CN112183710B - Method and device for determining path - Google Patents
Method and device for determining path Download PDFInfo
- Publication number
- CN112183710B CN112183710B CN202011141236.9A CN202011141236A CN112183710B CN 112183710 B CN112183710 B CN 112183710B CN 202011141236 A CN202011141236 A CN 202011141236A CN 112183710 B CN112183710 B CN 112183710B
- Authority
- CN
- China
- Prior art keywords
- path
- pheromone
- ant
- determining
- optimal path
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 71
- 239000003016 pheromone Substances 0.000 claims abstract description 169
- 238000007689 inspection Methods 0.000 claims abstract description 40
- 241000257303 Hymenoptera Species 0.000 claims description 72
- 230000006870 function Effects 0.000 claims description 22
- 230000015654 memory Effects 0.000 claims description 19
- 238000004891 communication Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 7
- 238000012546 transfer Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 10
- 230000008901 benefit Effects 0.000 description 5
- 125000004122 cyclic group Chemical group 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 230000002457 bidirectional effect Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 229940057373 c-time Drugs 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Manipulator (AREA)
Abstract
The application provides a method and a device for determining a path, which relate to the field of robot path planning, and can not only accelerate the convergence speed of an ant colony algorithm, but also prevent the algorithm from sinking into local optimum, thereby improving the speed and the efficiency of path optimization of a patrol robot. The method comprises the following steps: determining at least m paths between a starting position and an end position through an ant colony algorithm; updating the pheromones on the effective path according to a first pheromone updating strategy; comparing the existing global optimal path with the local optimal path, and determining the current global optimal path; updating the pheromone on the current global optimal path according to a second pheromone updating strategy; adding one to the cycle number c, and repeating all the steps if the cycle number c does not reach n times; if the cycle times c reach n times, determining the current global optimal path as a target path of the inspection robot, wherein the target path is used for indicating the inspection robot to reach an end position along the target path from the initial position.
Description
Technical Field
The present application relates to the field of artificial intelligence, and in particular, to a method, apparatus, and system for determining a path.
Background
With the development of fifth generation communication technology, 5G communication is increasingly used in industry. And compared with the traditional 4G positioning, the 5G accurate positioning has higher precision. The 5G ultra-high network speed carrying inspection robot can rapidly and accurately realize the functions of early warning, meter reading, fault removal and the like, saves manpower and improves the working efficiency and accuracy. As the 5G inspection robot has more and more application scenes, the complexity of the scenes is also greater and greater. To achieve high-precision task execution, the 5G inspection robot must be able to achieve precise navigation. Considering the problems of robot power consumption, task execution time and the like, the 5G inspection robot must be able to find the shortest path from the departure point to the destination in the shortest time.
The ant colony algorithm is widely applied to the interior of the inspection robot and is used for intelligent navigation of the robot, avoidance of roadblocks, searching of optimal paths and the like. However, the traditional ant colony algorithm has the problems of low convergence speed, easy sinking into local optimum and the like, and is easy to cause the conditions of low efficiency, incapability of timely responding and the like of the inspection robot, so that the advantages of the 5G network cannot be fully exerted.
Disclosure of Invention
The application provides a method, a device and a system for determining a path, which are used for a routing inspection robot, can accelerate the convergence rate of path planning and prevent an algorithm from sinking into local optimum.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect, the present application provides a method of determining a path, the method may comprise: determining at least m paths between a starting position and an end position through an ant colony algorithm; the method comprises the steps that m is used for representing the total ant number of the ant colony algorithm, the value of m is greater than or equal to 1, at least m paths comprise effective paths, and the effective paths comprise local optimal paths; updating the pheromone on the effective path according to a first pheromone updating strategy; comparing the existing global optimal path with the local optimal path, and determining the current global optimal path; updating the pheromone on the current global optimal path according to a second pheromone updating strategy; adding one to the cycle number c, and repeating all the steps if the cycle number c does not reach n times; wherein, the initial value of c is 0, n is used for representing the total circulation times, and the value of n is more than 1; and if the cycle times c reach n times, determining a current global optimal path as a target path of the inspection robot, wherein the target path is used for indicating the inspection robot to reach the end position along the target path from the initial position.
In a second aspect, the present application provides an apparatus for determining a path, the apparatus comprising: the device comprises a cyclic search module, a first updating module, a first determining module, a second updating module, a first counting detection module and a second determining module. The loop searching module is used for determining at least m paths between a starting position and an end position through an ant colony algorithm; the method comprises the steps that m is used for representing the total ant number of the ant colony algorithm, the value of m is greater than or equal to 1, at least m paths comprise effective paths, and the effective paths comprise local optimal paths; the first updating module is used for updating the pheromones on the effective path according to a first pheromone updating strategy; the first determining module is used for comparing the existing global optimal path with the local optimal path and determining the current global optimal path; the second updating module is used for updating the pheromone on the current global optimal path according to a second pheromone updating strategy; the first counting detection module is used for adding one to the cycle number c and judging whether the cycle number c reaches n times or not; if the cycle times c do not reach n times, indicating all the modules to execute the corresponding steps again in sequence; wherein, the initial value of c is 0, n is used for representing the total circulation times, and the value of n is more than 1; and the second determining module is used for determining a current global optimal path as a target path of the inspection robot if the cycle times c reach n times, wherein the target path is used for indicating the inspection robot to reach the end position along the target path from the initial position.
In a third aspect, the present application provides an apparatus for determining a path, the apparatus comprising: a processor, a transceiver, and a memory. Wherein the memory is used to store one or more programs. The one or more programs include computer-executable instructions that, when executed by the apparatus, cause the apparatus to perform the method of determining a path as described in any of the first aspect and its various alternative implementations.
In a fourth aspect, the present application provides a computer readable storage medium having instructions stored therein which, when executed by a computer, perform the method of determining a path as described in any of the above first aspect and its various alternative implementations.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when run on a computer, performs the method of determining a path of the first aspect and any of its various alternative implementations.
