isij(t) the pheromone amount of the cycle; tau isij(t +1) information amount of t + 1; the core of the ant algorithm is that the transfer probability selection behavior of ants is simulated, the transfer probability is calculated according to pheromones and heuristic function values, the transfer probability is obtained through calculation, and subsequent ants can select paths according to the transfer probability; wherein the heuristic information is
dijIs the length between i and j.
In a preferred implementation of the present invention, the local path planning based on the improved artificial potential field method specifically includes:
when path planning is carried out, the periphery of the guided vehicle is regarded as a potential field environment, the target point generates an attraction force to the guided vehicle, and the barrier generates a repulsive force to the guided vehicle, so that the guided vehicle can advance towards the target point under the synthetic action of the two simulated forces; under the combined action of the repulsive force of the obstacle and the attractive force of the target point, the guided vehicle can avoid the obstacle and move towards the target point. From the superposition of forces, the total potential field function in the environment of the lead vehicle is:
Ut＝Ua+Ur
in the above formula, UaAn attractive force potential field; u shaperA repulsive force potential field; u shapetThe vehicle is in the total potential field of the environment. The resultant force experienced by the vehicle is:
Ft＝Fa+Fr
in the above formula, FaThe gravitational forces experienced by the vehicle; frThe repulsive force to which the vehicle is subjected. Suppose the vehicle coordinates are X ═ (X, y)TThen for gravitational potential field functions can be defined as:
in the above formula, λ gravitational potential field constant; an X guide vehicle position vector; xtThe position of the target in the environment of the lead vehicle, then the attractive force at this time can be represented by a negative gradient of the attractive force potential field function:
Fa＝-grad(Ua)＝-λ(X-Xt)＝λ(Xt-X)
as can be seen from the above formula, the attraction force is smaller and smaller when the guided vehicle gets closer to the target point, and conversely, the attraction force is larger and larger; when the guided vehicle reaches the target position, the attractive force received is 0. The repulsive force potential field function is defined as:
in the above formula, the μ repulsive potential field constant; the minimum distance between the alpha guide vehicle and the barrier; alpha is alphaoThe upper limit of the distance that the barrier can act on the guided vehicle is a constant, and when the distance between the guided vehicle and the barrier exceeds alphaoWhen the repulsive force is 0, the guided vehicle is not acted by the repulsive force any more. It can be seen that the repulsion force is equal to the self potential field negative gradient:
in the above formula, the first and second carbon atoms are,
when the lead vehicle travels in a complex environment with multiple obstacles, the lead vehicle is acted on by the multiple obstacles at this time, and the sum of the potential fields can be expressed as:
U＝Ua+∑Ur
the total force experienced by the lead vehicle is then:
F＝Fa+∑Fr。
the invention has the beneficial effects that:
in the aspect of global path planning, the traditional A-x algorithm has the defects of long path, overlarge turning angle and insufficient smoothness of the path when path planning is carried out. The ant colony optimization method is improved and optimized through the iteration effect of the ant colony algorithm, so that the path is shorter, the turning angle is smaller, the path is smoother when the path planning is carried out, and the path planning effect is improved. Aiming at the local minimum value problem and the target unreachable problem existing in the artificial potential field method in the aspect of local path planning, the artificial potential field method is improved by adopting a mode of correcting a potential field function and introducing escape force, so that a vehicle can smoothly avoid an obstacle to reach a target position when the local path planning is carried out.
The invention also carries out the deep research of the unmanned driving technology, improves the existing A-algorithm and the artificial potential field method, designs the hybrid path planning algorithm by combining the improved A-algorithm and the artificial potential field method, and provides a proper control algorithm for the subsequent vehicle path planning. By adopting a proper path planning algorithm, a feasible path is automatically planned by the system, and the vehicle avoids the obstacle according to the path, so that the vehicle can accurately and quickly reach the target position. Thereby realize thereby the auxiliary driving technique to the popularization of helping hand unmanned driving, greatly reduce the emergence of road traffic accident, make people's mode of going out take place huge change also let out the line and become safer high-efficient.
