Mobile robot path planning method based on improved eagle optimization algorithm

Abstract

The invention relates to a mobile robot path planning method based on an improved eagle optimization algorithm, and belongs to the technical field of robot path planning, and the method comprises the following steps: S1, constructing a two-dimensional grid map according to map information, and marking a starting point, a target point and an obstacle; s2, constructing a multi-objective function, and evaluating the advantages and disadvantages of the path according to the path length and the path smoothness; s3, using uniformly distributed Logistic chaotic mapping and combining with an LOS sight line detection algorithm to initialize a population; s4, improving an egret optimization algorithm by adopting a self-adaptive nonlinear inertia factor and a doliolaria group algorithm, selecting a corresponding global search mode and a local search mode according to different iterations, and updating the position and the target function value of an egret individual; and S5, outputting the optimal path.

Description

Technical Field

[0001] The invention belongs to the technical field of robot path planning, and relates to a mobile robot path planning method based on an improved egret optimization algorithm. Background Art

[0002] With the acceleration of a new round of scientific and technological revolution and industrial transformation, the technology of mobile robots has been continuously upgraded and iterated, and the breadth and depth of its application fields have continued to expand. Autonomous navigation technology is the basis for mobile robots to safely and effectively complete various complex tasks, and path planning is the core of autonomous navigation technology. Path planning refers to the robot planning a safe and collision-free path from the starting point to the target point with the shortest distance, the least time and the lowest energy consumption in a known or unknown environment. The performance of its algorithm directly affects the robot's operating efficiency, safety and motion control. In addition, it can also be used as an indicator to measure the intelligence level of mobile robots.

[0003] However, it is more difficult to determine the best path in an environment with many obstacles. Therefore, mobile robots encounter many challenges in large-scale complex environments, mainly focusing on how to quickly plan a reasonable path and stable autonomous movement. Traditional algorithms have problems such as large computational complexity and long running time in large-scale complex environments. With the rise of artificial intelligence technology in recent years, more and more intelligent bionic algorithms have been proposed and applied in robot path planning. Intelligent bionic algorithms can find high-quality solutions when traditional methods cannot provide satisfactory results.

[0004] The Egret Eagle Optimization Algorithm is a new intelligent bionic optimization algorithm proposed by Fu Ying and others in 2024. It is inspired by the hunting and evasion of natural enemies by the Egret Eagle. In the field of path planning, compared with 15 other algorithms such as differential evolution and whale optimization, the Egret Eagle Optimization Algorithm has a faster convergence speed and higher convergence accuracy, and the obtained trajectory is the shortest and relatively smooth. The Egret Eagle Optimization Algorithm can effectively improve the efficiency of path planning and shows certain advantages. However, the Egret Eagle Optimization Algorithm still has some shortcomings in its construction and needs further improvement. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a mobile robot path planning method based on an improved egret optimization algorithm, so as to optimize the global path of the mobile robot more quickly and effectively in a large-scale complex environment and obtain the optimal path.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A mobile robot path planning method based on an improved egret optimization algorithm comprises the following steps:

[0008] S1: Construct a two-dimensional grid map based on the map information and mark the starting point, target point, and obstacles;

[0009] S2: Construct a multi-objective function to evaluate the quality of the path based on path length and path smoothness;

[0010] S3: Initialize the population using uniformly distributed Logistic chaotic mapping combined with LOS line of sight detection algorithm;

[0011] S4: Adopt adaptive nonlinear inertia factor and salp swarm algorithm to improve the egret optimization algorithm, select corresponding global search mode and local search mode according to different iteration times, and update the position and objective function value of the egret individual;

[0012] S5: Output the best path.

[0013] Further, the multi-objective function in step S2 is as follows:

[0014] F i =λ1L(P)+λ2S(P)

[0015] λ1+λ2=1,λ1≥0,λ2≥0

[0016] where F i represents the fitness of the path corresponding to the i-th egret; L(P), S(P) represent the objective functions required to calculate the fitness, λ1 and λ2 represent the weights required to calculate the two cost functions; L(P), S(P) are shown in the following formula:

[0017]

[0018] Where (x i ,y i ) represents the coordinates of the i-th path node;

[0019]

[0020] Where P i Represents the i-th path node.