According to the method and the device for determining the path, provided by the application, the m ants are used for carrying out c-time cyclic search between the initial position and the final position of the path to be determined through the ant colony algorithm; the effective path with the shortest length obtained by searching in each cycle is a local optimal path, and the pheromone on the effective path is updated according to a first pheromone updating strategy; and after multiple times of circulation, searching to obtain a local optimal path with the shortest length as a global optimal path, and updating the pheromone on the current global optimal path according to a second pheromone updating strategy. Compared with the prior art, the method has the advantages that after each cycle is finished, all ants update the pheromone uniformly. The method for determining the path provided by the application carries out the pheromone update based on the principle of how much ant searches the contribution of the optimal path in each cycle, namely only the pheromone on the effective path is increased, and more pheromones are added on the locally optimal path, and then the pheromone on the globally optimal path is additionally increased, thereby accelerating the convergence speed of the ant colony algorithm, avoiding the algorithm from sinking into the locally optimal path, and improving the speed and the accuracy of the determined path.
Drawings
Fig. 1 is a schematic structural diagram of an inspection robot to which the method and apparatus for determining a path provided in the embodiments of the present application are applied;
FIG. 2 is a schematic diagram of a method for determining a path according to an embodiment of the present application;
FIG. 3 is a schematic diagram of another method for determining a path according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an environmental model provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a path determining apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram II of a path determining apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram III of a path determining apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a path determining apparatus according to an embodiment of the present application.
Detailed Description
First, terms related to the embodiments of the present application will be described.
Ant colony algorithm: the ant colony algorithm (ant colony algorithm) is a population-based simulated evolution algorithm which is inspired by people on the research result of the actual ant colony body behaviors in the nature, and belongs to a random search algorithm. The ants can leave the pheromone on the path through which the ants pass in the process of movement through 'pheromone' (pheromone) information transfer between the ants, and the ants can sense the existence and the intensity of the pheromone in the process of movement and guide the movement direction of the ants by the aid of the pheromone, and the ants tend to move towards places with high pheromone concentration. Thus, the collective behavior of an ant colony composed of a large number of ants exhibits an information positive feedback phenomenon: the more ants that walk on a path, the greater the probability that the latter will select that path. The ant colony algorithm for searching shortest path is the collective behavior of ant, and the ant colony algorithm designs virtual ant to search different paths and leave virtual pheromone gradually disappearing with time, and the optimal path may be found based on the principle of denser pheromone path.
Ant: the ant colony algorithm designs 'ants' which simulate the actions of ants in the ant colony virtually, and each common ant has the following characteristics:
(1) Ants select their next node according to probability functions that take pheromone and heuristic information as variables.
(2) It is specified that ants cannot walk around the already walked nodes unless the search is completed, controlled by a tabu table.
(3) After the search is completed, the ants leave the pheromone on the path that they walk.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or between different processes of the same object and not for describing a particular order of objects.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.
The method, the device and the system for determining the path provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of a patrol robot according to an embodiment of the present application.
On the basis of the existing substation inspection robot, the multifunctional inspection robot uses advanced communication and control and other technical means, and functions of the multifunctional inspection robot are enhanced and expanded through 5G, so that the multifunctional inspection robot has the characteristics of flexibility, real-time, high definition and panorama. The inspection robot is used as an intelligent mobile platform, so that the position of a fault point of a transformer substation can be rapidly and intelligently positioned, and the advantage of large bandwidth of the 5G technology can be fully exerted. The inspection robot not only can improve the efficiency of common power inspection, but also can be widely applied to various scenes such as overhaul, rush repair, construction and the like, improves the field management and control and the operation level, and improves the operation efficiency and the safety.
Wherein the 5G customer premise equipment (customer premise equipment, CPE) is for implementing 5G communication; the laser navigation system models the operation scene by adopting a laser radar, so that the automatic cruising task of timing and fixed points is realized, and unmanned inspection is truly realized; the VR panoramic camera adopts a virtual reality technology, and the video can be transmitted back to VR glasses of the monitoring center, so that the driving feeling of a remote patrol personnel is improved, and the remote manual spot check and remote teaching functions are realized; the thermal infrared imager is used for measuring the temperature of the instrument; the high-definition camera is used for measuring readings of instruments and meters, and achieving high-precision and high-reliability operation scene environment and equipment image acquisition.
The embodiment of the application provides a method for determining a path, which is applied to a patrol robot shown in fig. 1, and as shown in fig. 2, the method can comprise S101-S107:
s101, determining at least m paths between a starting position and an end position through an ant colony algorithm.
Wherein, m is used for representing the total ant number of the ant colony algorithm, and the value of m is more than or equal to 1. The m paths include an active path and an inactive path.
According to the ant colony algorithm, m ants search between a starting position and an end position in one cycle, and if no ants search reversely, m ants search to obtain m paths; if the ants select to perform reverse search, m ants can search to obtain more than m paths. The path obtained by ant k search can be an effective path or an ineffective path, wherein the effective path is a path obtained by ant search and connecting a starting position and an end position, and the ineffective path is a path obtained by ant search without a next optional position point when the end position or the starting position is not reached yet. The effective path with the shortest path length is called a locally optimal path.
S102, updating the pheromones on the effective path according to a first pheromone updating strategy.
Specifically, the first pheromone updating strategy specifically includes that a path between adjacent position points i and j<i,j>On t 1 Pheromone tau at +Deltat ij (t 1 +Δt) and t 1 Time pheromone tau ij (t 1 ) The following formula is satisfied:
τ ij (t 1 +Δt)＝τ ij (t 1 )+Δ * τ ij (t 1 ,t 1 +Δt)
wherein τ ij (t 1 ) Indicating the route after the end of the present cycle<i,j>A pheromone thereon; delta * τ ij Representing a path<i,j>A pheromone increment thereon; EP represents an effective path; LB represents a locally optimal path; re represents the ratio of the total effective path length to the locally optimal path length.
And according to the first pheromone updating strategy, only the effective path obtained by the current cycle search is subjected to pheromone increase. Because the local optimal path is the effective path with the shortest length, the value of Re is more than or equal to 1, and compared with other effective paths, the pheromone on the local optimal path is greatly increased. Therefore, the first pheromone updating strategy has the effect that when ants select paths in the next circulation, the ants can select effective paths with larger probability, so that the convergence speed of the ant colony algorithm is improved, and the path searching efficiency is improved.
S103, comparing the existing global optimal path with the local optimal path, and determining the current global optimal path.
Specifically, after the first cycle is finished, the obtained local optimal path is the effective path with the shortest current path length, so that the local optimal path is determined to be the current global optimal path; after the circulation is finished for more than two times, comparing the local optimal path generated by each circulation with the existing global optimal path, and determining the path with shorter path length as the current global optimal path.
S104, updating the pheromone on the current global optimal path according to a second pheromone updating strategy.
