Path planning method based on umbrella-sky optimization improved model

Abstract

The invention discloses a path planning method based on an umbrella-sky optimization improved model. Relates to the technical field of intelligent obstacle avoidance, in particular to the technical field of path planning based on an umbrella-sky optimization and improvement model. On the basis of a dynamic and static cooperation method, an environment model is updated in real time, the model is optimized and improved in combination with umbrella sky, and a path which can avoid obstacles and is efficient is planned. The method comprises the following steps: acquiring an environment information data set; the method comprises the following steps: constructing and improving an umbrella-sky optimization model: in a development stage of the umbrella-sky optimization model, adding an adaptive t distribution variation strategy and a Levy path strategy in parallel between a conventional strategy for calculating a new position of a population individual and a new position updating module of the population individual; a Cauchy variation strategy is added between a population individual new position updating module and a population individual optimal position updating module; and planning a path through the trained umbrella-sky optimization improved model.

Description

Technical Field

[0001] The present invention relates to the field of intelligent obstacle avoidance technology, and in particular to the field of path planning technology based on an improved optimization model of a lizard. Background Art

[0002] In the field of smart mobile devices, intelligent agents with tracking capabilities, such as robots, unmanned vehicles, and drones, face multiple technical challenges in their actual operation. As application scenarios gradually expand into complex and dynamic environments, such as urban traffic arteries, complex terrain, and crowded places, the high complexity and rapid dynamic changes in environmental information have led to increasingly prominent problems such as delayed map data updates and inaccurate obstacle avoidance path planning. Due to technical bottlenecks in the real-time and accuracy of path avoidance planning, intelligent agents struggle to respond promptly to sudden obstacles in their path, resulting in frequent collision accidents.

[0003] In complex dynamic environments, the spatial position and speed of obstacles are constantly changing. Traditional obstacle avoidance algorithms based on static environment assumptions are no longer able to meet real-world requirements. In recent years, the Frilled Lizard Optimization (FLO) algorithm has provided a new solution for dynamic obstacle avoidance path planning. While it can adapt to dynamic environmental changes to a certain extent, in practice, its planned paths often suffer from significant redundancy. This redundancy not only increases computing resource consumption and prolongs path planning time, but also increases device energy consumption and reduces execution efficiency. This results in significant waste of manpower, material resources, and time, hindering the effectiveness of intelligent agents in complex scenarios. Summary of the Invention

[0004] To address the existing issues of inability to update environmental information in a timely manner and the significant redundancy in obstacle avoidance paths, this paper proposes a path planning method based on an optimized and improved model of a frilled lizard. This method, based on a dynamic-static collaborative approach, updates the environmental model in real time. Combined with the frilled lizard optimized and improved model, it plans an efficient and obstacle-avoiding path.

[0005] The method comprises the following steps: S1. Obtain environmental information dataset; S2. Build and improve the lizard optimization model: S21. During the development phase of the optimization model for the frilled lizard, an adaptive strategy was added in parallel between the conventional strategy for calculating the new positions of individuals in the population and the module for updating the new positions of individuals in the population. Distribution mutation strategy and Levy path strategy; Setting the update policy threshold , Representation interval A randomly selected number from when When performing adaptive Distribution mutation strategy; Otherwise, the Levy path strategy is executed; S22. During the development phase of the optimization model for the frilled lizard, a Cauchy mutation strategy is added between the population individual new position update module and the population individual optimal position update module. S3. Iterative training of the lizard to optimize the improved model Second-rate; S4, optimize and improve the model planning path through the trained frilled lizard; The path planned by the trained optimization and improvement model of the frilled lizard is specifically as follows: S41, determine whether the distance between the sudden obstacle and the current position is less than the safety distance, if less than the safety distance, execute step S42, otherwise, maintain the original path; S42: Update the environmental information dataset to trigger the trained improved frilled lizard optimization model to plan a path.

[0006] Furthermore, the environmental information data set represents the environment of the path to be planned. dimensional path model.

[0007] Furthermore, the adaptive The distribution mutation strategy is as follows: ,in, Indicates that the development phase is adaptive In the distribution mutation strategy The location of the frilled lizard, Represents a number of iterations distributed, represents the conventional strategy for calculating the new position of individuals in the population. The location of the frilled lizard, .

