An Amphibious Robot Path Planning Method Based on Improved Sparrow Search Algorithm

Abstract

The present invention discloses an amphibious robot path planning method based on an improved sparrow search algorithm, which relates to the technical fields of robot control and path planning. It includes collecting global environmental information, obtaining image signals of the global environment, inputting the improved sparrow search algorithm to generate a global path plan, sharing environmental information and the global path plan among multiple amphibious robots to generate a collaborative path plan, evaluating the complexity of the collaborative path environment, dynamically adjusting the key parameters of the improved sparrow search algorithm, adjusting the collaborative path plan to obtain the globally optimal path, and then performing local search optimization to obtain the optimized shortest path. The algorithm is not only applied to the exploration requirements of static environments, but can also be adjusted in real time according to the complexity of dynamic environments to explore a comprehensive exploration environment with multiple situations. It enhances the system cooperation ability of amphibious robots, improves the overall task completion efficiency, and improves the ability of amphibious robots to perform tasks in complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of amphibious robot path planning, and particularly to an amphibious robot path planning method based on an improved sparrow search algorithm. Background Art

[0002] With the rapid development of artificial intelligence and robot technology, amphibious robots are increasingly widely used in the fields of ocean exploration, environmental detection, and disaster relief. Traditional path planning methods can only solve path planning problems in static and semi-static environments. In the face of complex and changing environments, these algorithms often show certain limitations. When it comes to crossing different media, traditional algorithms are difficult to effectively handle the challenges brought by complex terrain changes and environmental uncertainties. Therefore, researchers have begun to explore how to use intelligent behavior patterns in nature to improve the path planning ability of robots. Among them, the sparrow search algorithm has received extensive attention because it simulates the foraging behavior of sparrows.

[0003] Although the sparrow search algorithm has certain adaptive and global search capabilities, its original version still has some limitations. When dealing with high-dimensional optimization problems, the slow convergence speed of the algorithm leads to waste of computing resources. Secondly, in the face of non-linear environmental features, the sparrow search algorithm will fall into local optimal solutions, affecting the quality of the final path planning. Although some studies have tried to apply the sparrow search algorithm to robot path planning and achieved certain results, most of these methods have not fully considered the complexity and uncertainty of the actual operating environment. Moreover, the current methods generally lack real-time path adjustment for the data collected by the sensor network. And the existing path planning methods of the sparrow search algorithm do not consider the needs of multi-robot collaborative work. Especially in the amphibious environment, due to insufficient information exchange between each amphibious robot, the collaborative efficiency is low, and the advantages of swarm intelligence cannot be fully utilized for path optimization. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an amphibious robot path planning method based on an improved sparrow search algorithm to solve the problems that the application effect of the sparrow search algorithm in a complex environment is not clear, amphibious robots cannot cooperate in operation, and the cooperation efficiency is low.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an amphibious robot path planning method based on an improved sparrow search algorithm, which includes: collecting global environmental information to obtain an image signal of the global working environment; inputting the image signal of the global working environment into the improved sparrow search algorithm to generate a global path plan; multiple amphibious robots share environmental information and the global path plan through a wireless communication network, and use the swarm intelligence theory to generate a collaborative path plan; evaluate the environmental complexity of the collaborative path plan, and dynamically adjust the key parameters of the improved sparrow search algorithm; based on the adjusted key parameters, adjust the collaborative path plan to obtain the global optimal path; perform local search optimization on the global optimal path to obtain the optimized shortest path.

[0008] As a preferred embodiment of the amphibious robot path planning method based on the improved sparrow search algorithm of the present invention, wherein: collecting global environmental information to obtain an image signal of the global working environment includes the following steps:

[0009] Distribute multiple sensor nodes evenly in the working area;

[0010] Obtain the image signal of the working environment of the node where the sensor is located;

[0011] Stitch the collected image signals of the node working environment using spatial registration of the iterative closest point algorithm to obtain the image signal of the global working environment.

[0012] As a preferred embodiment of the amphibious robot path planning method based on the improved sparrow search algorithm of the present invention, wherein: inputting the image signal of the global working environment into the improved sparrow search algorithm to generate a global path plan includes the following steps:

[0013] Filter the noise of the image signal of the global working environment;

[0014] Use the scale-invariant feature transform algorithm on the noise-filtered image signal of the global working environment to extract environmental features;

[0015] According to the extracted environmental features, comprehensively consider the path length and the distance to obstacles, and design a fitness function;

[0016] Introduce a local search mechanism into the sparrow search algorithm, and perform further search on the surrounding area of the current path after each iteration to obtain the improved sparrow search algorithm;

[0017] Based on the improved sparrow search algorithm, randomly initialize the positions of the sparrows;

[0018] Combine the fitness function and the improved sparrow search algorithm, iteratively update the positions of the sparrows, and after continuous iterative optimization, output the global path plan.

