CN112486185A - Path planning method based on ant colony and VO algorithm in unknown environment - Google Patents
Path planning method based on ant colony and VO algorithm in unknown environment Download PDFInfo
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Abstract
The invention discloses a path planning method based on ant colony and VO algorithm in an unknown environment, thereby realizing the avoidance of dynamic obstacles and static obstacles by an intelligent agent in the unknown environment. Firstly, an initial map matrix is constructed by rasterizing known map information, a global path under a known environment is constructed by adopting an ant colony algorithm, and a path planning strategy is specifically divided into two parts aiming at the problems that the map environment is unknown and other static obstacles and dynamic obstacles may exist. And for unknown static obstacles, when the intelligent agent detects the static obstacle information in the movement process of the intelligent agent, updating the global map matrix, and reconstructing a global path through an ant colony algorithm. For the dynamic barrier, a corresponding penalty function formula is designed by combining a VO algorithm, and the optimal speed is selected from the speed candidate set, so that the effects of avoiding the dynamic barrier and tracking the path are achieved. Experimental results show that the path planning method provided by the invention can effectively avoid dynamic obstacles and static obstacles in unknown environments.
Description
The technical field is as follows:
the invention relates to a path planning method based on ant colony and VO algorithm in an unknown environment, which can realize real-time path planning under the condition that unknown static obstacles and dynamic obstacles exist in the environment and belongs to the technical field of intelligent optimization algorithm.
Background art:
the ant colony algorithm is a heuristic search algorithm, which was first proposed by Dorigo in 1991. The algorithm simulates the foraging behavior of ants in nature. The ants in the ant colony leave pheromones on the traveling path of the ants, the following ants also select the advancing direction according to the concentration of the pheromones on the path, and the ant colony finally finds a foraging path through the accumulation of the pheromones on the path.
Social animal clustering often results in dramatic self-organizing behavior, as ants whose individual behavior appears simple and blind can find the shortest path from the nest to the food source after forming a colony. Biologists have conducted intensive research to find the shortest path between ants through indirect communication and cooperation of a substance called "pheromone". Inspired by this phenomenon, italian scholars m.Dorigo, V.Maniezzo and A.Colorni propose a population-based simulated evolutionary algorithm, the ant colony algorithm, by simulating the foraging behavior of the ant colony. The emergence of the algorithm arouses the great attention of scholars, and in the past short time of more than twenty years, the ant colony algorithm has been widely applied in the fields of combination optimization, function optimization, system identification, network routing, robot path planning, data mining, comprehensive wiring design of large-scale integrated circuits and the like, and has achieved better effect.
Much research work is done on the shortcomings of the ant colony algorithm by MarcoDorigo, ThomasStutzle and the like, and various improved strategies such as an elite ant colony optimization algorithm, a maximum minimum ant system and the like are provided for solving the optimization problems of different characteristics in different fields more effectively [1] (see Maniezzov, GambardellaLM, LuigiFD, Antolonylation, New Optimization technique engineering [ M ]. Springer Berlin Heidelberg,2004: 422-; the correction ant colony Algorithm (ACS) is an ant colony algorithm adopting a strategy of updating local pheromones, and can improve the probability of selecting an unaccessed path and strengthen the global search capability of the algorithm; applying a spatial and global pheromone update strategy to enhance the concentration of pheromones on the obtained locally optimal path so as to enhance the positive feedback effect of the algorithm and accelerate the convergence speed of the algorithm [2] (see Dorigo M, Gambardella L M. Ant color system: A cooperative learning gain process to the converging sampling plan [ J ]. IEEE transfer evolution, 1997,1(1): 53-56.); aiming at the continuous domain problem, in order to improve the capability of searching the global optimal solution and convergence speed and balance the convergence speed and the convergence speed, an improved ant colony algorithm [3] (see Niniaouli, Kurazavia, Suzuzhi) of a solution updating mode for adaptively adjusting pheromone volatilization and an information sharing mechanism is provided; zhangyi et al propose an improved algorithm that utilizes the stronger global search capability of genetic algorithms and a feedback mechanism in conjunction with ant colony algorithms. The method comprises the steps that a genetic algorithm can be used for carrying out cross and variation capacity, cross variation operation is carried out on ant populations under the condition that a certain condition is met to obtain a new population, the new population is used as an initial population of the ant population algorithm to carry out fine estimation on the state of a power distribution network, and the state of the power distribution network can be more accurately reflected [4] (see Zhang, Wang stand bin, Power distribution network state estimation of the genetic-ant population algorithm [ J ]. modern electronic technology, 2016,39(19): 165-168); the method reasonably improves the path selection strategy of the ant colony algorithm in the optimizing process, is favorable for reducing the possibility that the ant colony algorithm is easy to fall into local optimization, and is favorable for improving the performance of the algorithm.
