CN112925315A - Crawler path planning method based on improved ant colony algorithm and A-star algorithm - Google Patents
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Abstract
The invention provides a crawler path planning method based on an improved ant colony algorithm and an A-star algorithm, which comprises the following steps: acquiring position information of obstacles around a vehicle body, performing grid segmentation on a planning area, marking the obstacles and a passable area, and constructing a grid map; parameters such as ant colony algorithm related parameters, iteration times, a starting point and a terminal point of the tracked vehicle and the like are set; completing path planning of the tracked vehicle under the action of an optimized ant colony algorithm, calculating the time consumption of each path by combining a turning time cost function of the tracked vehicle, and selecting the optimal path of each iteration; and obtaining the optimal path with the shortest time consumption after the maximum iteration times are reached. The method optimizes the initial pheromone concentration distribution of the ant colony algorithm, and can improve the initial search efficiency, thereby greatly improving the time of path planning. Meanwhile, in order to better improve the path planning efficiency and shorten the path planning time, the method optimizes the heuristic function of the ant colony algorithm, and is more suitable for path planning of the tracked vehicle.
Description
Technical Field
The invention belongs to the technical field of industrial control, and particularly relates to a crawler path planning method based on an improved ant colony algorithm and an A-star algorithm.
Background
With the progress and development of science, the research of robots has been one of the hot research problems in artificial intelligence, wherein the research of robot path planning is a classical problem in robotics, and the path planning of robots is that the selection of paths by robots is a reasonable decision.
At present, scholars at home and abroad have some classical path planning algorithms in the aspect of path planning, such as the path planning algorithm: dijkstra algorithm, genetic algorithm, ant colony algorithm and the like find an optimal collision-free path from a starting point to an end point in a complex environment. The A-algorithm has the disadvantages that along with the increase of the search range, the calculation amount is increased geometrically, the method has the advantages of quick response to the environment, direct searched path, simple and efficient heuristic function calculation mode and reference. The ant colony algorithm has the advantages of strong robustness, good effect on obstacle avoidance in a dynamic environment, and long convergence time and easy trapping in a local optimal solution. The crawler-type unmanned vehicle yard is applied to special application scenes such as disaster-resistant rescue, space operation, combat collection, tunnel inspection and the like, and the shortest path planning distance is taken as a target in the scenes. However, the damage to the track due to the large turn is large and the turning time of the tracked vehicle is long, and the tracked vehicle path planning cannot be ignored.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problems that the traditional ant colony algorithm has more iteration times and low convergence speed and is easy to fall into the situation of local optimum when the global path planning is carried out, the invention aims to provide the tracked vehicle path planning method based on the improved ant colony algorithm and the A-X algorithm, so that the iteration times of the path planning algorithm of the tracked vehicle are less, the convergence speed is accelerated, the turning time of the tracked vehicle is considered, and the optimum path with the shortest time consumption is finally obtained.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme:
a crawler path planning method based on an improved ant colony algorithm and an A-star algorithm comprises the following steps:
(1) acquiring position information of obstacles around a vehicle body, performing grid segmentation on a planning area, marking the obstacles and accessible areas on the grid according to the position information of the obstacles, and constructing a grid map;
(2) setting initial parameters including the number of ants, pheromone elicitation factors, initial pheromone concentration, expected elicitation factors, pheromone volatilization constants, maximum iteration times, and a starting point S and an end point E of the tracked vehicle;
(3) placing ants at the position of the starting point S, and adding the position of the starting point S into a taboo table;
(4) planning the path of the tracked vehicle under the action of an optimized ant colony algorithm, wherein for each time t, the probability of selecting the next node j from the current node i by the ant k at the time t is calculated
And go to the next node; when the ant k reaches the next node j, adding the node i into the taboo table; wherein the probability
The adopted heuristic function is optimized based on the self-adaptive proportion in the A-x algorithm, when the ant k selects the next node each time, the distance between the current node and the end point E is estimated, and the node close to the end point E is endowed with a larger weight;
(5) judging whether the ants reach the end point E, if so, calculating the path length according to the path nodes in the tabu table, and otherwise, continuously searching the next node until the end point E is found; circulating all ants until all ants are traversed;
(6) calculating the time consumption of each path by combining a turning time cost function of the tracked vehicle, and selecting the optimal path of each iteration;
(7) judging whether the current iteration times reach the maximum iteration times, if so, outputting a path with the shortest consumed time by combining the turning cost of the tracked vehicle; otherwise, re-planning the path until reaching the maximum iteration times and outputting the path with the shortest time consumption.