[0019] As a preferred solution of the amphibious robot path planning method based on the improved sparrow search algorithm described in the present invention, the following steps are included: Multiple amphibious robots share environmental information and global path planning through a wireless communication network, and generate collaborative path planning using swarm intelligence theory:

[0020] Select the ZigBee protocol as the communication basis and set up a wireless communication network for stable connection of all amphibious robots;

[0021] Set a timer to synchronize data through the wireless communication network;

[0022] Comprehensively consider the current position and surrounding environmental information of each amphibious robot and design a collaborative strategy function;

[0023] Based on the collaborative strategy function, after each amphibious robot receives the synchronized data, it starts to adjust its global path planning. While maintaining a safe distance, each amphibious robot minimizes its own path length;

[0024] When the path planning of all amphibious robots no longer changes, output the collaborative path planning scheme.

[0025] As a preferred solution of the amphibious robot path planning method based on the improved sparrow search algorithm described in the present invention, the following steps are included: Evaluate the environmental complexity of the collaborative path planning and dynamically adjust the key parameters of the improved sparrow search algorithm:

[0026] Extract data on the positions and sizes of obstacles from the environmental information shared by each amphibious robot;

[0027] Obtain the environmental complexity evaluation result by counting the number of obstacles per unit area and calculating the standard deviation of the terrain height change;

[0028] Adjust the step size parameter of the amphibious robot in the improved sparrow search algorithm according to the environmental complexity evaluation result.

[0029] As a preferred solution of the amphibious robot path planning method based on the improved sparrow search algorithm described in the present invention, the following steps are included: Based on the adjusted key parameters, adjust the collaborative path planning to obtain the global optimal path:

[0030] Based on the adjusted step size parameter of the sparrow search algorithm, calculate the collaborative planning path length of the amphibious robot, evaluate the avoidance probability according to the distance between the obstacle and the collaborative planning path, and evaluate the cooperation efficiency according to the concentration of the collaborative paths planned by the amphibious robots;

[0031] The obtained path length, obstacle avoidance probability, and cooperation efficiency are integrated into the fitness function by means of weighted summation;

[0032] The improved sparrow search algorithm with adjusted parameters is re-run to regenerate the global optimal path.

[0033] As a preferred solution of the amphibious robot path planning method based on the improved sparrow search algorithm described in the present invention, wherein: local search optimization is performed on the global optimal path to obtain the optimized shortest path, including the following steps:

[0034] Initialize the local search radius, adjustment coefficient, and local fitness threshold;

[0035] Based on the initialized local search radius, adjustment coefficient, and local fitness threshold, local search optimization is performed on the global optimal path using the simulated annealing algorithm;

[0036] After each local search optimization, update the global optimal path information and recalculate the total length of the global path;

[0037] Evaluate the environmental fitness based on the total length of the global path and the obstacle avoidance probability;

[0038] When the total length of the global path is the shortest and the environmental fitness is the best, output the finally optimized path.

[0039] As a preferred solution of the amphibious robot path planning method based on the improved sparrow search algorithm described in the present invention, wherein: local search optimization is performed on the global optimal path, including the following steps:

[0040] Set the spatial range of each local search according to the task requirements and environmental complexity;

[0041] During the iteration process, narrow the spatial range of the local search;

[0042] When a new local path is found, evaluate the environmental fitness of the new local path. When the environmental fitness of the new local path is better than that of the current optimal path and exceeds the local path environmental fitness threshold, replace the current optimal path with the new local path planning.

[0043] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the amphibious robot path planning method based on the improved sparrow search algorithm described in the first aspect of the present invention is implemented.

[0044] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the amphibious robot path planning method based on the improved sparrow search algorithm as described in the first aspect of the present invention is implemented.

[0045] The beneficial effects of the present invention are as follows: taking the image signal of the global environmental information to comprehensively perceive the operation environment, ensuring the accuracy and real-time nature of the path planning, and improving the reliability of the path planning; inputting the image signal into the improved sparrow search algorithm to generate the global path planning, enhancing the efficiency of the path planning, and effectively avoiding the problem of local optimal solutions; multiple amphibious robots share information through wireless communication and generate collaborative path planning, enhancing the collaboration ability of the system, reducing path conflicts, and improving the overall task completion efficiency; evaluating the environmental complexity of the collaborative path planning and dynamically adjusting key parameters, enabling the amphibious robot to adapt to the changes in the complex environment, and enhancing the flexibility of the path planning; further optimizing the path based on the adjusted parameters, making the path length the shortest and the avoidance ability the best, achieving an efficient and safe path planning effect, and enhancing the ability of the amphibious robot to perform tasks in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a flowchart of the amphibious robot path planning method based on the improved sparrow search algorithm in Embodiment 1.