The invention content is as follows:
in order to guarantee the safety and reliability of the movement of the intelligent agent on the planned path in the unknown environment, the invention provides a path planning design method based on an ant colony algorithm and a VO algorithm, so that the real-time path planning is safely and reliably performed under the conditions that unknown dynamic static obstacles exist in the environment and dynamic obstacles exist. The method utilizes a simulated ant colony foraging strategy, finds out a reliable path by leaving pheromones in a map environment, and carries out a new round of path planning when a new static barrier is found in a detection radius by maintaining a global known map matrix, thereby reducing the operation cost. Meanwhile, by combining the VO obstacle avoidance strategy, the dynamic obstacles are effectively avoided while the path planning is completed, and the moving route of the intelligent agent is optimized by designing the path tracking strategy, so that the moving route is smoother, and the problem of a possible circuitous route in the ant colony algorithm is solved.
In order to achieve the purpose, the technical scheme of the invention is as follows: the method for planning the path based on the ant colony algorithm and the VO algorithm in the unknown environment comprises the following steps:
step 1, an initialization matrix Map is constructed according to known Map information; wherein, the area value of the static obstacle is 1, and the other area values are 0;
step 2, calculating a global path pathList by using an ant colony algorithm;
step 3, after obtaining the global path, the intelligent agent tracks the path according to the tracking strategy; setting a serial number for each path point, and recording the path points on the currently tracked global path through the trackId;
step 4, finding out the moving speed of the intelligent agent at the next moment according to the VO algorithm;
step 6, if no new static obstacle appears in the detection radius, turning to step 3; if a new static obstacle appears, updating the Map, turning to the step 2, and performing a new round of path planning;
further, the specific construction method of the global path from the starting point to the end point in the step 2 is as follows:
step 2-1, setting ant colony algorithm iteration times G, ant number n and pheromone volatilization coefficient rho parameter information;
step 2-2, constructing an pheromone concentration matrix pheromone Map with the same size according to the size of the matrix Map, setting the initial value of the pheromone concentration matrix pheromone Map to be 1, and constructing a temporary pheromone storage matrix temppheromone Map with the same size, and setting the initial value of the temporary pheromone storage matrix temppheromone Map to be 0;
step 2-3, placing n ants at the starting point, sequentially searching paths of ants with serial numbers from 0 to n-1, and recording the path selected by each ant;
2-4, constructing a taboo list recordMap with the same size according to the size of the matrix Map, and recording the positions where ants walk;
step 2-5, calculating an availableDirection of the selectable direction set according to the Map and recordMap information; selecting the optimal direction from the availableDirection set as the moving direction according to a state transition probability formula; if the availableDirection set is empty, that is, there is no direction to go, then turn to step 2-3, start the path planning process of the next ant;
wherein ,
is the probability that the kth ant transfers from node i to node j; tau isij(t) represents the pheromone concentration on the path from node i to node j; etaiIs a function of the heuristic function,
direpresenting the distance from the node i to the target point; alpha and beta represent the relative importance degree of pheromone concentration and heuristic information respectively; allowedkIs the set of nodes that ants can select in the next step;
meanwhile, in order to accelerate the convergence rate of the algorithm, the following state transition mode is adopted, wherein q represents a random number between 1 and 100, and q represents1Is a constant with the value between 1 and 100, and m represents that the next node is selected by adopting a roulette mode;
step 2-6, if the current position and the final position of the ant are the same, calculating an pheromone value according to the record path and an pheromone calculation formula, and storing corresponding pheromones in a temporary pheromone storage matrix, wherein the storage value of the temporary pheromone storage matrix is the sum of the pheromones left on the path by all ants in one iteration; otherwise, executing the step 2-5;