Preferably, in the step (4), the probability that the ant k selects the next node j at the current node i at the time t
Calculated using the following formula:
where allowed (k) is the set of nodes around the current node i of ant k that can be selected, Tij(t) represents pheromone content of ant k from node i to node j at time t, etaij(t) is the optimized heuristic function, which is the product of the heuristic weight and the heuristic function strength, α is the pheromone heuristic factor, and β is the expected heuristic factor.
Preferably, the heuristic function divides the weights by the distance between the node and the end point E, and distributes the weights proportionally.
Preferably, the number of the nodes which can be selected around the ant is eight, and the weight ratio of the heuristic weight which is set from far to near to the end point is 1:2: 3:4: 5.
Further, the initial pheromone distribution is optimized in the step (4) as follows:
wherein λ isij(0) Representing the initial pheromone concentration from node i to node j, d (j, E) representing the distance from node j to end point E, xj、yjIs the abscissa and ordinate, x, of the node j in the grid mapE、yEIs the abscissa and ordinate of the end point E in the grid map, and C is a constant.
Preferably, the turning time cost function of the tracked vehicle is:
wherein, t2The turning time of the tracked vehicle is s, n1Is the rotating speed of the driving wheel at the outer side of the turning of the tracked vehicle, and the unit is r/min, n2The unit of the rotating speed of a driving wheel on the inner side of the turning of the tracked vehicle is r/min, T is the radius of a driving wheel, the unit is m, B is the center distance between the two side tracks, the unit is m, G is the transmission efficiency of the tracked vehicle, and theta is the steering angle.
Has the advantages that: the invention has the advantages that: aiming at the problem that the convergence speed of the algorithm is reduced due to the randomness of the early search work of the ant colony algorithm, the method adopts the strategies of optimizing pheromone distribution and optimizing heuristic functions, and reduces the blindness of the initial path planning of the algorithm and the efficiency of the path planning. Aiming at a special application scene of the tracked vehicle, the method aims to minimize the time consumed by path planning, and provides a calculation mode capable of calculating the turning time cost function of the tracked vehicle, so that the improved ant colony algorithm is more suitable for the application scene of the path planning of the tracked vehicle.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
FIG. 2 is a grid map of the environment surrounding a tracked vehicle body in an embodiment of the present invention.
FIG. 3 is a schematic view of analysis of the turning motion of a crawler in an embodiment of the present invention
Fig. 4 is a schematic diagram of a node selection direction of the improved ant colony algorithm in the embodiment of the present invention.
Fig. 5 is a diagram of an example output best path of a tracked vehicle according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
As shown in fig. 1, a method for planning a track vehicle path based on an improved ant colony algorithm and an a-x algorithm disclosed in the embodiments of the present invention mainly includes the following steps:
the method comprises the following steps: and constructing a grid map according to the acquired laser radar data. In the step, the position information of obstacles around the vehicle body is acquired by using a vehicle-mounted laser radar sensor, the position information of the obstacles acquired by the vehicle-mounted laser radar and the accessible area are subjected to data processing, a planning area is divided into 20 multiplied by 20 grid units, coordinate axes are marked from left to right and from bottom to top, the grid units are set to be distances with the same unit length, black represents the obstacles in the vehicle body environment, and white represents the accessible area of the vehicle. When the obstacle is not full of one cell, the obstacle is filled in one cell, and a grid map is constructed, as shown in fig. 2. The positioning of the tracked vehicle is realized by utilizing the vehicle-mounted GPS.
Suppose the coordinates of the tracked vehicle are (N)x，Ny) Then, the sequence coding of the tracked vehicle in the grid map is obtained by the following formula:
where Num is a sequence number, x represents an abscissa of a current node in the grid map, and y represents an ordinate of the current node in the grid map.
Step two: setting initial parameters on the basis of the first step: taking 50 ants with the number m; taking 10 as pheromone elicitation factor alpha and initial pheromone concentration C; taking 10 as an expected elicitation factor beta; taking the pheromone volatilization constant rho to be 0.7; the maximum number of iterations is nmaxTake 100 times. Initializing global pheromones, setting parameters such as a starting point S and an end point E of the crawler and the like, wherein the current iteration number n, the strength of the heuristic function is epsilon, and the pheromone concentration is lambda.