[0008] Furthermore, the Levy path strategy is specifically as follows: ,in, Indicates that the development stage is the first in the Levy path strategy The location of the frilled lizard, represents the position offset coefficient, Represents the Levy path mechanism, represents the random path length.

[0009] Furthermore, the population individual new position update module is specifically: ,in, express The evaluation function value of and Respectively and The evaluation function value of .

[0010] Furthermore, the Cauchy mutation strategy is specifically as follows: ,in, Represents the first position determined by the population individual new position update module The location of the frilled lizard, represents the Cauchy distribution function, Indicates the development stage in the Cauchy mutation strategy The location of the frilled lizard.

[0011] Furthermore, the population individual optimal position update module is specifically: ,in, express The evaluation function value of express The evaluation function value of represents the first position determined by the population individual optimal position update module The location of the frilled lizard.

[0012] The beneficial effects of the method of the present invention are: (1) The method of the present invention is based on a dynamic and static combination method to timely update the environmental model (obstacle information), provide real-time and reliable environmental information for path planning, and ensure that the new path can avoid moving or newly added obstacles in a timely manner.

[0013] (2) The method of the present invention can solve the problems of slow convergence and easy falling into local optimum of the basic lizard optimization algorithm by introducing the Levy path strategy in the development stage of the lizard optimization algorithm, thereby improving the global search capability.

[0014] (3) The method of the present invention introduces adaptive Distribution mutation strategy, with a certain probability Distributed perturbation variation not only does not change the original update principle formula of the umbrella lizard optimization algorithm, but also enables it to have better global development capabilities in the early stage of iteration and good local exploration capabilities in the later stage of iteration, thereby accelerating the convergence speed of the umbrella lizard optimization algorithm.

[0015] (4) The method of the present invention introduces the Cauchy mutation strategy in the development stage of the umbrella lizard optimization algorithm to perturb the current optimal individual to ensure that the umbrella lizard optimization improvement model can smoothly jump out of the local extreme value area, thereby reducing the impact of local optimality on the optimization ability of the umbrella lizard optimization improvement model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the path planning flow chart of the present invention; Figure 2 A flow chart of the optimized and improved model of the fringed lizard of the present invention; Figure 3 The present invention Schematic diagram of obstacle avoidance path; Figure 4 The present invention Schematic diagram of obstacle avoidance path; Figure 5 The present invention Schematic diagram of obstacle avoidance path when ; Figure 6 The present invention Schematic diagram of obstacle avoidance path; Figure 7 The present invention Schematic diagram of obstacle avoidance path; Figure 8 The present invention Schematic diagram of obstacle avoidance path; Figure 9 This is a three-dimensional schematic diagram of the comparative test results of the path planning algorithms in the first type of obstacle map of the present invention; Figure 10 This is a top view of the results of the path planning algorithm comparison test in the first type of obstacle map of the present invention, where origin represents the starting point and destination represents the end point; Figure 11 This is a three-dimensional schematic diagram of the comparative test results of the path planning algorithms in the second type of obstacle map of the present invention; Figure 12 This is a top view of the results of the path planning algorithm comparison test in the second type of obstacle map of the present invention, where origin represents the starting point and destination represents the end point; Figure 13 This is a three-dimensional schematic diagram of the comparative test results of the path planning algorithms in the third type of quantitative obstacle map described in the present invention; Figure 14 This is a top view of the results of the path planning algorithm comparison test in the third type of quantitative obstacle map described in the present invention, where origin represents the starting point and destination represents the end point; Figure 15 This is a schematic diagram of the path lengths planned by each model in the comparative test of the three maps (environments) with different numbers of obstacles described in the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1 This embodiment provides a path planning method based on the optimized improved model of the umbrella lizard. In this embodiment, the intelligent mobile device is an unmanned vehicle, such as Figure 1 As shown, the method includes the following steps: Step 1: When the unmanned vehicle detects a sudden static obstacle, it immediately calculates the current path segment vector and the projection of the static obstacle onto the current path. Based on the above calculation results, it determines whether the static obstacle is inside or near the current path (line segment). If the static obstacle is not on the current path, it skips the current path segment and jumps to the next path segment.