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FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a diagram of an ant routing model established by the present invention.
Detailed Description
The first embodiment is as follows:
a path planning method based on the improved a-algorithm in this embodiment is implemented by the following steps, as shown in fig. 1: the method comprises the steps of processing an acquired road condition map by a grid method to obtain a grid map; then, optimizing and improving the A-star algorithm by using the ant colony algorithm; then, local path planning is carried out by adopting an artificial potential field method, and improvement and optimization are carried out aiming at the defects that the local minimum value and the target of the artificial potential field method are inaccessible; then, the improved A-algorithm is combined with an artificial potential field method to design a hybrid path planning algorithm, and a proper control algorithm is provided for subsequent vehicle path planning; and then, a feasible path is automatically planned by the system by adopting a proper path planning algorithm, and the vehicle avoids the barrier according to the path, so that the target position is accurately and quickly reached.
The second embodiment is as follows:
different from the specific embodiment, the method for path planning based on the improved a-algorithm in this embodiment combines the improved a-algorithm with the artificial potential field method to design the hybrid path planning algorithm, which specifically includes:
step one, carrying out global path planning by using an improved A-x algorithm to obtain an initial path;
step two, detecting whether a dynamic barrier exists in the map environment;
if so, performing local path planning by adopting an improved artificial potential field method, and performing improvement optimization on the local minimum value and the inaccessible defect of the target;
if not, the path planning is considered to be finished, and the final hybrid algorithm planning path is obtained.
Step three, judging whether a destination point is reached or not after local path planning is carried out by adopting an improved artificial potential field method;
if not, returning to the step two;
if so, the path planning is considered to be completed, and the final hybrid algorithm planning path is obtained.
Wherein the content of the first and second substances,
the path planned by the traditional A-x algorithm of global path planning is longer, the turning angle is larger, the path is not smooth enough, and the requirements can not be met certainly when the actual path planning of the guide vehicle is carried out.
And performing optimization improvement on the A-star algorithm through the iterative optimization performance of the ant colony algorithm. The optimized and improved A-algorithm provides a proper and effective global path planning algorithm for the experiments in the next actual environment.
The third concrete implementation mode:
different from the first or second specific embodiments, in the path planning method based on the improved a-algorithm according to the present embodiment, in the step of performing the local path planning by using the improved artificial potential field method, because the artificial potential field method is used for performing the local path planning, the local minimum and the target may not reach the two defects.
Local minima problem
When the artificial potential field method is used for local path planning, a vehicle is enabled to travel along a path with a lower potential field in the environment under the combined action of the repulsive force of an obstacle and the attractive force of a target point in a potential force field, so that the obstacle is avoided and the target position is reached. However, sometimes, the repulsive force and the attractive force applied to the guided vehicle are 0 after being combined, the guided vehicle does not move any more, the position of the guided vehicle is not the global potential field minimum value point but a local minimum value point, the guided vehicle repeatedly wanders in the current position, and finally the path planning fails.
Problem of unreachable target
When the vehicle carries out path planning by using a local path planning algorithm manual potential field method, the vehicle can avoid an obstacle to reach a target point under the action of the resultant force of attraction and repulsion, and the attraction and repulsion are determined by the relative positions of the obstacle, the target position and a guided vehicle. If an obstacle exists near the target position, and when the obstacle is far away from the target position and the obstacle position, the attraction force is larger, the repulsion force is smaller, and the attraction force is larger than the repulsion force, then the guided vehicle can advance towards the target point under the action of the resultant force. When the distance between the guiding vehicle and the target position is closer to the obstacle, the received attractive force is smaller and larger, and when the repulsive force is larger than the attractive force, the guiding vehicle deviates from the position direction of the target point and leaves the target position, so that the guiding vehicle can not reach the target position forever, and the path planning fails.
