CN113778093A - AMR autonomous mobile robot path planning method based on improved sparrow search algorithm - Google Patents
AMR autonomous mobile robot path planning method based on improved sparrow search algorithm Download PDFInfo
- Publication number
- CN113778093A CN113778093A CN202111073456.7A CN202111073456A CN113778093A CN 113778093 A CN113778093 A CN 113778093A CN 202111073456 A CN202111073456 A CN 202111073456A CN 113778093 A CN113778093 A CN 113778093A
- Authority
- CN
- China
- Prior art keywords
- sparrow
- search
- population
- fitness
- mobile robot
- 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.)
- Pending
Links
- 241000287127 Passeridae Species 0.000 title claims abstract description 120
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000010845 search algorithm Methods 0.000 title claims abstract description 29
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 25
- 230000006870 function Effects 0.000 claims abstract description 21
- 230000002431 foraging effect Effects 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims abstract description 12
- 238000002922 simulated annealing Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 8
- 238000004590 computer program Methods 0.000 claims description 13
- 230000004888 barrier function Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000004904 shortening Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 9
- 238000005070 sampling Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 241000282461 Canis lupus Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to an AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm. Based on a sparrow search algorithm, path search is carried out in a simulated sparrow foraging mode, the passing position of each sparrow represents a possible track, in the foraging process, a reverse learning strategy is utilized to optimize an initial sparrow population, the initial solution quality is improved, and the local search capability of the algorithm is enhanced; meanwhile, the Metropolis criterion in the hybrid simulated annealing algorithm judges whether to accept a new solution, so that the algorithm can jump out of local optimum and the global search capability is enhanced. Sparrows are divided into seekers and followers, the seekers are responsible for searching for food in the sparrow population and providing foraging areas and directions for the sparrow population, and the followers use discoverers to obtain the food. The fitness of the sparrow individual is an objective function value, the optimal position of the sparrow population is obtained by updating and comparing the fitness of the seeker and the follower in the sparrow population, and after continuous iteration optimization, the returned optimal solution is the optimal track.
Description
Technical Field
The invention belongs to the field of robot navigation planning, and particularly relates to an AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm.
Background
The AMR autonomous mobile robot is an unmanned logistics carrier, belongs to an intelligent AMR autonomous mobile robot, and plays an important role in the national modernization construction process. The AMR autonomous mobile robot can determine position and environmental information through the vehicle-mounted sensor according to an instruction given by the upper computer, automatically runs along a planned route and stops at a specified position according to a pre-designed program, has the functions of automatic obstacle avoidance and navigation and the like, greatly liberates labor force, and improves the transportation efficiency. Meanwhile, the safety protection and navigation obstacle avoidance function are more perfect, and the intelligent scheduling management is matched, so that the safety and reliability of industrial logistics transportation are greatly improved, and the path planning function is a core support for the AMR autonomous mobile robot to complete tasks and realize autonomous navigation in a complex environment.
Path planning methods are generally considered to be classified into search-based path planning methods, sampling-based path planning algorithms, and intelligent algorithms. Search-based path planning, such as Dijkstra, a, and classical variant algorithms thereof, such as ARA, WeightedA, Theta, lazytita, etc., all have the essence that on an already constructed map, a cost function is first defined, and then each node and edge are searched to find a path with minimum cost, and the core of the search-based path planning lies in the design of a heuristic function, which is easy to expand many nodes with the same cost. And a sampling-based path planning algorithm, RRT and the like are suitable for solving the problem of path planning of the multi-degree-of-freedom robot in a complex environment and a dynamic environment. By sampling the state space, a feasible solution connecting the starting point and the end point can be guaranteed, but the sampling process is unstable sampling of the whole space, so that the efficiency is low, real-time solution is difficult to realize in a complex scene, and the optimality of the solution is guaranteed. Intelligent algorithms such as tabu search algorithm, particle swarm optimization, gray wolf algorithm, etc. often require a lot of time to perform iterative techniques, have poor real-time performance, and are often used in combination with other algorithms. The search-based path planning method is good in guidance and high in real-time performance, is suitable for being used in a low-dimensional state space, and is widely applied to the working environment of the AMR autonomous mobile robot. However, the existing search-based algorithm still has the problems of large memory consumption, low efficiency, difficulty in robot execution of a path, and the like in the path search in a large scene.