[0019] Step 2: Once the static obstacle is confirmed to be on the current path, calculate the shortest distance from the static obstacle to the current path segment. ,like If the distance is greater than or equal to the safety distance threshold, the original path is maintained, but the area should be continuously monitored to prevent the future path from expanding into the area or static obstacles from moving onto the current path; Step 3: If If the distance is less than the safety distance threshold, the location is immediately marked as impassable and the environmental information dataset (environmental model) is updated. The environmental information dataset represents the environment of the path to be planned. dimensional path model; Triggering the optimization and improvement model of the frilled lizard to replan the path based on the updated environmental information dataset; Step 4: Determine whether the shortest distance from the new path to the static obstacle is less than the safety distance threshold. If so, trigger the Flying Lizard optimization model to replan the path. Otherwise, synchronize the new path to the environmental information dataset.

[0020] Example 2 This embodiment further limits the embodiment 1.

[0021] When faced with a single or multiple moving obstacles, the autonomous vehicle needs to predict the obstacle's position over a period of time based on known speed and direction information. This process involves calculating the target distance (the distance the autonomous vehicle should travel within a given timeframe), accumulating the path distance to determine which section of the path the autonomous vehicle should be located on, and ultimately calculating the vehicle's specific position along that section of the path. This method effectively simulates the movement of the autonomous vehicle along the path. A time window is set, and a smaller time step is used to gradually check the relative position between the autonomous vehicle and each moving obstacle. If the predicted distance between the two is less than a preset safety threshold, a potential collision risk is considered. At this point, the predicted collision point needs to be immediately marked as impassable, and the environmental information dataset updated.

[0022] Based on the updated environmental information dataset, the Flying Lizard optimization model is used to plan a new safe path. This new path should avoid all predicted areas where collisions may occur, while minimizing the additional distance caused by path adjustments. It is worth noting that if the newly planned path still has a potential collision risk, it needs to be adjusted again until a completely safe path is found. In addition, to ensure that the unmanned vehicle's path is always safe, the above collision detection and path adjustment process is repeated at fixed time intervals. Once a new dynamic obstacle or static obstacle is detected, the path replanning process is triggered immediately, rather than waiting for the next periodic inspection. This continuous monitoring and immediate response mechanism is crucial for coping with complex and changing environments, as it ensures that the unmanned vehicle can always follow the optimal and safest path.

[0023] Example 3 This embodiment further limits Embodiment 1 and Embodiment 2.

[0024] The Frilled Lizard Optimization (FLO) model, proposed by Ibraheem AbuFalahah et al. in 2024, simulates the unique hunting behavior of frilled lizards in their natural habitat. Therefore, the algorithm consists of three phases: population initialization, exploration, and exploitation.

[0025] The method of the present invention improves the optimization model of the frilled lizard.

[0026] like Figure 2 As shown in Figure 2, the working process of the Flying Lizard optimization and improvement model is as follows: 1. Population Initialization Initialize the frilled lizard population. The population members are randomly initialized in the search space. The population matrix can be expressed by formula (1), where: Indicates the Dimension, Indicates the total number of map dimensions to search. Indicates the population of frilled lizards, : (1) No. The position of a frilled lizard can be expressed by formula (2), where Indicates the Dimension The location of the frilled lizard, Indicates that from the interval A randomly selected number from and Respectively represent Lower and upper bounds for dimensional random walks: (2) The optimization model of the lizard gets the starting and ending points, sets the initial parameters of the optimization model, and sets and , , Indicates the iterations, Represents the total number of iterations, when When , it means that the optimal path from the starting point to the end point has been generated and the iteration is finished. , Indicates the A frilled lizard, Represents the total number of frilled lizards (population members).

[0027] 2. Exploration Phase One of the most iconic natural behaviors of frilled lizards is their hunting strategy. Frilled lizards are sit-and-wait predators that attack upon sighting their prey. Simulating the frilled lizards' movement toward their prey results in extensive changes in the positions of population members within the problem-solving space, thereby enhancing the algorithm's global search capabilities.