[0048] Figure 2 It is a schematic diagram of multiple amphibious robots sharing environmental information and global path planning through a wireless communication network and generating collaborative path planning in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0050] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0051] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive of other embodiments.

[0052] Embodiment 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a path planning method for an amphibious robot based on an improved sparrow search algorithm, including the following steps:

[0053] Collect global environmental information and obtain the image signal of the global working environment, including: it is necessary to select multiple sensor nodes evenly distributed in the working area and use a regular hexagon layout to arrange the sensor nodes because a regular hexagon can provide the largest coverage area within a given area and minimize the overlap between adjacent nodes; after obtaining the image signal of the working environment of the sensor's location node, use the iterative closest point (ICP) algorithm for spatial registration to splice these local image signals into the image signal of the global working environment. To improve the splicing accuracy, design an optimized formula for the ICP algorithm as follows:

[0054] ;

[0055] is the overall error function used to evaluate the difference between the changed image and the target image. represents the position of the image after being adjusted by the transformation matrix . is the position of the target image. represents the entire spatial domain where the image is located. represents a three-dimensional coordinate point in the image domain. is the regularization parameter used to control smoothness and avoid overfitting. represents the gradient operator used to measure the smoothness of the image change. The value range of this formula is theoretically non-negative real numbers, and the smaller the value, the higher the matching degree of the two images.

[0056] Input the image signal of the global working environment into the improved sparrow search algorithm to generate a global path plan, including: using a Gaussian filter to better retain image edge information while removing noise; using the scale-invariant feature transform algorithm (SIFT) to extract environmental features from the global working environment image after filtering noise. The SIFT algorithm can identify key points in the image and generate descriptors that are invariant to scale and rotation changes; based on the extracted environmental features, comprehensively consider the path length and obstacle distance, and design the fitness function as follows:

[0057] ;

[0058] In this function, represents the fitness value of the path , is the total length of the path, is the distance from the path to the th obstacle, represents the number of obstacles, represents the number of each obstacle, is the weight used to balance the path length and the importance of obstacle avoidance. The value range of this formula is positive real numbers. The larger the value, the better the path, and at the same time, the risk of approaching the obstacle too closely is avoided; To improve the sparrow search algorithm, after each iteration, the simulated annealing algorithm is used to implement the local search mechanism, and its core formula is:

[0059] ;

[0060] Among them, represents the probability of accepting a worse solution, is the energy difference between the new solution and the current solution, is the temperature parameter, which gradually decreases as the number of iterations increases. This step enhances the ability of the sparrow search algorithm to jump out of the local optimal solution by introducing the simulated annealing algorithm; Randomly allocate the initial positions of each sparrow in the search space, combine the fitness function and the improved sparrow search algorithm, iteratively update the positions of the sparrows, and finally output the global path planning by continuously adjusting the positions and evaluating the fitness.

[0061] Multiple amphibious robots share environmental information and global path planning through a wireless communication network, and use the theory of swarm intelligence to generate collaborative path planning, including: Select the ZigBee protocol as the wireless communication basis for amphibious robots. Because of the low power consumption, low cost and self-organizing network characteristics of ZigBee, it is very suitable for data exchange in multi-robot systems; To ensure that all amphibious robots can update their environmental information and path planning in real time, set a timer to synchronize data through wireless communication at fixed time intervals; Considering the current positions and surrounding environmental information of each amphibious robot, design a collaborative strategy function, aiming to optimize the path planning of each amphibious robot while maintaining a safe distance. This function is expressed as:

[0062] ;

[0063] Among them, represents the collaborative strategy value of the th amphibious robot, is the path length of the th amphibious robot, is the distance between the th amphibious robot and the th amphibious robot, represents the distance between the th amphibious robot and the nearest obstacle, and represent the numbers of each amphibious robot, and

[0064] are weight coefficients used to balance the importance of path length, safe distance between amphibious robots, and obstacle avoidance. The value range of this formula is positive real numbers. The larger the value, the better the path. At the same time, it ensures the safe operating distance between amphibious robots. Based on the collaborative policy function, each amphibious robot optimizes its path planning according to the current environmental information and the position information of other robots received, so as to minimize its own path length while maintaining a safe distance from other amphibious robots. When the path planning of all amphibious robots no longer changes, the collaborative path planning scheme is output.

