CN116820094A - Mobile robot three-dimensional path planning method and equipment based on improved ant colony algorithm - Google Patents
Mobile robot three-dimensional path planning method and equipment based on improved ant colony algorithm Download PDFInfo
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Abstract
The application provides a mobile robot three-dimensional path planning method and equipment based on an improved ant colony algorithm. The method comprises the following steps: establishing a grid three-dimensional working environment of the mobile robot, and selecting an initial node and a target node; iterative search until the iteration number T reaches a preset maximum iteration number T max Iterative search for t-th time: putting all ants into an initial node, assigning a random value to a dynamic selection variable q, and calculating a dynamic selection factor q 0 The method comprises the steps of carrying out a first treatment on the surface of the If q is less than or equal to q 0 Selecting the next node through the pheromone concentration among the nodes and the expected heuristic function information, if q is more than q 0 Selecting a next node through the transition probability among the nodes; discarding the ants trapped in the deadlock, and acquiring the pheromone increment of the path passed by the ants successfully reaching the target node by adopting a rewarding punishment mechanism; updating the pheromone concentration of each road section; from T max The shortest path to the target node is output in the multiple iterations. The path of the mobile robot is more in line with the actual application scene, and the global searching capability of the algorithm is improved,Search efficiency and convergence speed.
Description
Technical Field
The application relates to the technical field of artificial intelligence path planning, in particular to a mobile robot three-dimensional path planning method and equipment based on an improved ant colony algorithm.
Background
The path planning technology is a key technology of the mobile robot, is also a primary premise that the mobile robot can autonomously complete various tasks, and aims to plan a collision-free optimal path from a starting point to a target point for the mobile robot according to one index or a plurality of indexes with shortest distance, shortest time, lowest energy consumption and the like under the condition that the environment is known or part of the environment is known. At present, a great deal of research has been carried out at home and abroad and corresponding solutions are provided, such as an A-type algorithm, an artificial potential field method, a genetic algorithm, a particle swarm optimization algorithm, a neural network algorithm and the like, each algorithm has own advantages, however, most of the research is based on a two-dimensional space environment, and has some limitations on a three-dimensional space with complex environment and large scale, and the difficulty of path planning is increased due to the influence of special landforms of the three-dimensional environment, so that an ideal effect is difficult to obtain.
The ant colony algorithm is an intelligent optimization algorithm for simulating the foraging behavior of ants in nature, which is proposed by M.dorigo and other scholars. Ants secrete a pheromone during the foraging process and how much of the pheromone is inversely proportional to the path length that the ants travel, at the path nodes ants may tend to choose paths where the pheromone is larger. Along with the random exploration of the ant colony, the pheromone value on the optimal path is larger and larger, and finally the whole ant colony searches the optimal path, so that the ant colony algorithm has strong robustness and adaptability, and achieves good effect in solving the problem of three-dimensional space path planning.
The accuracy of the path planning algorithm is directly affected by the quality of the environment model establishment, so that the abstract expression of the three-dimensional environment is reasonably key to autonomous operation of the mobile robot in the three-dimensional environment. In the prior art, a grid method is adopted to expand a two-dimensional space into a three-dimensional space, as shown in fig. 1, a three-dimensional space ABCD-EFGH is constructed, a space coordinate system is established by taking an A point as an origin, an ABCD plane is on an XOZ plane, AE is equally divided into n parts along the Y axis direction by the origin O, AB is equally divided into m parts along the X axis direction, and AD is equally divided into l parts along the Z axis direction, so that n planes are obtained, and each plane is divided into m×l grids. The ABCD-EFGH space thus far may be represented by n×m×l grids, any one of which corresponds to a path node in which the ant colony algorithm performs path planning and simulation studies.