Disclosure of Invention
The invention aims to provide an AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm,
in order to achieve the purpose, the technical scheme of the invention is as follows: an AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm is characterized in that the AMR autonomous mobile robot carries out path search by adopting a mode of simulating sparrow foraging based on the sparrow search algorithm, namely, sparrow populations start iterative search from a starting point and finish the search to a target point, wherein the passing position of each sparrow represents a possible track, in the process of foraging, the sparrows are divided into searchers and followers, the searchers are responsible for searching food in the populations and providing foraging areas and directions for the whole sparrow populations, and the followers use discoverers to obtain the food; the fitness of the sparrow individual is an objective function value, the optimal position of the sparrow population is obtained by updating and comparing the fitness of the seeker and the follower in the sparrow population, and after continuous iteration optimization, the returned optimal solution is the optimal track.
In an embodiment of the invention, the method comprises the following steps:
1) dividing a two-dimensional environment map into binary grid cells with the same size, wherein each grid uses NijAnd each grid information is expressed as:
wherein N isijWhen the grid is equal to 0, the grid is represented as a free space without obstacles; n is a radical ofijWhen the grid is 1, the position of the current grid is indicated to have an obstacle;
2) the AMR autonomous mobile robot exists as a mass point in a two-dimensional environment and can only search and move in 8 directions, namely, up, down, left, right, left-up, left-down, right-up and right-down; assuming that the side length of each grid is 1, the single-step moving distance of the AMR autonomous mobile robot is 1 or
3) Acquiring starting point information of a path, initializing relevant parameters, generating an initial population and acquiring the position X of the first n/2 sparrows with higher fitnessn/2Meanwhile, the positions of the opposite points of the front n/2 sparrows with higher fitness are obtained through reverse learning
Generating a new initial population
Position of the opposite point
The calculation rule is described as:
wherein Lb is a lower boundary of the search space and Ub is an upper boundary of the search space;
4) obtaining the current optimal and worst sparrow individuals;
5) updating the positions of sparrows and calculating the fitness; the sparrow population is divided into explorers and followers, and the positions of the explorers are updated as follows:
where t is the current iteration, j is 1, 2.. d,
is the position of the jth dimension of the t generation of i sparrows, and alpha is [0,1]]Random number of (2), TmaxFor the maximum number of iterations, Q is a random number following a normal distribution, L ═ 1,1, …,1]1×d，R2∈[0,1]For the alarm value, ST ∈ [0.5,1.0 ]]Is an alarm threshold; when R2 < ST, no danger exists around sparrows, namely, no barrier grids exist, and the seeker starts searching; when R2 is larger than or equal to ST, sparrows are found to be dangerous, namely, barrier grids exist, and all sparrows are transferred to a safe area;
the follower obtains food by monitoring the seeker and following the seeker with higher fitness, and the position of the follower is updated as follows:
wherein,
for the best position occupied by the (t +1) th generation seeker,
for the global worst position in the population of the t generation, a represents a matrix, each element in the matrix is randomly assigned 1 or-1, and the calculation mode is as follows:
A+＝AT(AAT)-1
in the iterative optimization process, assuming that dangerous sparrows account for 10% -20% of the total number of sparrows, the effect on the overall sparrow position can be expressed as:
wherein,
beta is a step size control parameter, and follows normal distribution with mean value of 0 and variance of 1, and K is [0,1]]Random number of fiFitness of the current position of sparrows, fgFor global optimal fitness, fwIs the global worst fitness, and epsilon is a minimum value which is not zero;
6) judging whether to update the global optimal position according to the fitness value;
7) judging whether to accept a new solution by using a Metropolis criterion in a simulated annealing algorithm, wherein the Metropolis criterion is described as follows:
wherein Te is the current temperature, x is the current sparrow position, x ' is the candidate sparrow position, and by comparing P with the random number in the interval [0,1], if the random number is more than P, the candidate sparrow position x ' is abandoned, otherwise x ' is received;
8) and (4) judging whether the maximum iteration times are reached, if so, outputting the optimal sparrow position to form a search path, and if not, skipping to the step 4).