Aiming at the defects that the local minimum value and the target can not reach when the artificial potential field method carries out local path planning, the artificial potential field method is improved by correcting the potential field and introducing the escape method. From the previous studies, it is known that the motion of the guided vehicle in the potential force field is mainly generated by the attraction force of the target position to the guided vehicle and the repulsion force of the obstacle to the guided vehicle. The magnitude and direction of the attractive force and the repulsive force applied to the guided vehicle can be improved through the correction of the potential field, so that the direction and the magnitude of the resultant force can be changed by changing the direction magnitude of the stressed force under the condition that the guided vehicle falls into a local minimum value and a target is inaccessible, the defect that the guided vehicle is not capable of overcoming the local minimum value and the target is inaccessible is overcome, and the guided vehicle finally reaches the target position.
The fourth concrete implementation mode:
different from the third specific embodiment, in the path planning method based on the improved a-x algorithm of the present embodiment, the processing of the acquired road condition map by using the grid method to obtain the grid method of the grid map includes:
the grid method is that the space to be planned where the guide vehicle is located is decomposed into independent cells according to the standard that the guide vehicle can move freely in each cell to obtain grids which are connected but not overlapped; it should be noted that the partition precision of the space to be planned directly affects the planning result, the planning precision and the computation amount are inversely proportional to the partition precision, and the coarser the partition, the smaller the computation amount, and the finer the partition, the higher the computation amount. After rasterization, a traffic factor is given to each grid, and the path planning problem of the mobile robot in a grid map is converted into the problem of finding the shortest path between two grid nodes; if no obstacle exists in the grid, the robot can freely pass through the grid, and the grid is called a free grid, and is called an obstacle grid in the opposite direction; generally, the grid method is used as an environment modeling technology when path planning research is carried out, and is not used alone as a path planning algorithm, but is only a method for researching path planning and generally needs to be combined with other algorithms to achieve an ideal effect.
And after the grid identification is finished, the mobile robot searches and displays the path according to the coordinates or the sequence number. The modeling method is simple and easy to implement, is easy to expand to a three-dimensional environment, and has uniqueness in grid identification. The method mainly used for simulation research of the path planning algorithm is a grid method.
The fifth concrete implementation mode:
different from the first, second or fourth embodiments, the method for path planning based on the improved a-algorithm of the present embodiment includes the steps of,
the global path planning mainly comprises three steps: the method comprises the steps of constructing a map, selecting points on the map according to an algorithm, and generating a path according to the points.
The conventional a-algorithm satisfies the following formula:
F(n)＝g(n)+h(n)
in the above formula: f (n) an evaluation function from the initial state to the target state via the state n; g (n) the actual cost of the current node t to the node n; h (n) the estimated cost of the best path from state n to target state p.
The traditional A-x algorithm is simple in principle, convenient to understand and operate, and simple and fast in path searching during path planning. However, the path planned by the traditional a-x algorithm is long, the turning angle is large, the path is not smooth enough, and the requirement cannot be met certainly when the actual path of the guided vehicle is planned. Therefore, certain improvement and optimization are considered to be carried out on the traditional A-x algorithm, and the defects are made up while the advantages of the traditional A-x algorithm are inherited. The ant colony algorithm is a bionic intelligent algorithm provided according to the biological behavior in nature, has iteration characteristics, is often used for path planning, can also be used for carrying out iterative optimization on other algorithms, and belongs to a heuristic algorithm. Each ant during the process of searching for food will often secrete an pheromone for route marking to remind the fellow along the route to find the food. After pheromone secretion, it decays with time, and if fewer ants pass through the route, the pheromone concentration becomes lower, until no ant passes through the route, and the route disappears. On the contrary, more ants pass through the route, the pheromone concentration is higher, more ants are attracted, and a global optimal path is formed.