[0021] Further, the step S3 specifically includes the following steps:

[0022] S31: Generate a set of feasible points on the x-axis and y-axis using uniformly distributed Logistic chaotic mapping, and check whether the current node is reachable from its parent node and the end point through the LOS line of sight detection algorithm;

[0023] S32: If the current node and its parent node and the end point are all LOS reachable, then the individual is output as a solution for the current egret individual, and the next egret individual is returned to step S31. If only the current node and its parent node are LOS reachable, then the current node is used as the parent node of the next node and this step is repeated. If the current node and the parent node are not LOS reachable, then the current node is directly discarded.

[0024] S33: The algorithm iteratively generates feasible solutions until all egret individuals generate initial solutions.

[0025] Further, the uniformly distributed Logistic chaotic mapping formula in step S31 is:

[0026] x(n+1)=μx(n)(1-x(n))

[0027]

[0028] Where n is the nth snake heron individual, x(n) is a chaotic variable and x(n)∈(0,1), x(n+1) is the new value of the nth snake heron individual after logistic chaotic mapping, x m is a random variable that obeys a uniform distribution in the interval (0,1), and 1≤μ≤4 is a parameter used to determine the evolution process of the Logistic mapping.

[0029] Further, in step S4, the adaptive nonlinear inertia factor is introduced into the global search and prey-finding phase of the egret eagle algorithm to update the position of the corresponding egret eagle population, and the leader strategy of the salp swarm algorithm is integrated into the local search phase of the egret eagle algorithm. The improved formula for the global search and prey-finding phase is:

[0030]

[0031] in represents the new position state of the i-th egret, x r1 and x r2 is the random candidate solution in the current iteration, t is the current iteration number, T is the maximum iteration number, and w is the adaptive nonlinear inertia factor. The formula is:

[0032]

[0033] where a max =0.7, a min = 0.3, t is the current iteration number, T is the maximum iteration number, F t,best is the target value of the optimal egret in the tth iteration, F t,i is the target value of the i-th egret eagle in the t-th iteration;

[0034] The local search stage integrates the leader strategy of the salp swarm algorithm and selects different local search methods according to the generated random numbers. The specific formula is shown below:

[0035]

[0036] where x i is the updated position of the i-th individual, x best is the best individual position of the current iteration, ub and lb are the upper and lower bounds respectively, c2 and c3 are uniformly distributed random numbers between (0, 1) used to adjust the position, c1 is the convergence factor, which decreases adaptively with the increase of the number of iterations, and the value range is shown in the following formula;

[0037]

[0038] Where t is the current iteration number and T is the maximum iteration number.

[0039] The beneficial effects of the present invention are:

[0040] (1) It is proposed to use path length and total turning angle of the path as the evaluation criteria for the quality of the path, and to construct a multi-objective function, which can reduce the sharp turns in the robot's movement while ensuring the shortest path, thereby improving efficiency and reducing power consumption.

[0041] (2) Using uniformly distributed Logistic chaotic mapping to generate chaotic sequences, a more uniform heron population is generated, thus improving the quality of the initial population;

[0042] (3) Introducing adaptive nonlinear inertia weights in the prey-finding phase of global search to improve the convergence speed of the algorithm as the number of iterations increases and the fitness changes;

[0043] (4) The leader strategy of the salp swarm algorithm is integrated into the local search stage to enhance the ability to escape from the local optimum and improve the convergence speed of the algorithm.

[0044] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0046] Figure 1Flowchart of the mobile robot path planning method to improve the Heron Eagle optimization algorithm;

[0047] Figure 2 This is a schematic diagram of population distribution;

[0048] Figure 3 This is a schematic diagram of LOS line of sight detection;

[0049] Figure 4 A schematic diagram of a 256*256 city map and SBOA, WOA, and SABO simulation comparison experiment results of a mobile robot path planning method using an improved egret optimization algorithm according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the experimental results of the 512*512 game map and SBOA, WOA, and SABO simulation comparison of the mobile robot path planning method of the improved Heron Eagle optimization algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0052] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0053] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0054] The present invention provides a mobile robot path planning method based on an improved egret optimization algorithm, which mainly comprises the following steps:

[0055] S1, construct a two-dimensional grid map based on the map information, marking the starting point, target point, and obstacles, including:

[0056] S11, initialize map information and establish a grid map model. The grid map model is a matrix, which includes 0 elements and 1 elements. The 0 element represents the safe space that the mobile robot can reach; the 1 element represents an obstacle, a position where the mobile robot cannot travel.