[0028] In the exploration phase, the positions of the individuals in the population are updated according to the hunting strategy of the frilled lizard. The method of the present invention takes the position of the population member with the best evaluation function value as the prey position, i.e. The new location of the frilled lizard can have multiple more favorable locations (prey). The new position of the frilled lizard can be expressed by formula (3), where Indicates the The new location of the frilled lizard, Indicates the The evaluation function value of the current position of the frilled lizard, Indicates the Only prey, Indicates the The location of the prey of the frilled lizard, express The evaluation function value of : (3) Evaluation function value representation: initial position Through the Fringed lizard Arrive at the target location The distance can be expressed by formula (4): Indicates the The position of the frilled lizard in the first dimension, Indicates the The frilled lizard Dimensional location, Indicates the The frilled lizard Dimensional location, Indicates the The frilled lizard Dimension location: (4) Assume that the frilled lizard randomly selects one of the candidate prey and attacks it. Based on the modeling of the frilled lizard's movement toward the selected prey, the new position of each frilled lizard individual in the population during the exploration phase is calculated using formula (5), where Indicates the The new position of the frilled lizard during the exploration phase, Represents from the set Random numbers picked from: (5) judge The evaluation function value of Is it better than The evaluation function value of , and use formula (6) to calculate the The optimal position of a frilled lizard during the exploration phase : (6) 3. Development Phase After feeding, frilled lizards retreat to the treetops near their location. By simulating the lizard's movement to the treetops, the positions of individual lizards in the solution space undergo slight changes, thereby improving the algorithm's ability to utilize local search. During the development phase, the positions of individual lizards in the solution space are updated based on the strategy of retreating to the treetops after feeding.

[0029] Based on the modeling of the behavior of frilled lizards moving to the top of nearby trees, the general strategy for calculating the new position of individuals in the population is shown in formula (7), where represents the conventional strategy for calculating the new position of individuals in the population. The location of the frilled lizard, and They represent the upper and lower bounds of the random walk respectively: (7) The basic Frilled Lizard Optimization (FLO) algorithm is an intelligent algorithm that simulates the unique hunting behavior of frilled lizards in their natural habitat. This algorithm has the advantages of strong global search capabilities and fast convergence, but it also has some shortcomings: 1) Slow convergence: In some complex problems, the Flying Lizard Optimization Algorithm may require a large number of iterations to reach a satisfactory solution, which will affect the efficiency of the algorithm.

[0030] 2) Optimization accuracy: The basic lizard optimization algorithm may have insufficient accuracy in the process of finding the optimal solution, especially in high-dimensional and complex optimization problems. This may result in the found solution not being globally optimal.

[0031] 3) Easy to fall into local optimality: Although the lizard optimization algorithm has strong global search capabilities, in some cases, the algorithm may still fall into local optimal solutions, especially in search spaces with a large number of local optimal solutions.

[0032] 4) Parameter adjustment sensitivity: The performance of the basic lizard optimization algorithm depends to a certain extent on the setting of the initial parameters. Improper parameter selection may affect the algorithm's search effect and the quality of the final solution.

[0033] 5) Adaptability issues: Algorithms may need to be adjusted and optimized for different optimization problems to adapt to the characteristics and requirements of specific problems, which may increase the complexity of algorithm application. To solve the above problems, the method of the present invention adds an adaptive strategy in parallel between the conventional strategy for calculating the new position of the population individual (Formula (7)) and the population individual new position update module (Formula (13)). Distribution mutation strategy and Levy path strategy; Adaptive The distribution mutation strategy is as follows: (8) in, Indicates that the development phase is adaptive In the distribution mutation strategy The location of the frilled lizard, A variant is made to represent the number of iterations distribution, which is considered as parameter freedom. Based on this definition, we introduce Distribute random interference items to make full use of the information interference of the current population. The number of initial iterations is moderate. The distribution mutation is similar to the Cauchy distribution mutation, which has a strong global search performance; the later stage is similar to the Gaussian distribution mutation, which has excellent local development potential. Distribution mutation is between Cauchy mutation and Gaussian mutation. It combines the advantages of both Gaussian and Cauchy operators while optimizing both the global and local exploration performance of the algorithm.