step 2-7, at the moment, n ants finish the path searching process, and the pheromone concentration matrix is updated according to the temporary pheromone storage matrix; if the iteration times are reached, executing the step 3; otherwise, executing the step 2-3;
τij(t+Δt)＝(1-ρ)τij(t)+Δτij(t)
step 2-8, placing a path-finding ant at the starting point, selecting the moving direction according to the concentration of the pheromone, and recording the moving route, wherein the route is the global path pathList;
further, step 3, the specific design of the agent for performing path tracking according to the tracking policy is as follows:
step 3-1, if the trackId is equal to the maximum serial number on the path, the task is completed;
step 3-2, if the distance between the agent and the path point is less than dmin, the trackId is increased progressively; if the distance between the agent and the path point is larger than dmax, resetting the path point closest to the agent as a tracking path point;
3-3, setting an agent tracking path point target according to the trackId;
further, in step 4, the step of the movement speed of the agent at the next moment specifically includes:
step 4-1, randomly initializing a candidate speed set and setting a safety factor parameter;
4-2, selecting a candidate speed from the set, calculating the collision time of the intelligent agent with all dynamic obstacles and all static obstacles according to the candidate speed, and recording the minimum collision time t; if the intelligent agent does not collide with all the obstacles, the collision time t is infinite; calculating a penalty value according to a penalty function;
wherein mu is a safety factor, VpreFor ideal speed, tar is the tracking path point position, position is the agent position, | | Vmax| is the agent maximum speed;
4-3, if the speed candidate set is traversed, selecting the speed candidate with the minimum penalty value as the speed of the intelligent agent at the next moment; if the speed candidate set is not traversed, turning to the step 4;
compared with the prior art, the invention has the following advantages: the invention discloses a path planning design method based on an ant colony algorithm and a VO algorithm, so that real-time path planning can be safely and reliably carried out under the conditions that unknown dynamic static obstacles exist and dynamic obstacles exist in the environment. The method utilizes a simulated ant colony foraging strategy, finds out a reliable path by leaving pheromones in a map environment, and carries out a new round of path planning when a new static barrier is found in a detection radius by maintaining a global known map matrix, thereby reducing the operation cost. Meanwhile, by combining the VO obstacle avoidance strategy, the dynamic obstacles are effectively avoided while the path planning is completed, and the moving route of the intelligent agent is optimized by designing the path tracking strategy, so that the moving route is smoother, and the problem of a possible circuitous route in the ant colony algorithm is solved.
Drawings
FIG. 1 is a schematic flow chart of the ant colony and VO algorithm-based path planning method in an unknown environment according to the present invention;
FIG. 2 is a routing diagram of a known environment of an agent simulated by QT according to the present invention, in which unknown static obstacles and unknown dynamic obstacles are present;
FIG. 3(a) is a routing diagram of the known environment of the intelligent agent constructed by matlab of the invention, and FIG. 3(b) is an environment diagram of the actual existence of unknown obstacles;
FIG. 4(a) is a diagram of a smart body quadratic programming path simulated by QT simulation of the present invention, and FIG. 4(b) is a partially enlarged diagram of the smart body quadratic programming path;
FIG. 5 is a comparison graph of the intelligent agent quadratic programming path constructed using matlab according to the present invention;
FIG. 6(a) is a schematic diagram of the motion behavior of an agent simulated by QT simulation according to the invention when the agent is predicted to generate a collision when encountering an unknown dynamic obstacle, and FIG. 6(b) is a schematic diagram of the motion behavior of the agent when the agent is predicted to pass when encountering an unknown dynamic obstacle;
Detailed Description
Example 1: the objects, technical solutions and advantages of the present invention will be described in further detail with reference to the accompanying drawings.