Step three: m ants are placed at the position of the starting point S, and the position of the starting point S is added into a Tabu table.
Step four: planning the path of the tracked vehicle under the action of an optimized ant colony algorithm, wherein for each time t, the probability of selecting the next node j from the current node i by the ant k at the time t is calculated
And go to the next node; when the ant k reaches the next node j, the node i is added to the Tabu. Wherein the probability that the current ant k selects the next node j at the current node i
Can be calculated using the following formula:
in the formula, Tij(t) represents the pheromone content of the current kth ant from the node i to the node j at the time t, allowed (k) is a set of nodes which can be selected around the current node i of the ant k, eight nodes are available for selection, and the specific selection mode is shown in fig. 4.α is the pheromone elicitor, β is the desired elicitor, ηij(t) is the optimization heuristic of the present invention.
In step four, there are two aspects to the improvement of the traditional ant colony algorithm:
(1) optimizing a heuristic function ηij(t) adopting a self-adaptive proportion based on an A-x algorithm, wherein the heuristic weight is related to the distance, the smaller the distance is, the larger the heuristic weight is, the direction between the current node and the terminal E can be estimated every time the ant k selects the next node, the grid close to the terminal E is endowed with a larger weight, and otherwise, the smaller weight is endowed, and the ant k selects the next node and selects the node with a larger weight. For example, the end point E is arranged at the right lower part of the current ant k grid, the optimization heuristic weight is set to be 1:2: 3:4: 5, the strength of the heuristic function is epsilon, and the heuristic functions of eight grids around the current node are as follows:
when inspiring function etaij(t) heuristic function η when weights are divided according to the distance from end point EijAnd (t) is distributed in proportion, so that the method is more reasonable and the global planning of the algorithm is better. In the above formula, the coordinate of the current node i is (x)i，yi) The upper node of the node is (x)i，yi+1), lower node (x)i，yi-1), left node is (x)i-1，yi) And the right node is (x)i+1，yi) The upper left node is (x)i-1，yi+1), the lower left node is (x)i-1，yi-1), the upper right node is (x)i+1，yi+1), the lower right node being (xi +1, y)i-1), as shown in particular in figure 4.
(2) In the traditional ant colony algorithm, the initial information concentration is uniformly distributed, namely lambdaij(0) C is a constant, typically 10, so that the initial search direction has random uncertainty, resulting in a waste of a lot of ant resources. The invention optimizes the initial pheromone distribution.
Specifically, the pheromone distribution is optimized as follows:
wherein d (j, E) represents the distance between the node j and the end point E, and xj、yjIs the abscissa and ordinate, x, of the node j in the grid mapE、yEIs the horizontal and vertical coordinates of the end point E in the grid map. The closer the distance between d (j, E) and the end point E, the higher the pheromone concentration, and the increasing mode is adopted. The optimized pheromone distribution can accelerate the convergence of the ant colony algorithmSpeed.
Step five: and judging whether the ants reach the end point E, if so, calculating the path length according to the path nodes in the Tabu, and otherwise, continuously searching the next node until the end point E is found. And circulating all ants in the generation until all ants are traversed to complete.
Step six: and calculating the time consumption of each path by combining the turning time cost function of the tracked vehicle, and selecting the optimal path of each iteration. In the step, barrier information and self state information are acquired aiming at a sensor of the tracked vehicle, and a turning time cost function of the tracked vehicle is calculated according to initial speeds of the tracks on two sides of the tracked vehicle, the distance between the two sides, the radius of the driving wheel and other parameters. The calculation method of the turning time cost function of the tracked vehicle is as follows:
as shown in FIG. 3, let us say the turning time t of the crawler2Steering angle theta, turning outside track speed v1In m/s, steering angular velocity ω1In units of rad/s, the track speed on the inside of a turn is v2In m/s, steering angular velocity ω2Unit rad/s, speed v of the vehicle centroid, unit being m/s, unit rad/s, B being the two-sided track center-to-center spacing, unit m, drive wheel radius being R, unit m, the turning radius of the vehicle being R, obtainable by the kinematics principle:
the speed of the turning track is:
the principle of the kinematics of the tracked vehicle can be used as follows:
v is to be1，v2Substituting the formula into the formula can obtain:
in practical situations, the transmission efficiency is also considered, and is generally 90% as G. Then it can be obtained
In the formula: n is1Is the rotating speed of the driving wheel at the outer side of the turning of the tracked vehicle, and the unit is r/min, n2The unit is r/min, the rotating speed of the driving wheel at the turning inner side of the tracked vehicle is n, because the tracked vehicle realizes turning movement1>n2This is always true, and in this case the unit should be converted to r/s. The above formula is a calculation mode of the steering time of the tracked vehicle, the steering angle theta can be measured by a sensor, and the steering time of the tracked vehicle can be calculated by the above formula.