In an embodiment of the present invention, a cost function, i.e., a fitness function, of a search path of a sparrow search algorithm is as follows:
when the grid node i +1 is an obstacle grid, the Length is M, and M is the total number of the grids in the current environment; when the i +1 grid node is a free grid, considering the moving direction of the AMR autonomous mobile robot, when the direction is up, down, left, and right, Length is 1, when the direction is up, down, up, and down,
in an embodiment of the invention, the positions of the sparrows are updated by adopting a reverse learning strategy, so that the quality of the sparrows is improved, the fitness value of sparrow individuals is reduced, and the path searching time is shortened.
In an embodiment of the invention, the Metropolis criterion in the simulated annealing algorithm is used for judging whether to accept new solutions, so that the phenomenon that the sparrow population is stagnated or falls into local optimum in the optimizing process is avoided, and the searching capability is improved.
The invention also provides an AMR autonomous mobile robot path planning system based on the improved sparrow search algorithm, which comprises a memory, a processor and computer program instructions stored on the memory and capable of being operated by the processor, wherein when the processor operates the computer program instructions, the steps of the method can be realized.
Compared with the prior art, the invention has the following beneficial effects:
the method is based on a sparrow search algorithm, simulates a sparrow foraging mode, introduces a reverse learning strategy and a Metropolis criterion in a simulated annealing algorithm, improves the path search capability of the AMR autonomous mobile robot, can effectively jump out local optimum, and stably and efficiently plans a safe and feasible AMR autonomous mobile robot motion track with optimal cost and constraint satisfaction.
Drawings
Fig. 1 is an AMR autonomous mobile robot according to an embodiment of the present invention.
Fig. 2 is a motion pattern of an embodiment of the present invention.
Fig. 3 is a flow chart of a method implementation of an embodiment of the invention.
Fig. 4 is a diagram comparing the path search effects of the conventional method and the method in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment provides an AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm, based on the sparrow search algorithm, path search is carried out in a mode of simulating sparrow foraging, the passing position of each sparrow represents a possible track, in the process of foraging, a reverse learning strategy is utilized to optimize the initial population of the sparrow, the quality of the initial solution is improved, and the local search capability of the algorithm is enhanced; meanwhile, the Metropolis criterion in the hybrid simulated annealing algorithm judges whether to accept a new solution, so that the algorithm can jump out of local optimum and the global search capability is enhanced. Sparrows are divided into seekers and followers, the seekers are responsible for searching for food in the population and providing foraging areas and directions for the whole sparrow population, and the followers use discoverers to obtain food. The fitness of the sparrow individual is an objective function value, the optimal position of the sparrow population is obtained by updating and comparing the fitness of the seeker and the follower in the sparrow population, and after continuous iteration optimization, the returned optimal solution is the optimal track.