The method comprises the following steps:
establishing an ant path-finding model; the model is shown in fig. 2;
assume the following steps are satisfied:
τ1＝τ2＝τ3＝C＞0
in the subsequent iteration, the pheromone concentration secreted by the ant colony on different routes is attenuated along with time, so that the pheromone concentration is different, the different routes have different weights due to the change, and according to the change, the following ants have certain basis in routing. Releasing a certain pheromone as a mark when each ant passes through the road section, and establishing a calculation formula of pheromone increment:
in the formula: the total amount of Q pheromones is a fixed value; num ant population number;
ant k (1, 2, 3, … Num) releases the pheromone quantity delta tau from i to jij(t) total pheromone concentration increased over segment ij after t cycles; when the ant colony algorithm is iterated, pheromones on the route can volatilize once at the end time of different periods;
establishing a transition probability formula:
in the formula: the rho pheromone volatilization coefficient is generally between 0 and 1; tau isij(t) the pheromone amount of the cycle; tau isij(t +1) information amount of t + 1; the core of the ant algorithm is that the transfer probability selection behavior of ants is simulated, and the transfer probability is based on informationCalculating element and heuristic function value, obtaining transfer probability through calculation, and selecting path by following ants; wherein the heuristic information is
dijThe length from i to j is the main reason that ants can decide which route to choose according to the size of the ants.
The sixth specific implementation mode:
different from the fifth embodiment, in the path planning method based on the improved a-algorithm of the present embodiment, the local path planning based on the improved artificial potential field method specifically includes:
when path planning is carried out, the path planning problem in a static environment can be effectively solved by adopting the global path planning based on the A-x algorithm, however, various obstacles including some temporary dynamic obstacles can be encountered in actual operation, and the situation that the obstacles are encountered in a local range cannot be effectively solved by the global path planning. The artificial potential field method is simple in structure and widely applied to obstacle avoidance and smoothing of the robot, so that the method considers that the local path planning based on the artificial potential field method is adopted to carry out dynamic obstacle avoidance in a local range. In order to enable the guided vehicle to better avoid the obstacle in the dynamic environment, the problems of unreachable targets and local minimum values in the artificial potential field method are improved, and therefore the local path planning effect is improved. The lead vehicle may encounter an obstacle, which may be a static object or a temporary dynamic object, as it follows an initial route toward the target location. Then, obstacle avoidance of the guided vehicle is needed, and the artificial potential field method has a good effect in application of obstacle avoidance of the guided vehicle due to the advantages of simple algorithm principle, easiness in implementation and the like, and is convenient to study in detail below. So as to provide a good path planning algorithm for the local path planning of the guided vehicle.
The artificial potential field method is a classic algorithm commonly used for path planning of the guided vehicle, when the path planning is carried out, the periphery of the guided vehicle is regarded as a potential field environment, the target point generates an attraction force on the guided vehicle, and the obstacle generates a repulsive force on the guided vehicle, so that the guided vehicle can advance towards the target point under the synthetic action of two kinds of simulation forces; there are many types of definitions of potential field models, different types having different meanings, which also results in different ways in which the lead vehicle is acted upon at obstacles and target points.