Step seven: judging whether the current iteration number n reaches the maximum iteration number nmaxIf so, outputting a path with the shortest time consumption by combining the lateral turning cost of the crawler; if not, re-planning the path until n equals nmaxAnd outputs the shortest path as shown in fig. 5.
Claims (6)
1. A crawler path planning method based on an improved ant colony algorithm and an A-star algorithm is characterized by comprising the following steps:
(1) acquiring position information of obstacles around a vehicle body, performing grid segmentation on a planning area, marking the obstacles and accessible areas on the grid according to the position information of the obstacles, and constructing a grid map;
(2) setting initial parameters including the number of ants, pheromone elicitation factors, initial pheromone concentration, expected elicitation factors, pheromone volatilization constants, maximum iteration times, and a starting point S and an end point E of the tracked vehicle;
(3) placing ants at the position of the starting point S, and adding the position of the starting point S into a taboo table;
(4) planning the path of the tracked vehicle under the action of an optimized ant colony algorithm, wherein for each time t, the probability of selecting the next node j from the current node i by the ant k at the time t is calculated
And go to the next node; when the ant k reaches the next node j, adding the node i into the taboo table; wherein the probability
The adopted heuristic function is optimized based on the self-adaptive proportion in the A-x algorithm, when the ant k selects the next node each time, the distance between the current node and the end point E is estimated, and the node close to the end point E is endowed with a larger weight;
(5) judging whether the ants reach the end point E, if so, calculating the path length according to the path nodes in the tabu table, and otherwise, continuously searching the next node until the end point E is found; circulating all ants until all ants are traversed;
(6) calculating the time consumption of each path by combining a turning time cost function of the tracked vehicle, and selecting the optimal path of each iteration;
(7) judging whether the current iteration times reach the maximum iteration times, if so, outputting a path with the shortest consumed time by combining the turning cost of the tracked vehicle; otherwise, re-planning the path until reaching the maximum iteration times and outputting the path with the shortest time consumption.
2. The crawler path planning method based on the improved ant colony algorithm and the A-x algorithm according to claim 1, wherein the probability that the ant k selects the next node j at the current node i at the time t in the step (4)
Calculated using the following formula:
where allowed (k) is the set of nodes around the current node i of ant k that can be selected, Tij(t) represents pheromone content of ant k from node i to node j at time t, etaij(t) is the optimized heuristic function, which is the product of the heuristic weight and the heuristic function strength, α is the pheromone heuristic factor, and β is the expected heuristic factor.
3. The crawler path planning method based on the improved ant colony algorithm and the a-x algorithm according to claim 2, wherein the heuristic function divides the weight according to the distance between the node and the end point E, and the weight is distributed in proportion.
4. The crawler path planning method based on the improved ant colony algorithm and the A-x algorithm is characterized in that the number of selectable nodes around an ant is eight, and the weight ratio of the heuristic weight set from far to near to the end point is 1:2:2:3:3:4:4: 5.
5. The crawler path planning method based on the improved ant colony algorithm and the a-x algorithm according to claim 1, wherein the initial pheromone distribution in the step (4) is optimized as follows:
wherein λ isij(0) Representing the initial pheromone concentration from node i to node j, d (j, E) representing the distance from node j to end point E, xj、yjIs the abscissa and ordinate, x, of the node j in the grid mapE、yEIs the abscissa and ordinate of the end point E in the grid map, and C is a constant.