Specifically, the second pheromone updating strategy specifically includes a path between adjacent position points i and j<i,j>On t 2 Pheromone tau at +Deltat ij (t 2 +Δt) and t 2 Time pheromone tau ij (t 2 ) The following formula is satisfied:
τ ij (t 2 +Δt)＝τ ij (t 2 )+Δ * τ ij (t 2 ,t 2 +Δt)
wherein τ ij (t 2 ) Representing a post-pheromone path on said locally optimal path updated according to said first pheromone updating strategy<i,j>Is a pheromone of (2); l (L) * Representing the length of the globally optimal path; GB denotes a globally optimal path.
The second pheromone updating strategy has the effect that when ants select paths in the next cycle, global optimal paths can be selected with larger probability, the algorithm is prevented from being trapped into local optimal, and the accuracy of path searching is improved.
S105, adding one to the cycle number c.
S106, judging whether the cycle number c reaches the total cycle number n.
Specifically, if the cycle number c does not reach the total cycle number n, repeating the steps S101 to S104; if so, the process advances to step S107.
Optionally, if the number of loops c does not reach the total number of loops n, but it is detected that the current global optimal path is unchanged within the preset time, step S107 is performed. Wherein the preset time is 10s.
S107, determining the current global optimal path as a target path of the inspection robot.
Specifically, the target path is used for indicating that the inspection robot reaches the end position along the target path from the initial position.
According to the method for determining the path, provided by the application, the m ants are used for carrying out c-time cyclic search between the initial position and the final position of the path to be determined through the ant colony algorithm; the effective path with the shortest length obtained by searching in each cycle is a local optimal path, and the pheromone on the effective path is updated according to a first pheromone updating strategy; and after multiple times of circulation, searching to obtain a local optimal path with the shortest length as a global optimal path, and updating the pheromone on the current global optimal path according to a second pheromone updating strategy. Compared with the prior art, the method has the advantages that after each cycle is finished, all ants update the pheromone uniformly. The method for determining the path provided by the application carries out the pheromone update based on the principle of how much ant searches the contribution of the optimal path in each cycle, namely only the pheromone on the effective path is increased, and more pheromones are added on the locally optimal path, and then the pheromone on the globally optimal path is additionally increased, thereby accelerating the convergence speed of the ant colony algorithm, avoiding the algorithm from sinking into the locally optimal path, and improving the speed and the accuracy of the determined path.
The traditional ant colony algorithm is to search the target point from the starting point in one direction, and finish after searching the target point, so that although the optimal path can be found, the convergence speed is slower and the time is longer. In addition, the conventional ant colony algorithm has the tabu list, so that the ants are locked after entering the dead loop, and in the initial stage of the ant colony algorithm, the ants are relatively large in blindness and easy to lock, so that the utilization rate of the ants is greatly reduced, and the efficiency of the ant colony algorithm is reduced.
In order to solve the above problem, an embodiment of the present application provides another method for determining a path, which is applied to the inspection robot shown in fig. 1, and as shown in fig. 3, the method may include S201-S215:
s201, building an environment model by the inspection robot.
Specifically, the inspection robot builds an environment model according to the environment information identified by the sensor. Optionally, in order to facilitate the ant colony algorithm to search an optimal path, the inspection robot adopts a grid method to divide the environment and two-dimension the working space into a grid map. As shown in fig. 4, the grid points in the grid map, that is, the position points of the ant movement, include the start position and the end position of the path to be determined, and a limited number of obstacle position points.
S202, initializing initial parameters of an ant colony algorithm by the inspection robot.
The initial parameters comprise a starting position and an end position of a path to be determined, a total ant number m of an ant colony algorithm and a total cycle number n. Optionally, the initial parameters may also include other parameters, such as an information heuristic factor α, an expected heuristic factor β, a pheromone volatilization factor ρ, an initial pheromone concentration, and the like, which is not limited by the present application.
The initial parameters are used for determining a shortest path for connecting a starting position and an ending position and avoiding obstacle position points by adopting an ant colony algorithm.
S203, setting the searching starting points of x ants in the m ants at the initial position, and setting the searching starting points of the rest m-x ants at the final position.
Specifically, the inspection robot randomly divides m ants into two groups, sets the searching start point of one group of x ants at the initial position to search the path towards the end position, and sets the searching start point of the other group of m-x ants at the end position to search the path towards the initial position, thereby realizing bidirectional searching. The value of x can be adjusted according to practical conditions, namely, the two groups of ants can be equal or unequal in number, and the application is not limited. Alternatively, the group may be made according to the number of m ants, so that the odd-numbered ants start searching from the start position and the even-numbered ants start searching from the end position.
On the basis of bidirectional searching, reverse searching and single ant pheromone updating are introduced, namely if the ant searches to obtain an effective path or no next selectable position point exists, the pheromone updating is carried out, so that waiting of the ants in iteration is reduced, the repeated utilization rate of the ants is increased, and the searching efficiency is improved.
S204, introducing an obstacle avoidance function, and selecting the next position point of the ant k by using a roulette method.
Wherein the initial value of k is 1.
In one implementation, the obstacle avoidance function is:
wherein, allow k Representing all the position points which can be selected by the ant k in the next step, specifically, the ant k can reach adjacent position points along 8 directions of up, right, down, left, right up, right down, left up and left down on one position point, and the position points which the ant k has passed and the obstacle position points are excluded, namely, all the position points which the ant k can select in the next step; d, d ij Representing the distance between position point i and position point j; s is(s) g j Representing the shortest distance from the position point j to the end point position g; s represents the position point of the next step of the ant corresponding to the shortest distance.
After the obstacle avoidance function is introduced, the roulette method is used for selecting the next position point, specifically, at the time t, ant k is used for selecting the next position point according to the transfer probability p k ij (t) transition from location point i to location point j. Wherein the transition probability p k ij (t) satisfies the following formula:
wherein alpha is a non-zero constant, represents an information heuristic factor, represents the relative importance degree of pheromones in an ant colony algorithm, reflects the influence degree of the pheromone amount on a path on the ant selection path, and has stronger collaboration among ants as the alpha value is larger. Beta is a non-zero constant representing the desired heuristic factor, representing the relative importance of the heuristic in the ant colony algorithm.
The obstacle avoidance function has the effect that when the next position point is selected, the position point which is closer to the end point position can be selected with higher probability by ant k.
S205, judging whether ant k searches for a valid path or does not have a next selectable position point.