[0057] S12, preferably, setting the robot initial position (x start ,y start ) and the robot target position (x goal ,y goal ) and obstacles.

[0058] S2, construct a multi-objective function to evaluate the quality of the path based on path length and path smoothness;

[0059] S21, the size of the objective function of the mobile robot path planning determines the quality of the generated path. The objective function is established according to the goal of path optimization. The path length and path smoothness obtained by the optimization algorithm are used to evaluate the quality of the feasible path obtained by the multi-objective optimization algorithm.

[0060] The multi-objective function description is as follows:

[0061] F i =λ1L(P)+λ2S(P) (1)

[0062] Among them, F i represents the fitness of the path corresponding to the i-th egret. L(P) and S(P) represent the objective functions required to calculate the fitness, which are specifically the following two formulas. λ1 and λ2 represent the weights required to calculate the two cost functions and satisfy the following formula:

[0063] λ1+λ2=1,λ1≥0,λ2≥0 (2)

[0064] S211, in path optimization, path length is the primary performance indicator. The shorter the path, the shorter the robot's running time and the less energy it consumes. In the process of finding the optimal path, the shortest path from the starting point to the end point is usually preferred. The robot's walking route is divided into n nodes, and each node is represented by a coordinate (x i ,y i ), and its path length objective function is shown in formula (3). The distance between two adjacent path nodes is calculated, and its distance is expressed as Euclidean distance, and then added to get the path length;

[0065]

[0066] S222, path smoothness is a key factor affecting the energy consumption of the robot. In robot path planning, reducing the number of turns on the path can maximize the smoothness of the path, thereby effectively reducing energy consumption. The concept of deflection angle is introduced, that is, the angle formed by any three adjacent path points. The path smoothness cost function is shown in formula (4);

[0067]

[0068] Where P i represents the i-th path node;

[0069] S3, using uniformly distributed Logistic chaotic mapping combined with LOS line of sight detection algorithm to initialize the population;

[0070] S31, using uniformly distributed Logistic chaotic mapping to generate a set of feasible points on the x-axis and y-axis, and using the LOS line of sight detection algorithm to check whether the current node is reachable from its parent node and the end point;

[0071] S311, the Heron Eagle optimization algorithm uses randomly generated initialized populations, but this method is prone to uneven distribution of individuals within the population and distribution clustering, which affects the results of the algorithm. Chaotic mapping has randomness, ergodicity, and initial value sensitivity, which can make the algorithm converge faster. Using uniformly distributed Logistic chaotic mapping to initialize the population can give the population a certain degree of randomness, increase the breadth and diversity of the search space, and help find a better solution. In addition, it can also improve the distribution balance of the population and avoid the situation where multiple individuals gather near a local optimal solution and cannot jump out. The uniformly distributed Logistic mapping is shown in formulas (5) and (6). First, a set of sequences is generated by formula (5), and then the set of sequences is processed by formula (6);

[0072] x(n+1)=μx(n)(1-x(n)) (5)

[0073]

[0074] Where 1≤μ≤4, μ determines the evolution process of the Logistic mapping. When the parameter μ increases, the value range of the mapping sequence also increases, and the mapping distribution becomes more uniform. When μ takes the value of 4, the system is in a completely chaotic state, and the uniformity of the mapping distribution reaches its peak. Therefore, in the present invention, μ=4;

[0075] S32, if the current node and its parent node and the end point are all LOS reachable, then output the individual as a solution for the current egret individual, and return to step S31 of the next egret individual. If only the current node and its parent node are LOS reachable, then take the current node as the parent node of the next node and repeat the steps. If the current node and the parent node are not LOS reachable, then directly discard the current node;

[0076] S33, the algorithm iteratively generates feasible solutions until all egret individuals generate initial solutions;

[0077] S4, using adaptive nonlinear inertia factor and salp swarm algorithm to improve the egret optimization algorithm, select the corresponding global search method and local search method according to different iteration times, and update the position and objective function value of the egret individual;

[0078] S41, according to the egret population initialized and generated in step S3, the target values ​​of all egrets in the population are calculated, the best egret individual is selected, and its position and target value are obtained;

[0079] S42, when t<1 / 3T, where t represents the current iteration number and T represents the maximum iteration number, the global search phase of searching for prey is entered to update the position status of the egret population;

[0080] S421, wherein an adaptive nonlinear inertia factor is introduced in the prey-seeking phase of global search, and the specific formula is:

[0081]

[0082] in represents the new position state of the i-th egret, x r1 and x r2 is the random candidate solution in the current iteration, t is the current iteration number, T is the maximum iteration number, and w is the adaptive nonlinear inertia factor. The specific formula is:

[0083]

[0084] where a max =0.7, a min = 0.3, t is the current iteration number, T is the maximum iteration number, F t,best is the target value of the optimal egret in the tth iteration, F t,i is the target value of the i-th egret in the t-th iteration.