In a potential force field, for a certain point in space, it is acted upon by a certain force, not only in magnitude but also in direction, and this force is determined. In the potential force field, the obstacle has a repulsive force on the object and the target point has an attractive force on it. The attraction force is smaller and smaller when the object is closer to the target point, and is larger and larger on the contrary; as the object moves further away from the obstacle, the repulsive force becomes smaller and smaller, and vice versa. According to the principle of the artificial potential field, the guided vehicle can avoid the obstacle to advance towards the target point under the combined action of the repulsive force of the obstacle and the attractive force of the target point. From the superposition of forces, the total potential field function in the environment of the lead vehicle is:
Ut＝Ua+Ur
in the above formula, UaAn attractive force potential field; u shaperA repulsive force potential field; u shapetThe vehicle is in the total potential field of the environment. The resultant force experienced by the vehicle is:
Ft＝Fa+Fr
in the above formula, FaThe gravitational forces experienced by the vehicle; frThe repulsive force to which the vehicle is subjected. Suppose the vehicle coordinates are X ═ (X, y)TThen for gravitational potential field functions can be defined as:
in the above formula, λ gravitational potential field constant; an X guide vehicle position vector; xtThe position of the target in the environment of the lead vehicle, then the attractive force at this time can be represented by a negative gradient of the attractive force potential field function:
Fa＝-grad(Ua)＝-λ(X-Xt)＝λ(Xt-X)
as can be seen from the above formula, the attraction force is smaller and smaller when the guided vehicle gets closer to the target point, and conversely, the attraction force is larger and larger; when the guided vehicle reaches the target position, the attractive force received is 0. The repulsive force potential field function is defined as:
in the above formula, the μ repulsive potential field constant; the minimum distance between the alpha guide vehicle and the barrier; alpha is alphaoThe upper limit of the distance that the barrier can act on the guided vehicle is a constant, and when the distance between the guided vehicle and the barrier exceeds alphaoWhen the repulsive force is 0, the guided vehicle is not acted by the repulsive force any more. It can be seen that the repulsion force is equal to the self potential field negative gradient:
in the above formula, the first and second carbon atoms are,
when the lead vehicle travels in a complex environment with multiple obstacles, the lead vehicle is acted on by the multiple obstacles at this time, and the sum of the potential fields can be expressed as:
U＝Ua+∑Ur
the total force experienced by the lead vehicle is then:
F＝Fa+∑Fr
the artificial potential field method is simple in algorithm structure, easy to program, high in real-time performance when local path planning is carried out, and smooth in the planned path, so that the artificial potential field method is suitable for an obstacle avoidance process when the local path planning is carried out. The method mainly simulates acting force of obstacles and target points in the environment on an object into a potential field, and then carries out path planning under the acting force. The system can achieve a feedback mode with the surrounding environment in the process of path search planning and control, so that the system is strong in adaptability and mature through development and improvement.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A path planning method based on an improved A-algorithm is characterized in that: the method comprises the steps of processing an acquired road condition map by a grid method to obtain a grid map; then, optimizing and improving the A-star algorithm by using the ant colony algorithm; then, local path planning is carried out by adopting an artificial potential field method, and improvement and optimization are carried out aiming at the defects that the local minimum value and the target of the artificial potential field method are inaccessible; then, the improved A-algorithm is combined with an artificial potential field method to design a hybrid path planning algorithm, and a proper control algorithm is provided for subsequent vehicle path planning; and then, a feasible path is automatically planned by the system by adopting a proper path planning algorithm, and the vehicle avoids the obstacle according to the path so as to reach the target position.
2. A path planning method based on the improved a-algorithm according to claim 1, characterized in that: the method for designing the hybrid path planning algorithm by combining the improved A-algorithm with the artificial potential field method specifically comprises the following steps:
step one, carrying out global path planning by using an improved A-x algorithm to obtain an initial path;
step two, detecting whether a dynamic barrier exists in the map environment;
if so, performing local path planning by adopting an improved artificial potential field method, and performing improvement optimization on the local minimum value and the inaccessible defect of the target;
if not, the path planning is considered to be finished, and the final hybrid algorithm planning path is obtained.
Step three, judging whether a destination point is reached or not after local path planning is carried out by adopting an improved artificial potential field method;
if not, returning to the step two;
if so, the path planning is considered to be completed, and the final hybrid algorithm planning path is obtained.
3. A path planning method based on the improved a-algorithm according to claim 2, characterized in that: the process of local path planning by using the improved artificial potential field method comprises the following steps,
the artificial potential field method is improved by a method for correcting potential field and introducing escape force.
4. A method for path planning based on the modified a-algorithm according to claim 3, characterized in that: the process of processing the collected road condition map by adopting a grid method to obtain the grid method of the grid map comprises the following steps:
the grid method is that the space to be planned where the guide vehicle is located is decomposed into independent cells according to the standard that the guide vehicle can move freely in each cell to obtain grids which are connected with each other but are not overlapped; after rasterization, a traffic factor is given to each grid, and the path planning problem of the mobile robot in a grid map is converted into the problem of finding the shortest path between two grid nodes; if no obstacle exists in the grid, the robot can freely pass through the grid, and the grid is called a free grid, and is called an obstacle grid in the opposite direction; and after the grid identification is finished, the mobile robot searches and displays the path according to the coordinates or the sequence number.