Optionally, the path obtained by ant k search may be an effective path or an ineffective path, where the effective path is a path obtained by ant search and connecting a start position and an end position, and the ineffective path is a path obtained by ant search without a next optional position point when the end position or the start position is not reached yet.
If ant k searches to obtain an effective path or there is no next selectable position point, proceeding to step S206; if ant k has not searched for a valid path and there is a next selectable location point, the above step S204 is performed in a loop.
S206, updating the single ant pheromone.
In particular, the single ant pheromone update is performed by a path between adjacent position points i and j<i,j>At time t+Δt, pheromone τ ij Pheromone tau at times (t+Deltat) and t ij The following formula is satisfied between (t):
τ ij (t+Δt)＝(1-ρ)·τ ij (t)+Δτ ij k (t,t+Δt)
wherein the constant rho epsilon (0, 1) represents the pheromone volatilization factor, the loss degree of the pheromone on the path is represented, the magnitude of rho is related to the global searching capability and the convergence rate of the algorithm, and 1-rho represents the pheromone residual factor; τ ij (t) is shown inSearching to obtain the path after the path<i,j>Is a pheromone of (2); Δτ ij k (t) represents ant k on the path after searching to obtain the path<i,j>A pheromone increment thereon; q is a constant, and represents the total pheromone amount released by ants in one cycle; l (L) k Representing a path<i,j>Is a length of (c).
Specifically, after judging that the ant k searches for an effective path or does not have a next selectable position point, i.e. after finishing the cycle, the pheromone on each path in the grid map is volatilized. While the path traversed by ant k leaves the pheromone released by ant k, the shorter the path the more pheromone remains.
S207, if ant k has searched to obtain a valid path, randomly selecting whether ant k is to be searched reversely.
Alternatively, if ant k searches from the start position to the end position for the first time, it is randomly selected whether ant k wants to use the end position as a new search start point, and a reverse search is performed to the start position. If ant k first searches from the end position to the start position, randomly selecting whether ant k needs to take the start position as a new searching start point, and carrying out reverse searching to the end position.
S208, judging whether the ant k is to be searched reversely.
If ant k is to search reversely, go to step S209 (a), and repeat steps S204 to S207 described above; if ant k does not perform the reverse search, the process advances to step S209 (b).
S209 (a), the current location point is set as the search start point.
Specifically, if the current position point is the initial position, ant k takes the initial position as a new searching starting point and carries out reverse searching towards the final position; if the current position point is the end position, ant k takes the end position as a new searching starting point and carries out reverse searching to the initial position.
S209 (b), judging whether the ant number k reaches the total ant number m.
Specifically, the initial value of k is 1, which means that in this cycle, k ants have completed the path search. If k does not reach m, which means that the cycle is not finished, the process proceeds to step S210 (a), and the steps S204 to S208 are repeated; if k reaches m, which represents the end of the present cycle, the process proceeds to step S210 (b).
S210 (a), the ant number k is increased by one.
S210 (b), updating the pheromones on the effective path according to a first pheromone updating strategy.
Specifically, the first pheromone updating strategy specifically includes that a path between adjacent position points i and j<i,j>On t 1 Pheromone tau at +Deltat ij (t 1 +Δt) and t 1 Time pheromone tau ij (t 1 ) The following formula is satisfied:
τ ij (t 1 +Δt)＝τ ij (t 1 )+Δ * τ ij (t 1 ,t 1 +Δt)
wherein τ ij (t 1 ) Indicating the route after the end of the present cycle<i,j>A pheromone thereon; delta * τ ij Representing a path<i,j>A pheromone increment thereon; EP represents an effective path; LB represents a locally optimal path; re represents the ratio of the total effective path length to the locally optimal path length.
Specifically, after one cycle is finished, m ants search to obtain at least m paths, wherein the m paths comprise an effective path and an ineffective path, and the effective path with the shortest path length obtained by searching in the current cycle is called a local optimal path LB.
According to a first pheromone updating strategy, the pheromone is increased only for the effective path obtained by the current cycle searching, wherein the value of Re is more than or equal to 1, and the pheromone on the local optimal path is greatly increased compared with other effective paths because the local optimal path is the effective path with the shortest length. Therefore, the first pheromone updating strategy has the effect that when ants select paths in the next circulation, the ants can select effective paths with larger probability, so that the convergence speed of the ant colony algorithm is improved, and the path searching efficiency is improved.
S211, comparing and determining a current global optimal path.
Specifically, after the first cycle is finished, the obtained local optimal path is the effective path with the shortest current path length, so that the local optimal path is determined to be the current global optimal path; after the circulation is finished for more than two times, comparing the local optimal path generated by each circulation with the existing global optimal path, and determining the path with shorter path length as the current global optimal path.
S212, updating the pheromone on the current global optimal path according to the second pheromone updating strategy.
Specifically, the second pheromone updating strategy specifically includes a path between adjacent position points i and j<i,j>On t 2 Pheromone tau at +Deltat ij (t 2 +Δt) and t 2 Time pheromone tau ij (t 2 ) The following formula is satisfied:
τ ij (t 2 +Δt)＝τ ij (t 2 )+Δ * τ ij (t 2 ,t 2 +Δt)
wherein τ ij (t 2 ) Representing a post-pheromone path on said locally optimal path updated according to said first pheromone updating strategy<i,j>Is a pheromone of (2); l (L) * Representing the length of the globally optimal path; GB denotes a globally optimal path.
The second pheromone updating strategy has the effect that when ants select paths in the next cycle, global optimal paths can be selected with larger probability, the algorithm is prevented from being trapped into local optimal, and the accuracy of path searching is improved.
S213, adding one to the cycle number c.
S214, judging whether the cycle number c reaches the total cycle number n.
Specifically, if the cycle number c does not reach the total cycle number n, repeating the steps S204 to S213; if so, the process advances to step S215.
Optionally, if the number of loops c does not reach the total number of loops n, but it is detected that the current global optimal path is unchanged within the preset time, step S215 is performed. Wherein the preset time is 10s.
S215, determining the current global optimal path as a target path of the inspection robot.
Specifically, the target path is used for indicating that the inspection robot reaches the end position along the target path from the initial position.