As shown in fig. 3, the method of the embodiment of the present invention specifically includes the following steps:
1) dividing a two-dimensional environment map into binary grid cells with the same size, wherein each grid uses NijAnd each grid information is expressed as:
wherein N isijWhen the grid is equal to 0, the grid is represented as a free space without obstacles; n is a radical ofijWhen the grid is 1, the position of the current grid is indicated to have an obstacle;
2) an AMR autonomous mobile robot (as shown in fig. 1) exists as a particle in a two-dimensional environment and can only search and move in 8 directions, i.e., up, down, left, right, left-up, left-down, right-up, and right-down; the moving direction of the AMR autonomous mobile robot is shown in fig. 2. Assuming that the side length of each grid is 1, the single-step moving distance of the AMR autonomous mobile robot is 1 or
3) Acquiring starting point information of a path, initializing relevant parameters, generating an initial population and acquiring the position X of the first n/2 sparrows with higher fitnessn/2Meanwhile, the positions of the opposite points of the front n/2 sparrows with higher fitness are obtained through reverse learning
Generating a new initial population
Position of the opposite point
The calculation rule is described as:
wherein Lb is a lower boundary of the search space and Ub is an upper boundary of the search space;
4) obtaining the current optimal and worst sparrow individuals;
5) updating the positions of sparrows and calculating the fitness; the sparrow population is divided into explorers and followers, and the positions of the explorers are updated as follows:
where t is the current iteration, j is 1, 2.. d,
is the position of the jth dimension of the t generation of i sparrows, and alpha is [0,1]]Random number of (2), TmaxFor the maximum number of iterations, Q is a random number following a normal distribution, L ═ 1,1, …,1]1×d，R2∈[0,1]For the alarm value, ST ∈ [0.5,1.0 ]]Is an alarm threshold; when R2 < ST, no danger exists around sparrows, namely, no barrier grids exist, and the seeker starts searching; when R2 is larger than or equal to ST, sparrows are found to be dangerous, namely, barrier grids exist, and all sparrows are transferred to a safe area;
the follower obtains food by monitoring the seeker and following the seeker with higher fitness, and the position of the follower is updated as follows:
wherein,
for the best position occupied by the (t +1) th generation seeker,
for the global worst position in the population of the t generation, a represents a matrix, each element in the matrix is randomly assigned 1 or-1, and the calculation mode is as follows:
A+＝AT(AAT)-1
in the iterative optimization process, assuming that dangerous sparrows account for 10% -20% of the total number of sparrows, the effect on the overall sparrow position can be expressed as:
wherein,
beta is a step size control parameter, and follows normal distribution with mean value of 0 and variance of 1, and K is [0,1]]Random number of fiFitness of the current position of sparrows, fgFor global optimal fitness, fwIs the global worst fitness, and epsilon is a minimum value which is not zero;
6) judging whether to update the global optimal position according to the fitness value;
7) judging whether to accept a new solution by using a Metropolis criterion in a simulated annealing algorithm, wherein the Metropolis criterion is described as follows:
wherein Te is the current temperature, x is the current sparrow position, x ' is the candidate sparrow position, and by comparing P with the random number in the interval [0,1], if the random number is more than P, the candidate sparrow position x ' is abandoned, otherwise x ' is received;
8) and (4) judging whether the maximum iteration times are reached, if so, outputting the optimal sparrow position to form a search path, and if not, skipping to the step 4).
In an embodiment of the present invention, a cost function, i.e., a fitness function, of a search path of a sparrow search algorithm is as follows:
when the grid node i +1 is an obstacle grid, the Length is M, and M is the total number of the grids in the current environment; when the i +1 grid node is a free grid, considering the moving direction of the AMR autonomous mobile robot, when the direction is up, down, left, and right, Length is 1, when the direction is up, down, up, and down,
the cost function, namely the fitness function, of the search path of the sparrow search algorithm is as follows:
when the grid node i +1 is an obstacle grid, the Length is M, and M is the total number of the grids in the current environment; when the i +1 grid node is a free grid, considering the moving direction of the AMR autonomous mobile robot, according to fig. 2, when the direction is up, down, left, and right, Length is 1, when the direction is up, down, up, and down,
the invention also correspondingly provides an AMR autonomous mobile robot path planning system based on the improved sparrow search algorithm, which comprises a memory, a processor and computer program instructions stored on the memory and capable of being operated by the processor, wherein when the processor operates the computer program instructions, the steps of the method can be realized.
Fig. 4 shows the difference between the method of the present invention and the existing sparrow search algorithm in the path search effect. Fig. 4(a) shows a sparrow search algorithm, and fig. 4(b) shows the method of the present invention. As can be seen from fig. 4, compared with the existing sparrow search algorithm, the method of the present invention can effectively shorten the search path and further obtain a better path planning.
With the continuous improvement of the complexity of the application scene, the requirement on the environmental adaptability of the robot path planning method is higher and higher, and meanwhile, with the increase of the search space, the number of nodes to be searched and stored is also greatly increased. The method enhances the local searching capability of the algorithm by improving the quality of the initial solution, and judges whether to accept a new solution by utilizing the Metropolis criterion in the simulated annealing algorithm, so that the algorithm can jump out of the local optimum and the global searching capability is enhanced.