5. A method of path planning based on the modified A-algorithm according to claim 1, 2 or 4, characterized in that: the global path planning based on the improved a-algorithm comprises the steps of,
the optimization improvement of the A-algorithm is carried out through the iterative optimization performance of the ant colony algorithm, and the optimization improvement specifically comprises the following steps:
establishing an ant path-finding model;
assume the following steps are satisfied:
τ1＝τ2＝τ3＝C＞0
in subsequent iterations, the following calculation formula for the pheromone increment is established:
in the formula: the total amount of Q pheromones is a fixed value; num ant population number;
ant k, with the value of 1, 2, 3, … Num, the pheromone quantity delta tau released in the process from i to jij(t) total pheromone concentration increased over segment ij after t cycles; when the ant colony algorithm is iterated, pheromones on the route can volatilize once at the end time of different periods;
establishing a transition probability formula:
in the formula: the rho pheromone volatilization coefficient is generally between 0 and 1; tau isij(t) the pheromone amount of the cycle; tau isij(t +1) information amount of t + 1; the core of the ant algorithm is that the transfer probability selection behavior of ants is simulated, the transfer probability is calculated according to pheromones and heuristic function values, the transfer probability is obtained through calculation, and subsequent ants can select paths according to the transfer probability; wherein the heuristic information is
dijIs i toThe length between j.
6. A path planning method based on the improved A-algorithm according to claim 5, characterized in that: the local path planning based on the improved artificial potential field method specifically comprises the following steps:
when path planning is carried out, the periphery of the guided vehicle is regarded as a potential field environment, the target point generates an attraction force to the guided vehicle, and the barrier generates a repulsive force to the guided vehicle, so that the guided vehicle can advance towards the target point under the synthetic action of the two simulated forces; under the combined action of the repulsive force of the obstacle and the attractive force of the target point, the guided vehicle can avoid the obstacle and move towards the target point. From the superposition of forces, the total potential field function in the environment of the lead vehicle is:
Ut＝Ua+Ur
in the above formula, UaAn attractive force potential field; u shaperA repulsive force potential field; u shapetThe vehicle is in the total potential field of the environment. The resultant force experienced by the vehicle is:
Ft＝Fa+Fr
in the above formula, FaThe gravitational forces experienced by the vehicle; frThe repulsive force to which the vehicle is subjected. Suppose the vehicle coordinates are X ═ (X, y)TThen for gravitational potential field functions can be defined as:
in the above formula, λ gravitational potential field constant; an X guide vehicle position vector; xtThe position of the target in the environment of the lead vehicle, then the attractive force at this time can be represented by a negative gradient of the attractive force potential field function:
Fa＝-grad(Ua)＝-λ(X-Xt)＝λ(Xt-X)
as can be seen from the above formula, the attraction force is smaller and smaller when the guided vehicle gets closer to the target point, and conversely, the attraction force is larger and larger; when the guided vehicle reaches the target position, the attractive force received is 0. The repulsive force potential field function is defined as:
in the above formula, the μ repulsive potential field constant; the minimum distance between the alpha guide vehicle and the barrier; alpha is alphaoThe upper limit of the distance that the barrier can act on the guided vehicle is a constant, and when the distance between the guided vehicle and the barrier exceeds alphaoWhen the repulsive force is 0, the guided vehicle is not acted by the repulsive force any more. It can be seen that the repulsion force is equal to the self potential field negative gradient:
in the above formula, the first and second carbon atoms are,
when the lead vehicle travels in a complex environment with multiple obstacles, the lead vehicle is acted on by the multiple obstacles at this time, and the sum of the potential fields can be expressed as:
U＝Ua+∑Ur
the total force experienced by the lead vehicle is then:
F＝Fa+∑Fr。
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