According to the method, the device and the system for determining the path, provided by the application, the m ants are used for carrying out c-time cyclic search between the initial position and the final position of the path to be determined through the ant colony algorithm; setting the searching starting points of x ants in m ants at the initial position, and setting the searching starting points of the rest m-x ants at the final position; introducing an obstacle avoidance function, and selecting the next position point of the ant k by using a roulette method; if the ant k searches to obtain an effective path or no next selectable position point exists, updating the single ant pheromone; performing reverse search randomly; updating the pheromones on the effective path according to a first pheromone updating strategy; and updating the pheromone on the current global optimal path according to the second pheromone updating strategy. Compared with the prior art, the method has the advantages that after each cycle is finished, all ants update the pheromone uniformly. According to the method for determining the path, the pheromone is updated based on the principle that the ants find the contribution of the optimal path in each cycle, namely, only the pheromone on the effective path is increased, more pheromones are added on the locally optimal path, and the pheromone on the globally optimal path is additionally increased, so that the convergence speed of an ant colony algorithm is increased, meanwhile, the algorithm can be prevented from falling into the locally optimal path, and the speed and the accuracy of the determined path are improved; on the basis of bidirectional searching, reverse searching and single ant pheromone updating are introduced, namely if the ant searches to obtain an effective path or no next selectable position point exists, the pheromone updating is carried out, so that waiting of the ants in iteration is reduced, the repeated utilization rate of the ants is increased, and the searching efficiency is improved; the obstacle avoidance function is introduced, so that the probability of selecting a position point closer to the end point position by the ants is increased, the number of dead ants is reduced, and the global optimal efficiency of ant searching in the whole algorithm is greatly improved.
The embodiment of the application can divide the functional modules or functional units of the path determining device according to the method example, for example, each functional module or functional unit can be divided corresponding to each function, and two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware, or in software functional modules or functional units. The division of the modules or units in the embodiment of the present application is schematic, which is merely a logic function division, and other division manners may be implemented in practice.
Figure 5 shows a schematic diagram of one possible construction of the path determining device involved in the above embodiment. The apparatus includes a circular search module 204, a first update module 2051, a first determination module 2061, a second update module 2052, a first count detection module 207, and a second determination module 2062.
Wherein the loop search module 204 is configured to determine at least m paths between a start position and an end position by using an ant colony algorithm; the m is used for representing the total ant number of the ant colony algorithm, the value of m is greater than or equal to 1, at least m paths comprise effective paths, and the effective paths comprise locally optimal paths.
The first updating module 2051 is configured to update the pheromones on the effective path according to a first pheromone updating policy.
Wherein the first pheromone updating strategy is specifically that a path between adjacent position points i and j<i,j>On t 1 Pheromone tau at +Deltat ij (t 1 +Δt) and t 1 Time pheromone tau ij (t 1 ) The following formula is satisfied:
τ ij (t 1 +Δt)＝τ ij (t 1 )+Δ * τ ij (t 1 ,t 1 +Δt)
wherein τ ij (t 1 ) Indicating the route after the end of the present cycle<i,j>A pheromone thereon; delta * τ ij Representing a path<i,j>A pheromone increment thereon; EP represents an effective path; LB represents a locally optimal path; re represents the ratio of the total effective path length to the locally optimal path length.
A first determining module 2061 is configured to compare the existing global optimal path with the local optimal path, and determine the current global optimal path.
The second updating module 2052 is configured to update the pheromone on the current global optimal path according to a second pheromone updating policy.
Wherein the second pheromone updating strategy is specifically that a path between adjacent position points i and j<i,j>On t 2 Pheromone tau at +Deltat ij (t 2 +Δt) and t 2 Time pheromone tau ij (t 2 ) The following formula is satisfied:
τ ij (t 2 +Δt)＝τ ij (t 2 )+Δ * τ ij (t 2 ,t 2 +Δt)
wherein τ ij (t 2 ) Representing a post-pheromone path on said locally optimal path updated according to said first pheromone updating strategy <i,j>Is a pheromone of (2); q is a constant, representing the pheromone concentration; l (L) * Representing the length of the globally optimal path; GB denotes a globally optimal path.
The first count detection module 2061 is configured to increment the cycle number c by one, and determine whether the cycle number c reaches n times; if the cycle times c do not reach n times, indicating all the modules to execute the corresponding steps again in sequence; the initial value of c is 0, n is used for representing the total circulation times, and the value of n is greater than 1.
The second determining module 2062 is configured to determine, if the number of cycles c reaches n, a current global optimal path as a target path of the inspection robot, where the target path is used to instruct the inspection robot to reach the end position along the target path from the start position.
Optionally, in conjunction with fig. 5, as shown in fig. 6, the apparatus for determining a path provided by the embodiment of the present application may further include an environment modeling module 201, an algorithm initialization module 202, and an ant initialization module 203.
The environment modeling module 201 is configured to build an environment model according to the environment information identified by the inspection robot sensor, where the environment model includes the start position and the end position.
The algorithm initializing module 202 is configured to initialize initial parameters of the ant colony algorithm, where the initial parameters include: the initial position of the path to be determined, the final position of the path to be determined, the total ant number m of the ant colony algorithm and the total cycle number n.
The ant initializing module 203 is configured to set a search start point of x ants in the m ants at the start position, and set a search start point of the remaining m-x ants at the end position, where the value of x is greater than 0 and less than m.
Optionally, in conjunction with fig. 5, as shown in fig. 7, in the apparatus for determining a path provided by the embodiment of the present application, the circular search module 204 includes a first selection unit 2041, a judgment unit 2042, a third update module 2043, a second selection unit 2044, a reverse search unit 2045, and a second count detection unit 2046.
The first selecting unit 2041 is configured to introduce an obstacle avoidance function, and select a next position point of the ant k by using a roulette method; the initial value of k is 1.
Wherein, the obstacle avoidance function is:
wherein, allow k Representing all the position points which can be selected by ant k in the next step; d, d ij Representing the distance between position point i and position point j; s is(s) g j Representing the shortest distance from the position point j to the end point position g; s represents the position point of the next step of the ant corresponding to the shortest distance.
The first selecting unit 2041 is used for transferring ant k from the position point i to the position point j according to the transfer probability at the time t, wherein the transfer probability p k ij (t) satisfies the following formula:
wherein α is a constant representing an information heuristic factor; beta is a constant representing the desired heuristic.
The judging unit 2042 is configured to judge whether the ant k searches for an effective path connecting the start position and the end position or does not have a next selectable position point, and if not, instruct the first selecting unit 2041 to execute a corresponding step.
The third updating unit 2043 is configured to update the single ant pheromone if the ant k searches for an effective path connecting the start position and the end position or if no next selectable position point exists.