[0065] ;

[0066] Among them, represents the environmental complexity, is the number of obstacles per unit area, is the total area of the evaluation area, is a weight coefficient used to balance the influence of obstacle density and terrain complexity, is the standard deviation of the terrain height change, used to measure the undulation degree of the terrain. The value range of this formula is positive real numbers. The larger the value, the more complex the environment and the higher the requirements for path planning. According to the environmental complexity evaluation result, the step size parameter of the amphibious robot in the improved sparrow search algorithm is adjusted. The specific method is to design an adaptive step size adjustment function to adjust the step size parameter. The function formula is:

[0067] ;

[0068] Among them, represents the step size of the th amphibious robot, is the maximum allowable step size, is the current environmental complexity, is the reference complexity threshold, used to standardize the impact of environmental complexity on the step size. The value range of this formula is , and the smaller the value, the smaller the step size is adopted in the complex environment to improve accuracy and safety.

[0069] Based on the adjusted key parameters, the collaborative path planning is adjusted to obtain the globally optimal path, including according to the adjusted step size parameter and the total number of steps of the amphibious robot moving , calculate the path length ; For each planned path, use the following formula to evaluate the avoidance probability :

[0070] ;

[0071] Among them, is the distance between the path and the nearest obstacle, is the set safety distance threshold, is the sensitivity coefficient used to adjust the obstacle avoidance probability The sensitivity to distance changes. The value range of this formula is , and the larger the value, the safer the path; According to the collaborative path concentration situation planned by the amphibious robot, use the following formula to calculate the cooperation efficiency :

[0072] ;

[0073] Among them, represents the th path length of the amphibious robot, represents the number of each amphibious robot, is the total number of robots participating in collaboration. The closer the value of the cooperation efficiency is to , it means that the path allocation among robots is more balanced and the cooperation efficiency is higher; Integrate the obtained path length, obstacle avoidance probability and cooperation efficiency into the fitness function by weighted summation, that is:

[0074] ;

[0075] Among them, represents the fitness value of the path . The larger the fitness value, the better the path. , , are the weight coefficients of path length, obstacle avoidance probability and cooperation efficiency respectively, used to balance the importance of each factor; Re-run the improved sparrow search algorithm after adjusting the parameters to regenerate the globally optimal path.

[0076] Perform local search optimization on the globally optimal path to obtain the optimized shortest path, including: setting the local search radius to 10% of the total path length, allowing preliminary optimization within a large range, with an adjustment coefficient of 0.95, which is used to gradually reduce the search radius to ensure that the search range shrinks gradually after each iteration, and the local fitness threshold is the fitness value of the globally optimal path; based on the local search radius, adjustment coefficient, and local fitness function threshold, use the simulated annealing algorithm to perform local search optimization on the globally optimal path. When the fitness value of each candidate path is greater than the threshold and the difference between the candidate path and the optimal path is less than the search radius, update the optimal path. Otherwise, decide whether to update the optimal path according to the probability function of simulated annealing; after each local search optimization, recalculate the global path length and evaluate the obstacle avoidance probability; based on the path length and obstacle avoidance probability, recalculate the environmental fitness; when the global path length is the shortest and the fitness is optimal, output the finally optimized path.

[0077] This embodiment also provides a computer device applicable to the case of the path planning method of an amphibious robot based on the improved sparrow search algorithm, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the path planning method of the amphibious robot based on the improved sparrow search algorithm as proposed in the above embodiment.