However, the ant colony algorithm has the problems of easy sinking of the locally optimal solution, slow convergence speed and the like in the three-dimensional space-based path planning. In the paper in the prior art, in the three-dimensional path planning research of the AGV trolley based on the improved ant colony algorithm (author Jin Xinxin, computer application and software, 2022, 39 (07): 275-280), the basic ant colony algorithm is improved by adding a tabu table so as to solve the problem of three-dimensional broken line path optimization, the improved ant colony algorithm can avoid sinking into local optimum, and the operation efficiency of the AGV trolley can be effectively improved. But the algorithm is relatively inefficient and not robust.
Disclosure of Invention
Aiming at the problems existing in the background technology, the problems of low convergence speed, low searching efficiency, easy sinking into local optimum and the like easily occur when the traditional ant colony algorithm is applied to carry out path planning in a three-dimensional environment, and the three-dimensional path planning method and equipment of the mobile robot based on the improved ant colony algorithm are provided.
In order to achieve the above object of the present application, according to a first aspect of the present application, there is provided a mobile robot three-dimensional path planning method based on an improved ant colony algorithm, comprising: step S1, establishing a grid three-dimensional working environment of the mobile robot, wherein each grid represents a path node, and selecting an initial node and a target node in the grid three-dimensional working environment; step S2, iterative search is carried out until the iteration times T reach the preset maximum iteration times T max ，t∈[1，T max ]The t-th iterative search includes: s21, putting all ants into an initial node, initializing a tabu table, assigning a random value to a dynamic selection variable q, and calculating a dynamic selection factor q 0 The method comprises the steps of carrying out a first treatment on the surface of the Step S22, each ant selects the next node until reaching the target node or a deadlock state occurs according to the following steps: if q is less than or equal to q 0 By means ofThe pheromone concentration between nodes and the expected heuristic function information select the next node, if q is more than q 0 Selecting a next node through the transition probability among the nodes; updating a tabu table; step S23, discarding the ants trapped in the deadlock, and acquiring the pheromone increment of the path passed by the ants successfully reaching the target node by adopting a rewarding punishment mechanism; step S24, updating the pheromone concentration of each road section; step S25, if T is less than T max Let t=t+1, return to execute step S21, if T is greater than or equal to T max From T max The shortest path to the target node is output in the multiple iterations.
In order to achieve the above object of the present application, according to a second aspect of the present application, there is provided an electronic apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the mobile robot three-dimensional path planning method based on the improved ant colony algorithm according to the first aspect of the present application.
In order to achieve the above object of the present application, according to a third aspect of the present application, there is provided a mobile robot provided with the electronic device according to the second aspect of the present application, the electronic device planning a three-dimensional path for the mobile robot.
The beneficial technical effects of the application are as follows: aiming at the problem of path planning of the mobile robot in the three-dimensional environment, a three-dimensional path planning method based on an improved ant colony algorithm is provided, so that the path of the mobile robot is more in line with the actual application scene; in each iterative search, a pseudo-random state transition rule is adopted to select the next node, a dynamic selection factor is defined to adaptively change the selection strategy of the next node, and a distance parameter is introduced into calculation of the transition probability of the next node, so that the global search capability and the search efficiency of the algorithm are improved, the pheromone increment of a path is updated based on a rewarding penalty mechanism, and the convergence speed of the algorithm is improved.
Drawings
FIG. 1 is a schematic diagram of a prior art grid three-dimensional work environment setup;
fig. 2 is a flow chart of a three-dimensional path planning method of a mobile robot based on an improved ant colony algorithm according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a three-dimensional spatial search pattern;
FIG. 4 is a flowchart of the overall algorithm of the mobile robot three-dimensional path planning method based on the improved ant colony algorithm in an application scenario;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, which is based on a mobile robot three-dimensional path planning method of an improved ant colony algorithm.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
In the description of the present application, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
In the description of the present application, unless otherwise specified and defined, it should be noted that the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanical or electrical, or may be in communication with each other between two elements, directly or indirectly through intermediaries, as would be understood by those skilled in the art, in view of the specific meaning of the terms described above.