Wherein the single ant pheromone updates specifically to the path between adjacent position points i and j<i,j>At time t+Δt, pheromone τ ij Pheromone tau at times (t+Deltat) and t ij The following formula is satisfied between (t):
τ ij (t+Δt)＝(1-ρ)·τ ij (t)+Δτ ij k (t,t+Δt)
wherein the constant ρ e (0, 1) represents the pheromone volatilization factor; τ ij (t) represents the path after searching for the present path<i,j>Is a pheromone of (2); Δτ ij k (t) represents ant k on the path after searching to obtain the path<i,j>A pheromone increment thereon; l (L) k Representing a path<i,j>Is a length of (c).
The second selecting unit 2044 is configured to randomly select whether the ant k is to be searched for in the reverse direction if the ant k has searched for one of the valid paths.
The reverse search unit 2045 is configured to set the current location point as a search start point if the ant k is to search in the reverse direction, and instruct the first selection unit 2041, the judgment unit 2042, the third update unit 2043, and the second selection unit 2044 to sequentially perform corresponding steps.
The second count detecting unit 2046 is configured to determine whether k reaches m if the search is not reversed, and if k does not reach m, let k=k+1, and repeat the steps performed by all the above modules.
The device for determining the path provided by the embodiment of the application carries out c times of cyclic search between the initial position and the final position of the path to be determined by using m ants through an ant colony algorithm; setting the searching starting points of x ants in m ants at the initial position, and setting the searching starting points of the rest m-x ants at the final position; introducing an obstacle avoidance function, and selecting the next position point of the ant k by using a roulette method; if the ant k searches to obtain an effective path or no next selectable position point exists, updating the single ant pheromone; performing reverse search randomly; updating the pheromones on the effective path according to a first pheromone updating strategy; and updating the pheromone on the current global optimal path according to the second pheromone updating strategy. The device for determining the path provided by the application carries out pheromone update by taking the contribution of ants searching the optimal path in each cycle as a principle, namely only the pheromone on the effective path is increased, more pheromones are added on the locally optimal path, and the pheromone on the globally optimal path is additionally increased, so that the convergence speed of an ant colony algorithm is increased, meanwhile, the algorithm can be prevented from falling into the locally optimal path, and the speed and the accuracy of the determined path are improved; on the basis of bidirectional searching, reverse searching and single ant pheromone updating are introduced, namely if the ant searches to obtain an effective path or no next selectable position point exists, the pheromone updating is carried out, so that waiting of the ants in iteration is reduced, the repeated utilization rate of the ants is increased, and the searching efficiency is improved; the obstacle avoidance function is introduced, so that the probability of selecting a position point closer to the end point position by the ants is increased, the number of dead ants is reduced, and the global optimal efficiency of ant searching in the whole algorithm is greatly improved.
Figure 8 shows a further possible constructional schematic of the means of determining the path involved in the above-described embodiments. The apparatus for determining a path includes: the processor 301, the processor 301 is configured to control and manage actions of the device, for example, perform the steps performed by the loop search module 204, the first update module 2051, the first determination module 2061, the second update module 2502, the first count detection module 207, the second determination module 2062, and/or perform other processes of the techniques described herein. The means for determining a path may further comprise a memory 302, a communication interface 303 and a bus 304, the communication interface 303 being for supporting communication of the means for determining a path with other network entities, the memory 301 being for storing program code and data of the means for determining a path.
Wherein the memory 301 may be a memory in a means for determining a path, etc., which may comprise a volatile memory, such as a random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state disk; the memory may also comprise a combination of the above types of memories.
The processor 302 described above may be implemented or executed with various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor may be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor may also be a combination that performs the function of a computation, e.g., a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, etc.
Bus 304 may be an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus or the like. The bus 304 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
An embodiment of the application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of determining a path as described in the method embodiment above.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, when the network equipment executes the instructions, the network equipment executes each step executed by the path determining device in the method flow shown in the method embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a register, a hard disk, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuit, ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The foregoing is merely illustrative of specific embodiments of the present application, and the scope of the present application is not limited thereto, but any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (8)
1. A method of determining a path for use with a patrol robot, the method comprising:
determining at least m paths between a starting position and an end position through an ant colony algorithm; the method comprises the steps that m is used for representing the total ant number of the ant colony algorithm, the value of m is greater than or equal to 1, at least m paths comprise effective paths, and the effective paths comprise local optimal paths;
updating the pheromone on the effective path according to a first pheromone updating strategy;
comparing the existing global optimal path with the local optimal path, and determining the current global optimal path;
updating the pheromone on the current global optimal path according to a second pheromone updating strategy;
adding one to the cycle number c, and repeating all the steps if the cycle number c does not reach n times; wherein, the initial value of c is 0, n is used for representing the total circulation times, and the value of n is more than 1;
If the cycle times c reach n times, determining a current global optimal path as a target path of the inspection robot, wherein the target path is used for indicating the inspection robot to reach the end position along the target path from the initial position;
the first pheromone updating strategy is specifically that a path between adjacent position points i and j<i,j>On t 1 Pheromone tau at +Deltat ij (t 1 +Δt) and t 1 Time pheromone tau ij (t 1 ) The following formula is satisfied:
τ ij (t 1 +Δt)＝τ ij (t 1 )+Δ * τ ij (t 1 ,t 1 +Δt)
wherein τ ij (t 1 ) Indicating the route after the end of the present cycle<i,j>A pheromone thereon; delta * τ ij Representing a path<i,j>A pheromone increment thereon; EP represents an effective path; LB represents a locally optimal path; re represents the ratio of the total effective path length to the locally optimal path length.
2. Method according to claim 1, characterized in that the second pheromone updating strategy is in particular a path between adjacent location points i and j<i,j>On t 2 Pheromone tau at +Deltat ij (t 2 +Δt) and t 2 Time of dayPheromone tau of (2) ij (t 2 ) The following formula is satisfied:
τ ij (t 2 +Δt)＝τ ij (t 2 )+Δ * τ ij (t 2 ,t 2 +Δt)
wherein,
τ ij (t 2 ) Representing a post-pheromone path on said locally optimal path updated according to said first pheromone updating strategy<i,j>Is a pheromone of (2); q is a constant, representing the pheromone concentration; l (L) * Representing the length of the globally optimal path; GB denotes a globally optimal path.