At present, the application of the intelligent agent is more and more extensive, and the requirements on the safety and the reliability of the path planning of the intelligent agent are higher and higher. In a traditional classical path planning algorithm, all environment information is required to be acquired. However, in an actual operating environment, unknown static obstacles and unknown dynamic obstacles often exist, which puts higher requirements on path planning of the intelligent agent. Therefore, the problem of unknown obstacles in the operating environment must be overcome to research a reliable, safe and efficient path planning algorithm.
Based on the consideration, the method firstly utilizes a simulated ant colony foraging strategy, finds out a reliable path by leaving pheromones in a map environment, and carries out a new round of path planning when a new static barrier is found in a detection radius by maintaining a global known map matrix, thereby reducing the operation cost. Meanwhile, by combining the VO obstacle avoidance strategy, the dynamic obstacles are effectively avoided while the path planning is completed, and the moving route of the intelligent agent is optimized by designing the path tracking strategy, so that the moving route is smoother, and the problem of a possible circuitous route in the ant colony algorithm is solved.
Fig. 1 shows a path planning method based on ant colony and VO algorithm in an unknown environment, which is specifically implemented as follows:
step 1, constructing an initialization matrix Map according to known Map information, wherein the area value of a static obstacle is 1, and the other area values are 0;
step 2, calculating a global path pathList by using an ant colony algorithm;
the step 2 comprises the following steps:
step 2-1, setting ant colony algorithm iteration times G, ant number n and pheromone volatilization coefficient rho parameter information;
step 2-2, constructing a pheromone concentration matrix pheromoneMap with the same size according to the size of the matrix Map, and setting the initial value of the pheromone concentration matrix pheromoneMap to be 1; constructing a temporary pheromone storage matrix tempPheromoneMap with the same size, and setting the initial value of the temporary pheromone storage matrix tempPheromoneMap as 0;
step 2-3, placing n ants at the starting point, sequentially searching paths of ants with serial numbers from 0 to n-1, and recording the path selected by each ant;
2-4, constructing a taboo list recordMap with the same size according to the size of the matrix Map, and recording the positions where ants walk;
step 2-5, calculating an availableDirection of the selectable direction set according to the Map and recordMap information; selecting the optimal direction from the availableDirection set as the moving direction according to a state transition probability formula; if the availableDirection set is empty, that is, there is no direction to go, then turn to step 2-3, start the path planning process of the next ant;
wherein ,
is the probability that the kth ant transfers from node i to node j; tau isij(t) represents the pheromone concentration on the path from node i to node j; etaiIs a function of the heuristic function,
direpresenting the distance from the node i to the target point; alpha and beta represent pheromone concentration and elicitation, respectivelyThe relative importance of the information; allowedkIs the set of nodes that ants can select in the next step;
meanwhile, in order to accelerate the convergence rate of the algorithm, the following state transition mode is adopted, wherein q represents a random number between 1 and 100, and q represents1Is a constant with the value between 1 and 100, and m represents that the next node is selected by adopting a roulette mode;
step 2-6, if the current position and the final position of the ant are the same, calculating an pheromone value according to the record path and an pheromone calculation formula, and storing corresponding pheromones in a temporary pheromone storage matrix, wherein the storage value of the temporary pheromone storage matrix is the sum of the pheromones left on the path by all ants in one iteration; otherwise, executing the step 2-5;
step 2-7, at the moment, n ants finish the path searching process, and the pheromone concentration matrix is updated according to the temporary pheromone storage matrix; if the iteration times are reached, executing the step 3; otherwise, executing the step 2-3;
τij(t+Δt)＝(1-ρ)τij(t)+Δτij(t)
step 2-8, placing a path-finding ant at the starting point, selecting the moving direction according to the concentration of the pheromone, and recording the moving route, wherein the route is the global path pathList;
step 3, after obtaining the global path, the intelligent agent tracks the path according to the tracking strategy; setting a serial number for each path point, and recording the path points on the currently tracked global path through the trackId;
the step 3 comprises the following steps:
step 3-1, if the trackId is equal to the maximum serial number on the path, the task is completed;