Since the planned path is a series of broken line paths, the failure to consider kinematic constraints of the robot easily leads to unnecessary steering of the path. In order to solve the phenomenon, the invention introduces the concept of efficiency factors, and reduces unnecessary expansion by designing a penalty term of path straightness.
The most popular problem of the existing search algorithm is that the higher the precision is, the more node data needs to be stored, when a grid map is used for path search, once the size of the map is larger or the precision is too high, a large number of nodes need to be maintained, performance waste is caused, most of the nodes obtained by the algorithm are not significant for the actual motion trajectory of the robot, and the troublesome data amount brings troubles to later-stage robot execution tasks. Therefore, the invention extracts the characteristics of the acquired series of path track nodes, reduces the low algorithm efficiency caused by storage and maintenance, and provides key nodes for subsequently optimizing the actual motion track of the robot.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (6)
1. An AMR autonomous mobile robot path planning method based on an improved sparrow search algorithm is characterized in that the AMR autonomous mobile robot carries out path search by adopting a mode of simulating sparrow foraging based on the sparrow search algorithm, namely, a sparrow population starts iterative search from a starting point and finishes the search to a target point, wherein the passing position of each sparrow represents a possible track, the sparrows are divided into an explorer and a follower in the foraging process, the explorer is responsible for searching food in the population and providing a foraging area and direction for the whole sparrow population, and the follower acquires the food by utilizing the finder; the fitness of the sparrow individual is an objective function value, the optimal position of the sparrow population is obtained by updating and comparing the fitness of the seeker and the follower in the sparrow population, and after continuous iteration optimization, the returned optimal solution is the optimal track.
2. The AMR autonomous mobile robot path planning method based on the improved sparrow search algorithm as claimed in claim 1, characterized by comprising the following steps:
1) dividing a two-dimensional environment map into binary grid cells with the same size, wherein each grid uses NijAnd each grid information is expressed as:
wherein N isijWhen the grid is equal to 0, the grid is represented as a free space without obstacles; n is a radical ofijWhen the grid is 1, the position of the current grid is indicated to have an obstacle;
2) the AMR autonomous mobile robot exists as a mass point in a two-dimensional environment and can only search and move in 8 directions, namely, up, down, left, right, left-up, left-down, right-up and right-down; assuming that the side length of each grid is 1, the single-step moving distance of the AMR autonomous mobile robot is 1 or
3) Acquiring starting point information of a path, initializing relevant parameters, generating an initial population and acquiring the position X of the first n/2 sparrows with higher fitnessn/2Meanwhile, the positions of the opposite points of the front n/2 sparrows with higher fitness are obtained through reverse learning
Generating a new initial population
Position of the opposite point
The calculation rule is described as:
wherein Lb is a lower boundary of the search space and Ub is an upper boundary of the search space;
4) obtaining the current optimal and worst sparrow individuals;
5) updating the positions of sparrows and calculating the fitness; the sparrow population is divided into explorers and followers, and the positions of the explorers are updated as follows:
where t is the current iteration, j is 1, 2.. d,
is the position of the jth dimension of the t generation of i sparrows, and alpha is [0,1]]Random number of (2), TmaxFor the maximum number of iterations, Q is a random number following a normal distribution, L ═ 1,1, …,1]1×d，R2∈[0,1]For the alarm value, ST ∈ [0.5,1.0 ]]Is an alarm threshold; when R2 < ST, no danger exists around sparrows, namely, no barrier grids exist, and the seeker starts searching; when R2 is larger than or equal to ST, sparrows are found to be dangerous, namely, barrier grids exist, and all sparrows are transferred to a safe area;
the follower obtains food by monitoring the seeker and following the seeker with higher fitness, and the position of the follower is updated as follows:
wherein,
for the best position occupied by the (t +1) th generation seeker,
for the global worst position in the population of the t generation, a represents a matrix, each element in the matrix is randomly assigned 1 or-1, and the calculation mode is as follows:
A+＝AT(AAT)-1
in the iterative optimization process, assuming that dangerous sparrows account for 10% -20% of the total number of sparrows, the effect on the overall sparrow position can be expressed as:
wherein,
beta is a step size control parameter, and follows normal distribution with mean value of 0 and variance of 1, and K is [0,1]]Random number of fiFitness of the current position of sparrows, fgFor global optimal fitness, fwIs the global worst fitness, and epsilon is a minimum value which is not zero;
6) judging whether to update the global optimal position according to the fitness value;
7) judging whether to accept a new solution by using a Metropolis criterion in a simulated annealing algorithm, wherein the Metropolis criterion is described as follows:
wherein Te is the current temperature, x is the current sparrow position, x ' is the candidate sparrow position, and by comparing P with the random number in the interval [0,1], if the random number is more than P, the candidate sparrow position x ' is abandoned, otherwise x ' is received;
8) and (4) judging whether the maximum iteration times are reached, if so, outputting the optimal sparrow position to form a search path, and if not, skipping to the step 4).