3. Method according to claim 1 or 2, characterized in that before the determination of at least m paths between a start position and an end position by means of the ant colony algorithm, the method further comprises:
according to the environmental information identified by the inspection robot sensor, an environmental model is established, wherein the environmental model comprises the starting position and the end position;
initializing initial parameters of the ant colony algorithm, wherein the initial parameters comprise: the initial position of the path to be determined, the final position of the path to be determined, the total ant number m of the ant colony algorithm and the total cycle number n;
setting the searching starting points of x ants in m ants at the starting position, setting the searching starting points of the rest m-x ants at the end position, wherein the value of x is larger than 0 and smaller than m.
4. Method according to claim 1 or 2, characterized in that said determining at least m paths between a start position and an end position by means of an ant colony algorithm comprises in particular:
introducing an obstacle avoidance function, and selecting the next position point of the ant k by using a roulette method; the initial value of k is 1;
Judging whether ant k searches to obtain an effective path connecting the initial position and the final position or does not have a next selectable position point, if not, repeating the steps, and if so, updating the single ant pheromone;
if ant k has searched to obtain an effective path, randomly selecting whether ant k needs to be searched reversely;
if the ant k is to search reversely, setting the current position point as a searching starting point, repeating all the steps, and if not, judging whether k reaches m;
if k does not reach m, let k=k+1 and repeat all the above steps.
5. The method of claim 4, wherein the obstacle avoidance function is:
wherein,
allow k representing all the position points which can be selected by ant k in the next step; d, d ij Representing the distance between position point i and position point j; s is(s) g j Representing the shortest distance from the position point j to the end point position g; s represents the position point of the next step of the ant corresponding to the shortest distance;
after the obstacle avoidance function is introduced, selecting the next position point by using a roulette method, namely transferring the ant k from the position point i to the position point j according to the transfer probability at the time t, wherein the transfer probability p k ij (t) satisfies the following formula:
Wherein,
alpha is a constant and represents an information heuristic factor; beta is a constant representing the desired heuristic.
6. A device for determining a path, for use with a patrol robot, comprising:
the circulation searching module is used for determining at least m paths between a starting position and an end position through an ant colony algorithm; the method comprises the steps that m is used for representing the total ant number of the ant colony algorithm, the value of m is greater than or equal to 1, at least m paths comprise effective paths, and the effective paths comprise local optimal paths;
the first updating module is used for updating the pheromones on the effective path according to a first pheromone updating strategy; the first pheromone updating strategy is specifically that a path between adjacent position points i and j<i,j>On t 1 Pheromone tau at +Deltat ij (t 1 +Δt) and t 1 Time pheromone tau ij (t 1 ) The following formula is satisfied:
τ ij (t 1 +Δt)＝τ ij (t 1 )+Δ * τ ij (t 1 ,t 1 +Δt)
wherein,
τ ij (t 1 ) Indicating the route after the end of the present cycle<i,j>A pheromone thereon; delta * τ ij Representing a path<i,j>A pheromone increment thereon; EP represents an effective path; LB represents a locally optimal path; re represents the ratio of the total length of the effective path to the locally optimal path length;
the first determining module is used for comparing the existing global optimal path with the local optimal path and determining the current global optimal path;
The second updating module is used for updating the pheromone on the current global optimal path according to a second pheromone updating strategy;
the first counting detection module is used for adding one to the cycle number c and judging whether the cycle number c reaches n times or not; if the cycle times c do not reach n times, indicating all the modules to execute the corresponding steps again in sequence; wherein, the initial value of c is 0, n is used for representing the total circulation times, and the value of n is more than 1;
and the second determining module is used for determining a current global optimal path as a target path of the inspection robot if the cycle times c reach n times, wherein the target path is used for indicating the inspection robot to reach the end position along the target path from the initial position.
7. An apparatus for determining a path, the apparatus comprising: a processor, transceiver, communication interface and memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the apparatus, cause the apparatus to perform the method of determining a path of any of claims 1 to 5.
8. A computer readable storage medium having instructions stored therein which, when executed by a computer, perform the method of determining a path as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011141236.9A CN112183710B (en) | 2020-10-22 | 2020-10-22 | Method and device for determining path |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011141236.9A CN112183710B (en) | 2020-10-22 | 2020-10-22 | Method and device for determining path |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112183710A CN112183710A (en) | 2021-01-05 |
| CN112183710B true CN112183710B (en) | 2023-11-24 |
Family
ID=73923885
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011141236.9A Active CN112183710B (en) | 2020-10-22 | 2020-10-22 | Method and device for determining path |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112183710B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113050631B (en) * | 2021-03-11 | 2022-07-19 | 湖南大学 | Three-dimensional path planning method for electric automobile |
| CN113359761B (en) * | 2021-07-02 | 2023-07-18 | 广东电网有限责任公司 | Method, device and storage medium for planning inspection path of robot for transformer substation |
| CN113485360B (en) * | 2021-07-29 | 2023-05-19 | 福州大学 | AGV robot path planning method and system based on improved search algorithm |
| CN114995503B (en) * | 2022-06-16 | 2023-05-02 | 江西理工大学 | Unmanned aerial vehicle inspection path optimization method |
| CN115866071A (en) * | 2023-02-28 | 2023-03-28 | 中信云网有限公司 | Method and device for determining reporting path of initial equipment attribute information |
| CN116909320B (en) * | 2023-09-15 | 2023-12-01 | 众芯汉创(江苏)科技有限公司 | Electric power collaborative inspection strategy analysis method based on ant colony algorithm |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104199292A (en) * | 2014-08-11 | 2014-12-10 | 大连大学 | Method for planning space manipulator tail end effector avoidance path based on ant colony algorithm |