[0034] The Levy path strategy is used to simulate the random walk process of animals foraging. Many researchers have applied this strategy to solve random search problems to optimize algorithm performance and thus obtain better models. The advantages of the Levy path strategy lie in its search diversity and long jump characteristics, which effectively avoid local optima and explore the entire search space. It also has better convergence performance and robustness, making it suitable for a variety of optimization problems.

[0035] The Levy path strategy is as follows: (9) in, Indicates that the development stage is the first in the Levy path strategy The location of the frilled lizard, represents the position offset coefficient, (10), Representation interval A randomly selected number from Represents the Levy path mechanism, represents the random path length, (11), 、 All said A random number in the range, Indicates a constant. In this embodiment The value of is 1.5, Indicates the use Parameter representing the random path length, (12) Setting the update policy threshold , Representation interval A randomly selected number from when When performing adaptive Distribution mutation strategy; Otherwise, the Levy path strategy is executed; No. The position of the frilled lizard has adapted After the distribution mutation strategy or Levy path strategy, the population individual new position update module (Formula (13)) is used to obtain the first position determined by the population individual new position update module. The location of the frilled lizard : (13) In the basic frilled lizard optimization algorithm, all frilled lizard individuals will gather towards the optimal individual when searching for the best. However, in the later stages of the algorithm, the diversity of the population will decrease, which will cause the algorithm to easily fall into a local optimal state. The Cauchy mutation strategy is introduced to perturb the current optimal individual to ensure that the algorithm can successfully jump out of the local extreme value area, thereby reducing the impact of the local optimality on the algorithm's optimization ability. The Cauchy mutation outlier is derived from the Cauchy distribution, and its function expression is shown in formula (14), where, express Input, Represents the Cauchy distribution probability function: (14) As shown in formula (15), the Cauchy mutation operator is introduced into the improved optimization algorithm for frilled lizards, and its perturbation ability is fully utilized to adjust the objective function value of the optimal frilled lizard individual: (15) in, Represents the first position determined by the population individual new position update module The location of the frilled lizard, represents the Cauchy distribution function, Indicates the development stage in the Cauchy mutation strategy The location of the frilled lizard.

[0036] Will Input the optimal position update module of the population individual (Formula (16)) to obtain the development stage The optimal position of the step, if , then save the current best candidate solution ( The best positions of the first step are spliced together, which is the current best candidate solution). Otherwise, , No. Only the frilled lizard performs optimization during the exploration and exploitation phases.

[0037] The optimal position update module of the population individual is shown in formula (16): (16) in, express The evaluation function value of express The evaluation function value of represents the first position determined by the population individual optimal position update module The location of the frilled lizard.

[0038] Determine whether the iteration is completed, that is, if , then the iteration is completed and the current best candidate solution is output ( , the current best candidate solution represents the minimum path cost and the optimal path planning); On the contrary, , execute Suboptimization.

[0039] Example 4 This embodiment further limits Embodiments 1 to 3.

[0040] This embodiment sets the simulation experiment scene to include fixed obstacles, sudden obstacles and mobile obstacles. Fixed obstacles, such as walls and buildings, are marked with black crosses in the simulation map to clearly identify the robot's inaccessible areas. Sudden obstacles are randomly generated and appear in different positions of the map in the form of fixed red triangles. This setting increases the uncertainty of path planning and the requirements of dynamic response. Mobile obstacles have dynamic characteristics and can move according to preset trajectories in the simulation environment. These obstacles are usually represented by other robots or dynamic objects and are represented in the form of moving red rectangles on the simulation map to test the robot's obstacle avoidance ability when facing dynamic obstacles. No-entry zones: certain areas are set as no-entry zones due to safety or other reasons. In the simulation environment, these areas are intuitively marked with red crosses.

[0041] In this embodiment, the intelligent mobile device is a robot, which starts from a starting point S and ends at an end point T.

[0042] like Figure 3 As shown, the initial At this moment, the robot uses the first optimal path calculated by the improved algorithm of the umbrella lizard optimization and starts to move along the path.

[0043] like Figure 4 As shown, in At this moment, the robot faces the obstacle starting from To the end of the obstacle According to the set prediction time window analysis, if the current path is not changed, the robot will collide with the moving obstacle. Figure 5 As shown, in At this moment, the area between the two red cross points is identified as a potential collision interval and marked as a temporary no-entry area. The environmental information dataset is updated and the path replanning operation is performed to ensure that the robot can safely avoid obstacles.