The application provides a mobile robot three-dimensional path planning method based on an improved ant colony algorithm, which in an embodiment, as shown in fig. 1, comprises the following steps:
step S1, a grid three-dimensional working environment of the mobile robot is established, each grid represents a path node, and an initial node and a target node are selected in the grid three-dimensional working environment. The three-dimensional working environment of the grid is shown in fig. 1, wherein the initial node is a space grid where the initial position coordinates are located, and the target node is a space grid where the target position coordinates are located.
In the three-dimensional space, the algorithm search becomes extremely complex due to the expansion of the range of the selected nodes, and the deadlock phenomenon easily occurs, so that the algorithm convergence is poor and the global search is low. For this purpose, a search mode is used in which hierarchical progression is combined with grid-plane method as shown in fig. 3. Setting the starting node of the mobile robot as P S The target node is P G Selecting an X-axis as a main advancing direction, and prescribing a maximum transverse movement distance y of the robot max Maximum longitudinal travel distance z max That is, the ant searches for the next node for an reachable area, and the grid node set contained in the reachable area serves as the optional node set of the next node. First ant from the initial node P S Starting from the first plane, an optional node P is searched 1 (x 1 ，y 1 ，z 1 ) Then searching for a second optional node P in a second plane 2 (x 2 ，y 2 ，z 2 ) Ants select path nodes on each plane in turn until the target point P is searched G And (3) ending, searching an optimal path.
The step S1 also comprises a substep of initializing ant colony algorithm parameters, wherein the ant colony algorithm parameters comprise the concentration of the raster pheromone and the maximum iteration number T max The ant number M, the information heuristic factor alpha, the expected heuristic factor beta, the pheromone volatilization coefficient rho, a tabu list and the iteration number t. t has an initial value of 1. The tabu list is used for storing nodes which can not be selected when the ants search paths, such as pre-selectionKnown obstacle nodes, nodes that ants have walked on, etc., the tabu table initialization aims at emptying the nodes that ants have walked on.
Step S2, iterative search is carried out until the iteration times T reach the preset maximum iteration times T max ，t∈[1，T max ]The t-th iterative search includes:
s21, putting all ants into an initial node, initializing a tabu table, assigning a random value to a dynamic selection variable q, and calculating a dynamic selection factor q 0 。
Step S22, each ant selects the next node until reaching the target node or a deadlock state occurs according to the following steps, and records the path traversed by the ant successfully reaching the target node:
if q is less than or equal to q 0 Selecting the next node through the pheromone concentration among the nodes and the expected heuristic function information, if q is more than q 0 Selecting a next node through the transition probability among the nodes; the tabu table is updated. In step S22 and step S23, a pseudo-random state transition rule is adopted to perform path selection, and a dynamic selection factor q is defined 0 The self-adaptive updating selection proportion and strategy improve the global searching capability and searching efficiency of the algorithm.
Step S23, discarding the ants trapped in the deadlock, and acquiring the pheromone increment of the path passed by the ants successfully reaching the target node by adopting a rewarding punishment mechanism, thereby improving the algorithm convergence speed. Ants trapped in deadlock are preferably, but not limited to, ants that do not walk to the target node.
Step S24, updating the pheromone concentration of each road section.
Step S25, if T is less than T max Let t=t+1, return to execute step S21, if T is greater than or equal to T max From T max The shortest path to the target node is output in the multiple iterations.
In the present embodiment, it is further preferable that the dynamic selection factor q is calculated in step S21 according to the following formula 0 ：
In step S22, if q.ltoreq.q 0 Next node P of kth ant j The index j of (2) is:
wherein, allowed k A set of selectable nodes representing next nodes of the kth ant; the argmax (·) function is represented in the set allowed k Is solved for such that the expression [ tau ] ij′ (t)] α ×[H ij′ (t)]An optional node index where β takes the maximum value; i represents the current node P i Index of (2); alpha represents a preset information heuristic factor; beta represents a preset expected heuristic, τ ij′ (t) represents the current node P in the t-th iteration i Optional node P to the next node j′ Pheromone concentration, H ij′ (t) represents the current node P in the t-th iteration i Optional node P to the next node j′ Expected heuristic function information therebetween.