step 3-2, if the distance between the agent and the path point is less than dmin, the trackId is increased progressively, so that some knotted paths can be avoided, and the movement route of the agent can be smoother; if the distance between the intelligent agent and the path point is larger than dmax, the path point closest to the intelligent agent is reset as a tracking path point, so that path point tracking can be carried out again after the dynamic obstacle is avoided and deviates from the tracking path point, and the original position is not required to be returned;
3-3, setting an agent tracking path point target according to the trackId;
step 4, finding out the moving speed of the intelligent agent at the next moment according to the VO algorithm;
the step 4 comprises the following steps:
step 4-1, randomly initializing a candidate speed set and setting a safety factor parameter;
4-2, selecting a candidate speed from the set, calculating the collision time of the intelligent body and all dynamic obstacles and static obstacles according to the speed, and recording the minimum collision time t, wherein if the intelligent body and all the obstacles have no collision, the collision time t is infinite; calculating a penalty value according to a penalty function;
wherein mu is a safety factor, VpreFor ideal speed, tar is the tracking path point position, position is the agent position, | | Vmax| is the agent maximum speed;
4-3, if the speed candidate set is traversed, selecting the speed candidate with the minimum penalty value as the speed of the intelligent agent at the next moment; if the speed candidate set is not traversed, turning to the step 4;
step 6, if no new static obstacle appears in the detection radius, turning to step 3; if a new static obstacle appears, updating the Map, turning to the step 2, and performing a new round of path planning;
the following is the simulation verification of the ant colony and VO algorithm-based path planning method designed by the invention in an unknown environment.
In order to prove the feasibility and the effectiveness of the path planning method based on the ant colony algorithm and the VO algorithm in the unknown environment, the method carries out the intelligent agent path planning simulation experiment through QT. By setting a map environment, the situation that unknown static obstacles and dynamic obstacles exist is simulated. The information of each parameter of the algorithm in the experiment is listed in table 1, and the experiment results are shown in fig. 2-6.
TABLE 1 parameter settings for the algorithm
As can be seen from fig. 2 to 6, in an environment where unknown static obstacles and unknown dynamic obstacles exist, the ant colony algorithm and VO algorithm-based path planning method performs well, and can perform real-time path planning quickly and efficiently and avoid the dynamic and static obstacles safely and reliably.
Through comprehensive simulation experiments, the ant colony algorithm and VO algorithm-based path planning method can rapidly and efficiently plan a real-time path and safely and reliably avoid obstacles under the condition that unknown static obstacles and unknown dynamic obstacles exist.
The invention provides a path planning method based on an ant colony algorithm and a VO algorithm, which is characterized in that after a map is rasterized, a reliable path is found out by utilizing a simulated ant colony foraging strategy and reserving pheromones in a map environment, a new path planning is carried out by maintaining a global known map matrix when a new static obstacle is found in a detection radius, and the operation cost is reduced. Meanwhile, by combining the VO obstacle avoidance strategy, the dynamic obstacles are effectively avoided while the path planning is completed, and the moving route of the intelligent agent is optimized by designing the path tracking strategy, so that the moving route is smoother, and the problem of a possible circuitous route in the ant colony algorithm is solved.
It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Further, the above definitions of the various elements and methods are not limited to the various specific structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by those of ordinary skill in the art.
The above is only a specific embodiment of the present invention, it should be noted that the above embodiment does not limit the present invention, and various changes and modifications can be made by workers within the scope of the technical idea of the present invention, and the protection scope of the present invention is covered.
Claims (4)
1. A path planning design method based on ant colony and VO algorithm under unknown environment is characterized by comprising the following steps:
step 1, an initialization matrix Map is constructed according to known Map information; wherein, the area value of the static obstacle is 1, and the other area values are 0;
step 2, calculating a global path pathList by using an ant colony algorithm;
step 3, after obtaining the global path, the intelligent agent tracks the path according to the tracking strategy; setting a serial number for each path point, and recording the path points on the currently tracked global path through the trackId;
step 4, finding out the moving speed of the intelligent agent at the next moment according to the VO algorithm;
step 5, moving the intelligent agent according to the speed calculated in the step 4;
step 6, if no new static obstacle appears in the detection radius, turning to step 3; and if a new static obstacle appears, updating the Map, turning to the step 2, and performing a new round of path planning.