[0044] like Figure 6 As shown, the robot When encountering a fixed sudden obstacle red triangle at any time, the environment information dataset is updated immediately and the path is replanned based on the new environment information to avoid the obstacle.

[0045] like Figure 7 As shown, the robot At this moment, I encountered another obstacle starting point. Towards the end of the obstacle Moving obstacles, according to the time window prediction, if the path is not adjusted, the robot will collide with the obstacle, e.g. Figure 8 As shown, in At this moment, the red cross area is marked as a temporary no-entry area, the environment model is updated and the path is replanned to avoid collision with moving obstacles, ensuring that the robot can reach the destination T smoothly.

[0046] arrive Throughout the entire process, the dynamic obstacle avoidance strategy of the method described in the present invention demonstrates its efficiency and reliability in dealing with complex and changing environments, and emphasizes its ability to respond quickly to emergencies and its high adaptability.

[0047] Example 5 This embodiment further limits Embodiments 1 to 4.

[0048] The method of the present invention can also be applied to planning the obstacle avoidance path of a UAV.

[0049] Example 6 This embodiment further limits Embodiments 1 to 5.

[0050] In order to verify the effectiveness of the improved frilled lizard optimization algorithm (IFLO), this example uses three maps with different numbers of obstacles for comparative experiments. The improved frilled lizard optimization algorithm (IFLO), the raccoon optimization algorithm (COA), the frilled lizard optimization algorithm (FLO), the sparrow search algorithm (SSA), the dung beetle optimization algorithm (DBO), and the pelican optimization algorithm (POA) respectively perform global path planning experiments in a three-dimensional environment model. The experimental results are shown in Figure 2. Figures 9 to 14 shown.

[0051] In this example, the population size of COA, FLO, IFLO, SSA, DBO, and POA is set to 30, and the maximum number of iterations is set to 500. This is to verify the practicality of IFLO and its impact on model performance.

[0052] like Figures 9 to 14 As shown, both IFLO and other models can achieve conventional trajectory planning from the start point to the end point, avoiding obstacles and hazardous areas in the environment. However, while COA can complete the UAV's trajectory planning, its planned trajectory exhibits excessive unevenness, large fluctuations, and a bird's-eye (2D) image shows it is too close to hazardous areas. These issues indicate that COA is trapped in a local optimum during the trajectory planning process, resulting in a costly and poorly planned trajectory. In contrast, IFLO plans a more stable trajectory with moderate fluctuations, an overall smooth trajectory, and a safe distance from hazardous areas.

[0053] The path lengths planned by each model in the comparative test of three maps (environments) with different numbers of obstacles are as follows: Figure 15 As shown, the path planned by IFLO is the shortest in all three obstacle maps. While the paths planned by COA and DBO meet the requirements, they are not as good as IFLO due to their longer distances and higher energy consumption. IFLO achieves better planned paths than the other five algorithms in scenarios of varying complexity. This example tests IFLO's performance in environments of varying complexity (different obstacle maps). These test results demonstrate IFLO's ability to plan paths in complex environments, with good performance and short execution times. This demonstrates IFLO's effectiveness and superior performance.

[0054] Path planning technology for intelligent mobile devices is a core research area in the field of intelligent mobile device applications. It involves designing an optimal or feasible path from a starting point to a destination for an intelligent mobile device, taking into account multiple factors such as driving safety, efficiency, environmental factors, and driving constraints. With the widespread application of intelligent mobile devices in various fields, such as military reconnaissance, disaster relief, environmental monitoring, agricultural plant protection, and logistics distribution, the importance of path planning technology has become increasingly prominent. It not only improves the autonomous driving capabilities of intelligent mobile devices and reduces human intervention, but also enhances their ability to cope with complex environments and emergencies. Furthermore, optimized path planning can effectively reduce flight risks and improve the efficiency and quality of mission execution, having a profound impact on promoting the healthy development of the intelligent mobile device industry. In global path planning, compared to traditional path planning algorithms, swarm intelligence optimization algorithms have a wide range of applications, strong scalability, and significant improvements. Therefore, this paper uses the umbrella lizard optimization algorithm, which performs relatively well among swarm intelligence optimization algorithms, as a basis for improvement, and designs a path planning method based on the improved umbrella lizard optimization model.