Preferably, the method comprises the steps of,wherein lambda is 1 Representing the origin node P s Optional node P to the next node j′ The relative importance of the Euclidean distance of (2); lambda (lambda) 2 Optional node P representing the next node j′ To the target node P G The relative importance of the inverse of the Euclidean distance; lambda (lambda) 3 Representing the current node P i Optional node P to the next node j′ The relative importance of the inverse of the Euclidean distance; d (D) Sj′ (t) is the starting node P in the t-th iteration S Optional node P to the next node j′ Is a Euclidean distance of (2); d (D) j′G (t)' is the optional node P of the next node in the t-th iteration j′ To the target node P G Inverse of the euclidean distance of (a); d (D) ij′ (t)' is the current node P in the t-th iteration i To the next node P j Is the inverse of the euclidean distance.
Start node P S Is (x) S ，y S ，z S ) Target node P G Is (x) G ，y G ，z G ) Current node P i Is (x) i ，y i ，z i ) Optional node P of the next node j′ Is (x) j′ ，y j′ ，z j′ )。
If q > q 0 The kth ant in the kth iteration is obtained from the current node P according to the following formula i Move to node P j′ Is a transition probability of (2):
current node P based on kth ant i Move to node P j′ Selecting the next node of the kth ant by means of roulette. k is E [1, M]。
Wherein τ ij′ (t) represents the current node P in the t-th iteration i To node P j′ Pheromone concentration, H ij″ (t) represents the current node P in the t-th iteration i To node P j″ Desired heuristic function information, H ij″ (t) can refer to H ij′ The calculation formula of (t) is calculated. i. j, j', j "are all indexes of grid nodes in the grid three-dimensional work environment. Distance of introductionSeparation parameter (D) Sj″ (t)、D j″G (t)′、D ij″ (t)') calculating H ij″ (t) using H ij″ And (t) calculating transition probability, so that the global searching capability and searching efficiency of the algorithm are improved.
q 0 Gradually decreasing with increasing iteration number, algorithm early q 0 The value is larger, the searching efficiency of the algorithm in the initial stage can be improved, and q is the later stage in the algorithm 0 The value is smaller, the global searching capability of the algorithm can be ensured, and q is smaller 0 The value can ensure that most ants carry out node selection by means of transition probability, thereby improving the global searching capability and searching efficiency of the algorithm.
In another embodiment, in step S23, obtaining the pheromone increment of the path traversed by the ant successfully reaching the target node using the rewarding penalty mechanism includes:
in step S231, the average path length and the optimal path of the ant passing path that successfully reaches the target node are obtained. Specifically, the path lengths of all ants successfully reaching the target node are obtained, the average value of the path lengths is obtained to obtain the average path length, and the path with the shortest path length is selected as the optimal path.
Step S232, obtaining the pheromone increment of the section (i, j) in the ant passing path that the kth ant successfully reaches the target node:
if the kth ant successfully reaches the target node passes through the optimal path, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:(update equation 1);
if the length of the path traversed by the kth ant successfully reaching the target node is greater than the optimal path length and less than the average path length, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:(update equation 2);
if the length of the path traversed by the kth ant successfully reaching the target node is greater than the average path length, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:
(update equation 3);
wherein, kappa 1 Represents a first constant, κ, greater than 0 2 Represents a second constant, κ, greater than 0 3 Represents a third constant, κ, greater than 0 1 ＞κ 2 ，κ 1 、κ 2 、κ 3 Preferably, but not limited to, a value in the range of 0.1 to 10.L (L) k The path length of the kth ant which successfully reaches the target node is represented, and Q represents the preset pheromone intensity;
step S233, superposing the pheromone increment brought by the ants successfully reaching the target node on the road section (i, j) to obtain the pheromone increment delta tau of the road section (i, j) in the (t+1) th iteration ij (t+1). If K ants successfully reach the target node pass through the road section (i, j), the following steps are provided:
in this embodiment, after each iteration is completed, the dead-locked ants are discarded, the average value of the path lengths of the ants successfully reaching the target node is calculated, namely the average path length, the pheromone increment on the path planned by the optimal ants is updated by the updating formula 1, and for the optimal path, the pheromone concentration rewards are given to enhance the guiding effect of the optimal solution on the subsequent iteration; if the path length of the ant is greater than the optimal ant path length but less than the average path length, updating the pheromone increment on the path of the ant according to an updating formula 2; if the path length of the ant is greater than the average path length of the ant, updating the pheromone increment on the path of the ant according to an updating formula 3, and reducing misleading effect of the poor path on subsequent iteration through punishment of the poor solution. After each iteration is finished, the pheromone increment is updated by the improved pheromone increment updating formula, and the pheromone concentration is updated by the pheromone global updating formula, so that the global convergence speed of the algorithm is increased.