2. The ant colony and VO algorithm-based path planning and design method in the unknown environment as claimed in claim 1, wherein the specific construction method of the global path from the starting point to the end point in step 2 is as follows:
step 2-1, setting ant colony algorithm iteration times G, ant number n and pheromone volatilization coefficient rho parameter information;
step 2-2, constructing an pheromone concentration matrix pheromone Map with the same size according to the size of the matrix Map, setting the initial value of the pheromone concentration matrix pheromone Map to be 1, and constructing a temporary pheromone storage matrix temppheromone Map with the same size, and setting the initial value of the temporary pheromone storage matrix temppheromone Map to be 0;
step 2-3, placing n ants at the starting point, sequentially searching paths of ants with serial numbers from 0 to n-1, and recording the path selected by each ant;
2-4, constructing a taboo list recordMap with the same size according to the size of the matrix Map, and recording the positions where ants walk;
step 2-5, calculating an availableDirection of the selectable direction set according to the Map and recordMap information; selecting the optimal direction from the availableDirection set as the moving direction according to a state transition probability formula; if the availableDirection set is empty, that is, there is no direction to go, then turn to step 2-3, start the path planning process of the next ant;
wherein ,
is the probability that the kth ant transfers from node i to node j; tau isij(t) represents the pheromone concentration on the path from node i to node j; etaiIs a function of the heuristic function,
direpresenting nodesi distance to target point; alpha and beta represent the relative importance degree of pheromone concentration and heuristic information respectively; allowedkIs the set of nodes that ants can select in the next step;
meanwhile, in order to accelerate the convergence rate of the algorithm, the following state transition mode is adopted, wherein q represents a random number between 1 and 100, and q represents1Is a constant with the value between 1 and 100, and m represents that the next node is selected by adopting a roulette mode;
step 2-6, if the current position and the final position of the ant are the same, calculating an pheromone value according to the record path and an pheromone calculation formula, and storing corresponding pheromones in a temporary pheromone storage matrix, wherein the storage value of the temporary pheromone storage matrix is the sum of the pheromones left on the path by all ants in one iteration; otherwise, executing the step 2-5;
step 2-7, at the moment, n ants finish the path searching process, and the pheromone concentration matrix is updated according to the temporary pheromone storage matrix; if the iteration times are reached, executing the step 3; otherwise, executing the step 2-3;
τij(t+Δt)＝(1-ρ)τij(t)+Δτij(t)
and 2-8, placing a path-finding ant at the starting point, selecting the moving direction according to the concentration of the pheromone, and recording the moving path of the path, wherein the path is the global path pathList.
3. The ant colony and VO algorithm-based path planning and design method in an unknown environment as claimed in claim 1, wherein step 3 the specific design of the agent for path tracing according to the tracing strategy is:
step 3-1, if the trackId is equal to the maximum serial number on the path, the task is completed;
step 3-2, if the distance between the agent and the path point is less than dmin, the trackId is increased progressively; if the distance between the agent and the path point is larger than dmax, resetting the path point closest to the agent as a tracking path point;
and 3-3, setting an agent tracking path point target according to the trackId.
4. The ant colony and VO algorithm-based path planning and design method in the unknown environment as claimed in claim 1, wherein in step 4, the step of the movement speed of the agent at the next moment specifically comprises:
step 4-1, randomly initializing a candidate speed set and setting a safety factor parameter;
4-2, selecting a candidate speed from the set, calculating the collision time of the intelligent agent with all dynamic obstacles and all static obstacles according to the candidate speed, and recording the minimum collision time t; if the intelligent agent does not collide with all the obstacles, the collision time t is infinite; calculating a penalty value according to a penalty function;
wherein mu is a safety factor, VpreFor ideal speed, tar is the tracking path point position, position is the agent position, | | Vmax| is the agent maximum speed;
4-3, if the speed candidate set is traversed, selecting the speed candidate with the minimum penalty value as the speed of the intelligent agent at the next moment; and if the speed candidate set is not traversed, turning to the step 4.
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