6. The method of claim 1, wherein the crawler path planning method based on the improved ant colony algorithm and the a-x algorithm is characterized in that the crawler turning time cost function is as follows:
wherein, t2The turning time of the tracked vehicle is s, n1Is the rotating speed of the driving wheel at the outer side of the turning of the tracked vehicle, and the unit is r/min, n2The unit of the rotating speed of a driving wheel on the inner side of the turning of the tracked vehicle is r/min, r is the radius of a driving wheel, the unit is m, B is the center distance between the two side tracks, the unit is m, G is the transmission efficiency of the tracked vehicle, and theta is the steering angle.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113222520A (en) * | 2021-06-16 | 2021-08-06 | 江苏佳利达国际物流股份有限公司 | Ant colony algorithm-based goods optimized distribution method and system |
| CN113515127A (en) * | 2021-07-23 | 2021-10-19 | 华北电力大学（保定） | Mobile robot path planning method and system |
| CN113607172A (en) * | 2021-08-09 | 2021-11-05 | 北京中安智能信息科技有限公司 | Underwater route planning method and device for underwater vehicle under multi-constraint condition |
| CN113703450A (en) * | 2021-08-23 | 2021-11-26 | 皖西学院 | Mobile robot path planning method for improving ant colony algorithm based on smooth factors |
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| CN114371666A (en) * | 2021-12-29 | 2022-04-19 | 长春理工大学 | 3D printing three-dimensional path planning method based on DLP technology |
| CN115167420A (en) * | 2022-07-04 | 2022-10-11 | 武汉理工大学 | System and method for planning personnel path of extra-long tunnel |
| CN115202365A (en) * | 2022-08-10 | 2022-10-18 | 广东工业大学 | Robot welding obstacle avoidance and optimization path planning method based on three-dimensional discrete point construction |
| CN115530015A (en) * | 2022-09-19 | 2022-12-30 | 扬州大学江都高端装备工程技术研究所 | Needle type brown mushroom thinning system based on regional ant colony path planning and application method |
| CN115727850A (en) * | 2022-11-15 | 2023-03-03 | 广东工业大学 | Multi-robot full-coverage method based on optimized spanning tree and processing terminal |
| CN116772882A (en) * | 2023-06-13 | 2023-09-19 | 河海大学 | Dynamic road construction route planning method based on multi-machine cooperation |
| CN117670162A (en) * | 2023-12-06 | 2024-03-08 | 珠海市格努信息技术有限公司 | Intelligent logistics solving method in field |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105589461A (en) * | 2015-11-18 | 2016-05-18 | 南通大学 | Parking system path planning method on the basis of improved ant colony algorithm |
| CN107562072A (en) * | 2017-10-11 | 2018-01-09 | 湖北工业大学 | A kind of unmanned plane optimum path planning method based on self-adaptive genetic operator |
| CN110196061A (en) * | 2019-05-29 | 2019-09-03 | 华北理工大学 | Based on the mobile robot global path planning method for improving ant group algorithm |
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| CN111323016A (en) * | 2019-11-26 | 2020-06-23 | 广东工业大学 | Mobile robot path planning method based on self-adaptive ant colony algorithm |
| CN111982125A (en) * | 2020-08-31 | 2020-11-24 | 长春工业大学 | Path planning method based on improved ant colony algorithm |
-
2021
- 2021-01-25 CN CN202110096087.7A patent/CN112925315A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105589461A (en) * | 2015-11-18 | 2016-05-18 | 南通大学 | Parking system path planning method on the basis of improved ant colony algorithm |
| CN107562072A (en) * | 2017-10-11 | 2018-01-09 | 湖北工业大学 | A kind of unmanned plane optimum path planning method based on self-adaptive genetic operator |
| CN110196061A (en) * | 2019-05-29 | 2019-09-03 | 华北理工大学 | Based on the mobile robot global path planning method for improving ant group algorithm |
| CN111323016A (en) * | 2019-11-26 | 2020-06-23 | 广东工业大学 | Mobile robot path planning method based on self-adaptive ant colony algorithm |
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| CN111982125A (en) * | 2020-08-31 | 2020-11-24 | 长春工业大学 | Path planning method based on improved ant colony algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 黄琦: "履带式无人车辆的路径规划方法研究", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113222520A (en) * | 2021-06-16 | 2021-08-06 | 江苏佳利达国际物流股份有限公司 | Ant colony algorithm-based goods optimized distribution method and system |
| CN113515127A (en) * | 2021-07-23 | 2021-10-19 | 华北电力大学（保定） | Mobile robot path planning method and system |
| CN113515127B (en) * | 2021-07-23 | 2022-09-13 | 华北电力大学（保定） | Mobile robot path planning method and system |
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