In another embodiment, the updating of pheromone concentration is mainly used for simulating the accumulation and autonomous volatilization of natural ant pheromone with time. In the conventional ant colony algorithm, the pheromone volatilization coefficient ρ is a fixed constant, which is disadvantageous in ensuring global searching capability in the early stage of the algorithm and rapid convergence capability in the later stage of the algorithm. Therefore, in the present embodiment, the pheromone concentration is updated by using the adaptive pheromone volatilization coefficient in step S24, and step S24 includes:
step S241, calculating the pheromone volatilization coefficient of the t+1st iteration:
wherein ρ is min Representing the minimum value of the preset pheromone volatilization coefficient, wherein ρ (t) represents the pheromone volatilization coefficient of the t th iteration;
step S242, updating the pheromone concentration of the road segment (i, j) in the t+1th iteration:
τ ij (t+1)＝(1-ρ(t+1))×τ ij (t)+Δτ ij (t+1)；
wherein τ ij (t) represents the pheromone concentration of the road segment (i, j) in the t-th iteration; ρ (t+1) represents the pheromone volatility coefficient of the t+1st iteration.
The adaptive updating strategy is adopted for the pheromone volatilization coefficient rho, so that the global searching capability of the early stage of the algorithm can be ensured, the convergence speed of the middle and later stages of the algorithm can be improved, and the diversity of paths can be ensured while the convergence speed is improved. The pheromone volatilization coefficient rho monotonically decreases with the increase of the iteration times, and does not decrease after decreasing to the minimum value.
In this embodiment, further preferably, step S24 further includes:
step S243, the pheromone concentration τ of the road segment (i, j) in the t+1st iteration ij (t+1) is limited to a preset pheromone concentration interval. Let the pheromone concentration interval be [ tau ] min ，τ max ]，τ min Represents the lower limit of the pheromone concentration, τ max Represents the upper limit of the pheromone concentration, and the pheromone concentration tau is calculated according to the following formula ij (t+1) is limited to the pheromone concentration interval [ tau ] min ，τ max ]Early maturation and stagnation of the algorithm can be avoided:
the embodiment defines a self-adaptive pheromone volatilization coefficient, limits the upper and lower limits of the pheromone concentration, improves the algorithm globally and improves the convergence rate of the algorithm.
In an application scenario of the mobile robot three-dimensional path planning method based on the improved ant colony algorithm disclosed by the application, fig. 4 shows an overall algorithm flow chart, which specifically includes:
step1: and constructing a three-dimensional working environment and initializing ant colony algorithm parameters. The method specifically comprises the following steps: selecting a start position node P S Target position node P G Initializing the concentration of the raster pheromone and the maximum iteration number T of the algorithm max The ant number M, the information heuristic factor alpha, the expected heuristic factor beta, the pheromone volatilization coefficient rho and the like.
Step2: selecting ant path, putting M ants to the starting point P S Initializing a tabu table, and calculating expected heuristic function information of the optional node of the next node.
Step3: selecting a next node by adopting a random state transition rule, and updating a tabu table; judging whether one iteration is finished, if so, entering a Step4, otherwise, continuing to execute a Step3.