[0078] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (near-field communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0079] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the amphibious robot path planning method based on the improved sparrow search algorithm proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0080] In summary, the present invention includes: collecting global environmental information to obtain an image signal of the global working environment; inputting the image signal of the global working environment into an improved sparrow search algorithm to generate a global path plan; multiple amphibious robots sharing environmental information and the global path plan through a wireless communication network, and generating a collaborative path plan using the swarm intelligence theory; evaluating the environmental complexity of the collaborative path plan and dynamically adjusting the key parameters of the improved sparrow search algorithm; adjusting the collaborative path plan based on the adjusted key parameters to obtain a globally optimal path; performing local search optimization on the globally optimal path to obtain an optimized shortest path. The algorithm is not only applicable to the exploration requirements of static environments, but can also be adjusted in real time according to dynamic environments to explore comprehensive environments with multiple situations. It enhances the system cooperation ability of amphibious robots, improves the overall task completion efficiency, and improves the ability of amphibious robots to perform tasks in complex environments.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An amphibious robot path planning method based on an improved sparrow search algorithm, characterized in that: Including: Collect global environmental information and obtain the image signal of the global working environment; Input the image signal of the global working environment into the improved sparrow search algorithm to generate a global path plan, including the following steps: Filter the noise of the image signal of the global working environment; Use the Scale-Invariant Feature Transform (SIFT) algorithm on the noise-filtered image signal of the global working environment to extract environmental features; Design a fitness function based on the extracted environmental features, taking into account the path length and the distance to obstacles; Introduce a local search mechanism into the sparrow search algorithm. After each iteration, further search the surrounding area of the current path to obtain the improved sparrow search algorithm; Randomly initialize the positions of the sparrows based on the improved sparrow search algorithm; Combine the fitness function and the improved sparrow search algorithm, iteratively update the positions of the sparrows, and after continuous iterative optimization, output the global path plan; Multiple amphibious robots share environmental information and the global path plan through a wireless communication network, and use the theory of swarm intelligence to generate a cooperative path plan, including the following steps: Select the ZigBee protocol as the communication basis and set up a wireless communication network for stable connection of all amphibious robots; Set a timer to synchronize data through the wireless communication network; Design a cooperative strategy function by comprehensively considering the current positions and surrounding environmental information of each amphibious robot; Based on the cooperative strategy function, each amphibious robot adjusts its global path plan after receiving the synchronized data. Each amphibious robot minimizes its own path length while maintaining a safe distance; When the path plans of all amphibious robots no longer change, output the cooperative path plan; Evaluate the environmental complexity of the cooperative path plan and dynamically adjust the key parameters of the improved sparrow search algorithm; Based on the adjusted key parameters, adjust the cooperative path plan to obtain the globally optimal path; Conduct local search optimization on the globally optimal path to obtain the optimized shortest path.

2. The amphibious robot path planning method based on the improved sparrow search algorithm according to claim 1, characterized in that, Collect global environmental information and obtain the image signal of the global working environment, including the following steps: Evenly distribute multiple sensor nodes in the working area; Obtain the image signal of the working environment of the sensor's node; Use the Iterative Closest Point (ICP) algorithm for spatial registration to stitch the collected image signals of the node working environments to obtain the image signal of the global working environment.

3. The amphibious robot path planning method based on the improved sparrow search algorithm according to claim 1, characterized in that Evaluate the environmental complexity of the cooperative path plan and dynamically adjust the key parameters of the improved sparrow search algorithm, including the following steps: Extract data on the positions and sizes of obstacles from the environmental information shared by each amphibious robot; Obtain the environmental complexity evaluation result by counting the number of obstacles per unit area and calculating the standard deviation of the terrain height change; Adjust the step size parameter of the amphibious robot in the improved sparrow search algorithm according to the environmental complexity evaluation result.

4. The amphibious robot path planning method based on the improved sparrow search algorithm according to claim 1, characterized in that, Based on the adjusted key parameters, adjust the cooperative path plan to obtain the globally optimal path, including the following steps: Based on the step size parameter of the adjusted sparrow search algorithm, calculate the collaborative planning path length of the amphibious robot. Evaluate the avoidance probability according to the distance between the obstacle and the collaborative planning path. Evaluate the collaboration efficiency according to the concentration of the collaborative paths planned by the amphibious robot; Adopt the method of weighted summation to integrate the obtained path length, obstacle avoidance probability and collaboration efficiency into the fitness function; Rerun the improved sparrow search algorithm with adjusted parameters to regenerate the global optimal path.

5. The amphibious robot path planning method based on the improved sparrow search algorithm according to claim 1, characterized in that, Conduct local search optimization on the global optimal path to obtain the optimized shortest path, including the following steps: Initialize the local search radius, adjustment coefficient and local fitness threshold; Based on the initialized local search radius, adjustment coefficient and local fitness threshold, use the simulated annealing algorithm to conduct local search optimization on the global optimal path; After each local search optimization, update the global optimal path information and recalculate the total length of the global path; Evaluate the environmental fitness based on the total length of the global path and the obstacle avoidance probability; When the total length of the global path is the shortest and the environmental fitness is the best, output the finally optimized path.

6. The amphibious robot path planning method based on the improved sparrow search algorithm according to claim 5, wherein: Conduct local search optimization on the global optimal path, including the following steps: Set the spatial range of each local search according to the task requirements and environmental complexity; During the iteration process, narrow the spatial range of the local search; When a new local path is found, evaluate the environmental fitness of the new local path. When the environmental fitness of the new local path is better than that of the current optimal path and exceeds the local path environmental fitness threshold, replace the current optimal path with the new local path planning.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the amphibious robot path planning method based on the improved sparrow search algorithm according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the amphibious robot path planning method based on the improved sparrow search algorithm according to any one of claims 1 to 6.

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