3. The AMR autonomous mobile robot path planning method based on the improved sparrow search algorithm as claimed in claim 2, characterized in that the cost function, i.e. fitness function, of the sparrow search algorithm search path is:
when the grid node i +1 is an obstacle grid, the Length is M, and M is the total number of the grids in the current environment; when the i +1 grid node is a free grid, considering the moving direction of the AMR autonomous mobile robot, when the direction is up, down, left, and right, Length is 1, when the direction is up, down, up, and down,
4. the AMR autonomous mobile robot path planning method based on the improved sparrow search algorithm as claimed in claim 2, characterized in that a reverse learning strategy is adopted to update the sparrow population position, thereby improving the sparrow population quality, reducing the fitness value of sparrow individuals and shortening the path search time.
5. The AMR autonomous mobile robot path planning method based on the improved sparrow search algorithm as recited in claim 2, wherein Metropolis criterion in the simulated annealing algorithm is used to determine whether to accept a new solution, so as to avoid stagnation or local optimum trapping of sparrow population in the optimizing process and improve the search capability.
6. An AMR autonomous mobile robot path planning system based on an improved sparrow search algorithm, comprising a memory, a processor and computer program instructions stored on the memory and executable by the processor, the computer program instructions when executed by the processor being capable of implementing the method steps of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073456.7A CN113778093A (en) | 2021-09-14 | 2021-09-14 | AMR autonomous mobile robot path planning method based on improved sparrow search algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073456.7A CN113778093A (en) | 2021-09-14 | 2021-09-14 | AMR autonomous mobile robot path planning method based on improved sparrow search algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113778093A true CN113778093A (en) | 2021-12-10 |
Family
ID=78843553
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111073456.7A Pending CN113778093A (en) | 2021-09-14 | 2021-09-14 | AMR autonomous mobile robot path planning method based on improved sparrow search algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113778093A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114460941A (en) * | 2022-02-09 | 2022-05-10 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN114708435A (en) * | 2022-04-24 | 2022-07-05 | 湖南科技大学 | Obstacle size prediction and uncertainty analysis method based on semantic segmentation |
| CN115657693A (en) * | 2022-12-28 | 2023-01-31 | 安徽省交通航务工程有限公司 | Ship path optimization method, electronic device and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256349A (en) * | 2020-10-26 | 2021-01-22 | 重庆邮电大学 | SSA-SA algorithm-based mobile edge computing task unloading method |
| CN112329934A (en) * | 2020-11-17 | 2021-02-05 | 江苏科技大学 | RBF neural network optimization algorithm based on improved sparrow search algorithm |
| CN112461247A (en) * | 2020-12-16 | 2021-03-09 | 广州大学 | Robot path planning method based on self-adaptive sparrow search algorithm |
-
2021
- 2021-09-14 CN CN202111073456.7A patent/CN113778093A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256349A (en) * | 2020-10-26 | 2021-01-22 | 重庆邮电大学 | SSA-SA algorithm-based mobile edge computing task unloading method |
| CN112329934A (en) * | 2020-11-17 | 2021-02-05 | 江苏科技大学 | RBF neural network optimization algorithm based on improved sparrow search algorithm |
| CN112461247A (en) * | 2020-12-16 | 2021-03-09 | 广州大学 | Robot path planning method based on self-adaptive sparrow search algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 李敦桥: "一种混合麻雀搜索算法", 《电脑知识与技术》, vol. 17, no. 5, 28 February 2021 (2021-02-28), pages 232 - 234 * |