[0085] S422, when the updated position of the egret eagle individual is obtained in each iteration and the upper and lower limit constraints are imposed on it, all positions exceeding the upper limit are changed to the upper limit value, and all positions below the lower limit are changed to the lower current value, thereby obtaining a new egret eagle individual;

[0086] S423, recalculate the target value for the new egret eagle individual with upper and lower limits. If the target value of the current iteration is less than or equal to the original target value, update the target value and position. Otherwise, do not update the target value and position of the individual. The specific formula is as follows:

[0087]

[0088] in, represents the new state of the i-th egret at this stage, F i is the target value of the objective function of the original state, F i new The target value of the objective function representing the new state;

[0089] S424, repeating steps S421, S422, and S423 until all egret eagle individuals have completed the global search for prey, thereby obtaining a new egret eagle population;

[0090] S43, for the new egret population, the local search phase is entered, which incorporates the leader strategy of the salp swarm algorithm;

[0091] S431, select different local search methods according to the generated random number, and the specific formula is as follows:

[0092]

[0093] where x i is the updated position of the i-th individual, x best is the best individual position of the current iteration, ub and lb are the upper and lower bounds respectively, c2 and c3 are uniformly distributed random numbers between (0, 1), which play the role of adjusting the position in the formula, c1 is the convergence factor, which decreases adaptively with the increase of the number of iterations, and is used to balance the exploration and development of the algorithm. The value range is shown in the following formula;

[0094]

[0095] Where t is the current number of iterations, and T is the maximum number of iterations;

[0096] S432, recalculate the target value for the local search of the new egret individual. If the target value of the current iteration is less than or equal to the original target value, update the target value and position. Otherwise, do not update the target value and position of the individual. The specific formula is as follows:

[0097]

[0098] in, represents the new state of the i-th egret at this stage, F i is the target value of the objective function of the original state, Fi new The target value of the objective function representing the new state;

[0099] S433, repeating steps S431 and S432 until all egret eagle individuals have performed local searches, obtaining a new egret eagle population, and updating the position and target value of the best egret eagle individual;

[0100] S44, when 1 / 3T<t<2 / 3T, where t represents the current iteration number and T represents the maximum iteration number, the predation phase of the global search will be entered to update the position state of the egret population;

[0101] S441, in the predator-prey phase of global search, the specific formula is:

[0102]

[0103] RB=randn(1,Dim) (14)

[0104] Here, randn(1.Dim) represents a randomly generated array of dimension 1×Dim from a standard normal distribution (mean 0, standard deviation 1), and x best Indicates the place value of the current best egret eagle individual;

[0105] S442, repeating steps S422, S423, S424, S431, S432, and S433 to obtain a new egret population;

[0106] S45, when 2 / 3T<t<T, where t represents the current iteration number and T represents the maximum iteration number, the global search will enter the attack prey phase to update the position state of the egret population;

[0107] S451, in the attack prey phase of global search, the specific formula is:

[0108]

[0109] Among them, x best Indicates the position value of the current best egret individual, RL is weighted Levy flight, and its calculation method is as follows:

[0110] RL=0.5×Levy(Dim) (16)

[0111] Where Levy(Dim) represents the Levy flight distribution function. Its calculation method is as follows:

[0112]

[0113] Where s is a fixed constant of 0.01 and η is a fixed constant of 1.5. u and v are random numbers in the interval [0, 1], and the formula for σ is as follows:

[0114]

[0115] S452, repeating step S442 to obtain a new heron eagle population;

[0116] S5, obtain the position of the best individual and use its position as the best path output for mobile robot path planning.

[0117] The present invention uses MATLAB R2023b software for simulation analysis, i5-12500H processor, running memory of 16GB, computer system of Windows11, selects parameters to set population size N=30, dimension of 30, and maximum number of iterations of 500; the scale of the simulation scene is set to 256*256 and 512*512, and each scene is compared with Secretary Bird Optimization Algorithm (SBOA), Whale Optimization Algorithm (WOA), and Subtraction-Average-Based Optimizer (SABO) under the condition that the starting position and the ending position are the same. The simulation results of the present invention are as follows: Figure 4 , Figure 5 shown.