Step4: the pheromone delta is updated. When the first generation ants complete the search, discarding the ants which do not go to the target point, reinforcing the pheromone increment on the path passed by the optimal ants according to the updating formula 1, updating the pheromone increment on the path passed by the optimal ants (the path length is lower than the average value of the path length of the first generation ants but not the optimal ants) according to the updating formula 2, and weakening the pheromone on the path passed by the optimal ants (the path length is longer than the average value of the path length of the first generation ants) according to the updating formula 3.
Step5: outputting the optimal path. Judging whether the current iteration times are equal to the maximum iteration times, if so, outputting the optimal path length, and ending the algorithm; if the number of iterations is smaller than the maximum number of iterations, continuing to search for the next generation of iterations, searching for the optimal path, emptying all nodes in the tabu list, and turning to step2 to sequentially and circularly execute until the current number of iterations is equal to the maximum number of iterations.
It should be noted that the three-dimensional path planning method and the electronic device of the mobile robot based on the improved ant colony algorithm provided by the application are not limited to be used on the mobile robot, but can be applied to mobile tools such as unmanned automobiles, sweeping robots and the like.
The application also discloses an electronic device, which in an embodiment comprises at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the mobile robot three-dimensional path planning method based on the improved ant colony algorithm.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a mobile robot three-dimensional path planning method based on an improved ant colony algorithm according to an embodiment of the present application. The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in said memory 11 and executable on said processor 10, such as a mobile robot three-dimensional path planning method program based on an improved ant colony algorithm.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a combination of a graphics processor and various control chips, etc. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (for example, executing an image correction method program or the like) stored in the memory 11, and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in the electronic device and various types of data, such as codes of image correction method programs, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between said memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the above-described electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 10 through a power management device, so as to perform functions of charge management, discharge management, and power consumption management through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the examples are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Further, the integrated modules/units of the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The application also discloses a mobile robot, and the mobile robot is provided with the electronic equipment provided by the application, and the electronic equipment plans a three-dimensional path for the mobile robot. The electronic device is preferably, but not limited to, a central processor of a mobile robot or a newly provided processor.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Art ificialIntell igence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. The mobile robot three-dimensional path planning method based on the improved ant colony algorithm is characterized by comprising the following steps of:
step S1, establishing a grid three-dimensional working environment of the mobile robot, wherein each grid represents a path node, and selecting an initial node and a target node in the grid three-dimensional working environment;
step S2, iterative search is carried out until the iteration times T reach the preset maximum iteration times T max ，t∈[1，T max ]The t-th iterative search includes:
s21, putting all ants into an initial node, initializing a tabu table, assigning a random value to a dynamic selection variable q, and calculating a dynamic selection factor q 0 ；
Step S22, each ant selects the next node until reaching the target node or a deadlock state occurs according to the following steps:
if q is less than or equal toq 0 Selecting the next node through the pheromone concentration among the nodes and the expected heuristic function information, if q is more than q 0 Selecting a next node through the transition probability among the nodes; updating a tabu table;
step S23, discarding the ants trapped in the deadlock, and acquiring the pheromone increment of the path passed by the ants successfully reaching the target node by adopting a rewarding punishment mechanism;
step S24, updating the pheromone concentration of each road section;
step S25, if T is less than T max Let t=t+1, return to execute step S21, if T is greater than or equal to T max From T max The shortest path to the target node is output in the multiple iterations.
2. The mobile robot three-dimensional path planning method based on the improved ant colony algorithm according to claim 1, wherein the dynamic selection factor q is calculated in step S21 according to the following formula 0 ：
3. The mobile robot three-dimensional path planning method based on the improved ant colony algorithm according to claim 1 or 2, wherein in step S22, if q is less than or equal to q 0 Next node P of kth ant j The index j of (2) is:
wherein, allowed k A set of selectable nodes representing next nodes of the kth ant; the argmax (·) function is represented in the set allowed k Is solved for such that the expression [ tau ] ij′ (t)] α ×[H ij′ (t)] β A selectable node index that takes a maximum value; i represents the current node P i Index of (2); alpha represents a preset information heuristic factor; beta represents a preset valueIs defined by the desired heuristic factor, τ ij′ (t) represents the current node P in the t-th iteration i Optional node P to the next node j′ Pheromone concentration, H ij′ (t) represents the current node P in the t-th iteration i Optional node P to the next node j′ Expected heuristic function information therebetween.