| 游晓明 等: "移动机器人SLAM 目标跟踪及路径规划", 北京航空航天大学出版社, pages: 129 - 131 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114460941A (en) * | 2022-02-09 | 2022-05-10 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN114460941B (en) * | 2022-02-09 | 2023-10-24 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN114708435A (en) * | 2022-04-24 | 2022-07-05 | 湖南科技大学 | Obstacle size prediction and uncertainty analysis method based on semantic segmentation |
| CN114708435B (en) * | 2022-04-24 | 2024-07-19 | 湖南科技大学 | Obstacle size prediction and uncertainty analysis method based on semantic segmentation |
| CN115657693A (en) * | 2022-12-28 | 2023-01-31 | 安徽省交通航务工程有限公司 | Ship path optimization method, electronic device and storage medium |
| US11941553B1 (en) | 2022-12-28 | 2024-03-26 | Hefei University Of Technology | Methods, electronic devices and storage media for ship route optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113778093A (en) | AMR autonomous mobile robot path planning method based on improved sparrow search algorithm | |
| US10365110B2 (en) | Method and system for determining a path of an object for moving from a starting state to an end state set avoiding one or more obstacles | |
| Ding et al. | Epsilon: An efficient planning system for automated vehicles in highly interactive environments | |
| CN110989352B (en) | Group robot collaborative search method based on Monte Carlo tree search algorithm | |
| CN111024080B (en) | Unmanned aerial vehicle group-to-multi-mobile time-sensitive target reconnaissance path planning method | |
| CN112229419B (en) | Dynamic path planning navigation method and system | |
| CN112344945B (en) | Indoor distribution robot path planning method and system and indoor distribution robot | |
| CN114489052B (en) | Path planning method for improving RRT algorithm reconnection strategy | |
| Zhang et al. | A unified framework integrating decision making and trajectory planning based on spatio-temporal voxels for highway autonomous driving | |
| CN114296474A (en) | Unmanned aerial vehicle path planning method and system based on path time cost | |
| CN109341698B (en) | Path selection method and device for mobile robot | |
| CN113485360A (en) | AGV robot path planning method and system based on improved search algorithm | |
| Yang et al. | Mobile robot path planning based on enhanced dynamic window approach and improved A∗ algorithm | |
| Gu et al. | Path planning for mobile robot in a 2.5‐dimensional grid‐based map | |
| CN114815801A (en) | Adaptive environment path planning method based on strategy-value network and MCTS | |
| Julius Fusic et al. | Improved RRT* algorithm-based path planning for unmanned aerial vehicle in a 3D metropolitan environment | |
| Kundu et al. | Modified shuffled frog leaping algorithm based 6DOF motion for underwater mobile robot | |
| Xu et al. | An efficient recharging task planning method for multi-robot autonomous recharging problem | |
| Chen et al. | STExplorer: A hierarchical autonomous exploration strategy with spatio-temporal awareness for aerial robots | |
| CN113961016B (en) | Unmanned aerial vehicle dynamic target track planning method and system based on A-algorithm | |
| Ren et al. | Intelligent path planning and obstacle avoidance algorithms for autonomous vehicles based on enhanced rrt algorithm | |
| Wang et al. | SRM: An efficient framework for autonomous robotic exploration in indoor environments | |
| Li et al. | DF-FS based path planning algorithm with sparse waypoints in unstructured terrain | |
| Dang et al. | A path planning method for indoor robots based on partial & global A-star algorithm | |
| Chen et al. | iA $^* $: Imperative Learning-based A $^* $ Search for Pathfinding |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211210 |