[0118] It can be seen that when solving the path planning of mobile robots in large-scale complex environments, the improved Heron Eagle optimization algorithm has a smaller total path turning angle, a shorter path, a better path smoothing effect, and better operating efficiency.

[0119] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0120] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0121] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0122] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0123] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0124] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0125] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0126] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0127] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0128] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0129] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A mobile robot path planning method based on an improved egret optimization algorithm, characterized in that: The following steps are involved: S1: Construct a two-dimensional grid map based on the map information and mark the starting point, target point, and obstacles; S2: Construct a multi-objective function to evaluate the quality of the path based on path length and path smoothness; S3: Initialize the population using uniformly distributed Logistic chaotic mapping combined with LOS line of sight detection algorithm; S4: Adopt adaptive nonlinear inertia factor and salp swarm algorithm to improve the egret optimization algorithm, select corresponding global search mode and local search mode according to different iteration times, and update the position and objective function value of the egret individual; S5: Output the best path.

2. The mobile robot path planning method based on the improved egret optimization algorithm according to claim 1 is characterized in that: The multi-objective function in step S2 is as follows: F i =λ1L(P)+λ2S(P) λ1+λ2=1,λ1≥0,λ2≥0 Among them, F i represents the fitness of the path corresponding to the i-th egret; L(P), S(P) represent the objective functions required to calculate the fitness, λ1 and λ2 represent the weights required to calculate the two cost functions; L(P), S(P) are shown in the following formula: Where (x i ,y i ) represents the coordinates of the i-th path node; Where P i Represents the i-th path node.

3. The mobile robot path planning method based on the improved egret optimization algorithm according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: S31: Generate a set of feasible points on the x-axis and y-axis using uniformly distributed Logistic chaotic mapping, and check whether the current node is reachable from its parent node and the end point through the LOS line of sight detection algorithm; S32: If the current node and its parent node and the end point are all LOS reachable, then the individual is output as a solution for the current egret individual, and the next egret individual is returned to step S31. If only the current node and its parent node are LOS reachable, then the current node is used as the parent node of the next node and this step is repeated. If the current node and the parent node are not LOS reachable, then the current node is directly discarded. S33: The algorithm iteratively generates feasible solutions until all egret individuals generate initial solutions.

4. The mobile robot path planning method based on the improved egret optimization algorithm according to claim 1 is characterized in that: The uniformly distributed Logistic chaotic mapping formula in step S31 is: x(n+1)=μx(n)(1-x(n)) Where n is the nth snake heron individual, x(n) is a chaotic variable and x(n)∈(0,1), x(n+1) is the new value of the nth snake heron individual after logistic chaotic mapping, x m is a random variable that obeys a uniform distribution in the interval (0,1), and 1≤μ≤4 is a parameter used to determine the evolution process of the Logistic mapping.

5. The mobile robot path planning method based on the improved egret optimization algorithm according to claim 1 is characterized in that: In the step S4, the adaptive nonlinear inertia factor is introduced into the global search and prey search phase of the egret eagle algorithm to update the position of the corresponding egret eagle population, and the leader strategy of the salp swarm algorithm is integrated into the local search phase of the egret eagle algorithm. The improved formula for the global search and prey search phase is: in represents the new position state of the i-th egret, x r1 and x r2 is the random candidate solution in the current iteration, t is the current iteration number, T is the maximum iteration number, and w is the adaptive nonlinear inertia factor. The formula is: where a max =0.7, a min = 0.3, t is the current iteration number, T is the maximum iteration number, F t,best is the target value of the optimal egret in the tth iteration, F t,i is the target value of the i-th egret eagle in the t-th iteration; The local search stage integrates the leader strategy of the salp swarm algorithm and selects different local search methods according to the generated random numbers. The specific formula is shown below: where x i is the updated position of the i-th individual, x best is the best individual position of the current iteration, ub and lb are the upper and lower bounds respectively, c2 and c3 are uniformly distributed random numbers between (0, 1) used to adjust the position, c1 is the convergence factor, which decreases adaptively with the increase of the number of iterations, and the value range is shown in the following formula; Where t is the current iteration number and T is the maximum iteration number.

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