4. A mobile robot three-dimensional path planning method based on an improved ant colony algorithm as claimed in claim 3, wherein the following is adopted
Wherein lambda is 1 Representing the origin node P s Optional node P to the next node j′ The relative importance of the Euclidean distance of (2); lambda (lambda) 2 Optional node P representing the next node j′ To the target node P G The relative importance of the inverse of the Euclidean distance; lambda (lambda) 3 Representing the current node P i Optional node P to the next node j′ The relative importance of the inverse of the Euclidean distance; d (D) Sj′ (t) is the starting node P in the t-th iteration S Optional node P to the next node j′ Is a Euclidean distance of (2); d (D) jG (t)' is the optional node P of the next node in the t-th iteration j′ To the target node P G Inverse of the euclidean distance of (a); d (D) ij′ (t)' is the current node P in the t-th iteration i To the next node P j Is the inverse of the euclidean distance.
5. The mobile robot three-dimensional path planning method based on the improved ant colony algorithm of claim 4, wherein if q > q 0 The kth ant in the kth iteration is obtained from the current node P according to the following formula i Move to node P j″ Is a transition probability of (2):
current node P based on kth ant i Move to node P j″ Selecting the next node of the kth ant by means of roulette.
6. The method for three-dimensional path planning of mobile robot based on improved ant colony algorithm according to claim 1, 2, 4 or 5, wherein in step S23, the acquiring the pheromone increment of the path traversed by the ant successfully reaching the target node by using the rewarding penalty mechanism comprises:
step S231, obtaining the average path length and the optimal path of the ant passing the path successfully reaching the target node;
step S232, obtaining the pheromone increment of the section (i, j) in the ant passing path that the kth ant successfully reaches the target node:
if the kth ant successfully reaches the target node passes through the optimal path, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:
if the length of the path traversed by the kth ant successfully reaching the target node is greater than the optimal path length and less than the average path length, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:
if the length of the path traversed by the kth ant successfully reaching the target node is greater than the average path length, the pheromone increment of the road section (i, j) caused by the kth ant is as follows:
wherein, kappa 1 Represents a first constant, κ, greater than 0 2 Represents a second constant, κ, greater than 0 3 Representing a third constant greater than 0，κ 1 ＞κ 2 ，L k The path length of the kth ant which successfully reaches the target node is represented, and Q represents the preset pheromone intensity;
step S233, superposing the pheromone increment brought by the ants successfully reaching the target node on the road section (i, j) to obtain the pheromone increment delta tau of the road section (i, j) in the (t+1) th iteration ij (t+1)。
7. The mobile robot three-dimensional path planning method according to claim 6, wherein step S24 comprises:
step S241, calculating the pheromone volatilization coefficient of the t+1st iteration:
wherein ρ is min Representing the minimum value of the preset pheromone volatilization coefficient, wherein ρ (t) represents the pheromone volatilization coefficient of the t th iteration;
step S242, updating the pheromone concentration of the road segment (i, j) in the t+1th iteration:
τ ij (t+1)＝(1-ρ(t+1))×τ ij (t)+Δτ ij (t+1)；
wherein τ ij (t) represents the pheromone concentration of the road segment (i, j) in the t-th iteration; ρ (t+1) represents the pheromone volatility coefficient of the t+1st iteration.
8. The mobile robot three-dimensional path planning method according to claim 7, wherein step S24 further comprises:
step S243, the pheromone concentration τ of the road segment (i, j) in the t+1st iteration ij (t+1) is limited to a preset pheromone concentration interval.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the mobile robot three-dimensional path planning method based on the improved ant colony algorithm according to any one of claims 1 to 8.
10. A mobile robot, wherein the mobile robot is provided with the electronic device of claim 9, and wherein the electronic device plans a three-dimensional path for the mobile robot.
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