[0055] This invention addresses the problems of poor optimization results and low search stability during path planning for intelligent mobile devices. By using a Levy flight strategy and an adaptive t-distribution mutation strategy during the pursuit and escape phases, this algorithm effectively improves solution accuracy and optimizes the optimization results. This strategy also enhances local exploration capabilities in the later stages of iteration, thereby increasing the convergence speed of the algorithm. Finally, by introducing a Cauchy mutation strategy, the randomness of the improved optimization model is enhanced, helping it escape local optima and improving its global search capabilities. Based on this improved optimization model, it is applied to solving intelligent mobile device path planning problems, and experiments verify its advantages in solving these problems. The trajectory planned by IFLO is more stable, with moderate fluctuations, an overall stable trajectory, and a safe distance from threat areas. Compared to the other five algorithms, IFLO achieves a shorter flight distance and lower energy consumption, demonstrating its relatively fast convergence speed and high stability, confirming its effectiveness in solving the global path planning problem for unmanned aerial vehicles. The above results show that IFLO has improved optimization ability and ability to escape local optimality compared with other models.

Claims

1. A path planning method based on the optimized model of the frilled lizard, characterized in that: The method comprises the following steps: S1. Obtain environmental information dataset; S2. Build and improve the lizard optimization model: S21. During the development phase of the optimization model for the frilled lizard, an adaptive strategy was added in parallel between the conventional strategy for calculating the new positions of individuals in the population and the module for updating the new positions of individuals in the population. Distribution mutation strategy and Levy path strategy; Setting the update policy threshold , Representation interval A randomly selected number from when When performing adaptive Distribution mutation strategy; Otherwise, the Levy path strategy is executed; S22. During the development phase of the optimization model for the frilled lizard, a Cauchy mutation strategy is added between the population individual new position update module and the population individual optimal position update module. S3. Iterative training of the lizard to optimize the improved model Second-rate; S4, optimize and improve the model planning path through the trained frilled lizard; The path planned by the trained optimization and improvement model of the frilled lizard is specifically as follows: S41, determine whether the distance between the sudden obstacle and the current position is less than the safety distance, if less than the safety distance, execute step S42, otherwise, maintain the original path; S42: Update the environmental information dataset to trigger the trained improved frilled lizard optimization model to plan a path.

2. The path planning method based on the optimized improved model of the frilled lizard according to claim 1 is characterized in that: The environmental information dataset represents the environment of the path to be planned. dimensional path model.

3. The path planning method based on the optimized improved model of the frilled lizard according to claim 2 is characterized in that: The adaptive The distribution mutation strategy is as follows: ,in, Indicates that the development phase is adaptive In the distribution mutation strategy The location of the frilled lizard, Represents a number of iterations distributed, represents the conventional strategy for calculating the new position of individuals in the population. The location of the frilled lizard, .

4. The path planning method based on the optimized improved model of the frilled lizard according to claim 3 is characterized in that: The Levy path strategy is specifically as follows: ,in, Indicates that the development stage is the first in the Levy path strategy The location of the frilled lizard, represents the position offset coefficient, Represents the Levy path mechanism, represents the random path length.

5. The path planning method based on the optimized improved model of the frilled lizard according to claim 4 is characterized in that: The population individual new position update module is specifically: ,in, express The evaluation function value of and Respectively and The evaluation function value of .

6. The path planning method based on the optimized improved model of the frilled lizard according to claim 5 is characterized in that: The Cauchy mutation strategy is specifically: ,in, Represents the first position determined by the population individual new position update module The location of the frilled lizard, represents the Cauchy distribution function, Indicates the development stage in the Cauchy mutation strategy The location of the frilled lizard.

7. The path planning method based on the optimized improved model of the frilled lizard according to claim 6 is characterized in that: The optimal position update module of the population individual is specifically: ,in, express The evaluation function value of express The evaluation function value of represents the first position determined by the population individual optimal position update module The location of the frilled lizard.

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