| CN106600049A (en) * | 2016-12-09 | 2017-04-26 | 国网北京市电力公司 | Path generation method and apparatus thereof |
| CN107272679A (en) * | 2017-06-15 | 2017-10-20 | 东南大学 | Paths planning method based on improved ant group algorithm |
| CN108413976A (en) * | 2018-01-23 | 2018-08-17 | 大连理工大学 | A kind of climbing robot intelligence paths planning method and system towards multi-state |
| WO2018156404A1 (en) * | 2017-02-23 | 2018-08-30 | Carrier Corporation | A control system and control method for building |
| CN108508745A (en) * | 2018-01-22 | 2018-09-07 | 中国铁道科学研究院通信信号研究所 | A kind of multiple target cycle tests collection optimization generation method |
| CN108563239A (en) * | 2018-06-29 | 2018-09-21 | 电子科技大学 | A kind of unmanned aerial vehicle flight path planing method based on potential field ant group algorithm |
| CN109164815A (en) * | 2018-09-06 | 2019-01-08 | 中国计量大学 | A kind of Autonomous Underwater Vehicle paths planning method based on improvement ant group algorithm |
| CN109214498A (en) * | 2018-07-10 | 2019-01-15 | 昆明理工大学 | Ant group algorithm optimization method based on search concentration degree and dynamic pheromone updating |
| CN109345008A (en) * | 2018-09-17 | 2019-02-15 | 摩佰尔（天津）大数据科技有限公司 | Automatic row's ship's method |
| 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 |
| CN111382543A (en) * | 2020-03-13 | 2020-07-07 | 中国海洋石油集团有限公司 | Offshore cluster well drilling sequence optimization method based on improved ant colony algorithm |
| CN111539676A (en) * | 2020-05-12 | 2020-08-14 | 香港理工大学 | Network entity logistics system suitable for cross-border electronic commerce |
| CN111694364A (en) * | 2020-06-30 | 2020-09-22 | 山东交通学院 | Hybrid algorithm based on improved ant colony algorithm and dynamic window method and applied to intelligent vehicle path planning |
| CN111695668A (en) * | 2020-06-16 | 2020-09-22 | 江苏师范大学 | Ant colony algorithm optimization method based on reverse learning |
-
2020
- 2020-10-22 CN CN202011141236.9A patent/CN112183710B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104199292A (en) * | 2014-08-11 | 2014-12-10 | 大连大学 | Method for planning space manipulator tail end effector avoidance path based on ant colony algorithm |
| CN106600049A (en) * | 2016-12-09 | 2017-04-26 | 国网北京市电力公司 | Path generation method and apparatus thereof |
| WO2018156404A1 (en) * | 2017-02-23 | 2018-08-30 | Carrier Corporation | A control system and control method for building |
| CN107272679A (en) * | 2017-06-15 | 2017-10-20 | 东南大学 | Paths planning method based on improved ant group algorithm |
| CN108508745A (en) * | 2018-01-22 | 2018-09-07 | 中国铁道科学研究院通信信号研究所 | A kind of multiple target cycle tests collection optimization generation method |
| CN108413976A (en) * | 2018-01-23 | 2018-08-17 | 大连理工大学 | A kind of climbing robot intelligence paths planning method and system towards multi-state |
| CN108563239A (en) * | 2018-06-29 | 2018-09-21 | 电子科技大学 | A kind of unmanned aerial vehicle flight path planing method based on potential field ant group algorithm |
| CN109214498A (en) * | 2018-07-10 | 2019-01-15 | 昆明理工大学 | Ant group algorithm optimization method based on search concentration degree and dynamic pheromone updating |
| CN109164815A (en) * | 2018-09-06 | 2019-01-08 | 中国计量大学 | A kind of Autonomous Underwater Vehicle paths planning method based on improvement ant group algorithm |
| CN109345008A (en) * | 2018-09-17 | 2019-02-15 | 摩佰尔（天津）大数据科技有限公司 | Automatic row's ship's method |
| CN111382543A (en) * | 2020-03-13 | 2020-07-07 | 中国海洋石油集团有限公司 | Offshore cluster well drilling sequence optimization method based on improved 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 |
| CN111539676A (en) * | 2020-05-12 | 2020-08-14 | 香港理工大学 | Network entity logistics system suitable for cross-border electronic commerce |
| CN111695668A (en) * | 2020-06-16 | 2020-09-22 | 江苏师范大学 | Ant colony algorithm optimization method based on reverse learning |
| CN111694364A (en) * | 2020-06-30 | 2020-09-22 | 山东交通学院 | Hybrid algorithm based on improved ant colony algorithm and dynamic window method and applied to intelligent vehicle path planning |
Non-Patent Citations (4)
| Title |
|---|
| An improved ant colony algorithm for robot path planning;Jianhua Liu等;《Soft Computing》;第21卷;5829-5839 * |
| 基于改进蚁群算法的移动机器人二维路径规划;刘泽等;《传感器与微系统》;第39卷(第10期);149-152 * |
| 基于蚁群的水下无线传感器网络能量路由协议;蒋华等;《微电子学与计算机》;第34卷(第08期);12-16 * |
| 复杂海洋环境中的无人船自主扫海路径规划研究;林玉龙;《中国优秀硕士学位论文全文数据库_工程科技Ⅱ辑》;C036-300 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112183710A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112183710B (en) | Method and device for determining path | |
| CN102147260B (en) | Electronic map matching method and device | |
| CN104050817B (en) | Speed limiting information base generation and speed limiting information detection method and system | |
| CN104021674B (en) | A kind of quick and precisely prediction vehicle method by road trip time | |
| CN107367278A (en) | A kind of indoor navigation method and equipment | |
| CN109163722B (en) | Humanoid robot path planning method and device | |
| CN103994768A (en) | Method for seeking for overall situation time optimal path under dynamic time varying environment | |
| CN107389079B (en) | High-precision path planning method and system | |
| CN111750862A (en) | Multi-region-based robot path planning method, robot and terminal equipment | |
| CN113188562B (en) | Path planning method and device for travelable area, electronic equipment and storage medium | |
| CN108827335B (en) | Shortest path planning method based on one-way search model | |
| Thoma et al. | Mapping, localization and path planning for image-based navigation using visual features and map | |
| CN111310992B (en) | Multi-unmanned aerial vehicle path optimization method for rapid evaluation after earthquake disaster | |
| US8532922B2 (en) | Method for simplifying a description of a route of travel | |
| CN110998663B (en) | Image generation method of simulation scene, electronic equipment and storage medium | |
| CN114815802A (en) | Unmanned overhead traveling crane path planning method and system based on improved ant colony algorithm | |
| CN110503009A (en) | Lane line tracking and Related product | |
| CN113485429A (en) | Route optimization method and device for air-ground cooperative traffic inspection | |
| CN110967019A (en) | Method for planning local path of robot and robot | |
| CN109523781A (en) | A kind of crossing prediction technique based on satellite positioning | |
| CN113110601A (en) | Method and device for optimizing power line inspection path of unmanned aerial vehicle | |
| CN110264517A (en) | A kind of method and system determining current vehicle position information based on three-dimensional scene images | |
| CN112764428A (en) | Spacecraft cluster reconstruction method and system | |
| CN114625162A (en) | Hybrid algorithm-based optimal path planning method, system and medium for unmanned aerial vehicle | |
| WO2024007807A1 (en) | Error correction method and apparatus, and mobile device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |