Self-adaptive dynamic autonomous obstacle avoidance method for land-air amphibious operation equipment

Abstract

The invention discloses a self-adaptive dynamic autonomous obstacle avoidance method for land-air amphibious operation equipment, and aims to solve the problem of application of an existing dynamic obstacle avoidance algorithm in the land-air amphibious operation equipment. According to the method, the speed sampling range is automatically adjusted according to the obstacle density through a self-adaptive dynamic window mechanism, the planning precision is improved when obstacles are dense, and the planning efficiency is improved when the obstacles are sparse. The method comprises three stages of collision risk assessment, self-adaptive dynamic sampling and motion trail prediction, the running distance and energy consumption of land and air dual modes are considered in a trail evaluation function, and path selection is optimized. The method can remarkably improve the obstacle avoidance capability of equipment in a complex environment, and is widely applied to the fields of unmanned operation robots, unmanned aerial vehicles and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of robot path planning and obstacle avoidance, and particularly to an adaptive dynamic autonomous obstacle avoidance method for an amphibious land-air operation equipment. Background Art

[0002] With the continuous progress of technology, especially the rapid development in the fields of automation, artificial intelligence, and sensing technology, amphibious land-air operation equipment (e.g., drones or robots capable of traveling on the ground and having flight capabilities) has been widely used in multiple industries, including rescue, inspection, exploration, environmental monitoring, agriculture, etc. Such equipment has flexible movement capabilities and can complete efficient tasks in complex environments. However, with the continuous expansion of its application scope, how to improve the obstacle avoidance ability of such operation equipment in complex environments, optimize path planning, and reduce energy consumption has become an important challenge in the current technological development.

[0003] The amphibious land-air operation equipment can work in two modes: on the ground and in the air, which gives it great advantages in complex and dynamic environments. However, in these environments, the operation equipment often faces dense obstacles, dynamically changing working conditions, and uncertain task objectives. Especially in a multi-obstacle environment, how to accurately and efficiently plan a path, avoid collisions, and not waste too much energy is an urgent problem to be solved.

[0004] Traditional path planning methods usually rely on static or pre-set environment models. When facing dynamic obstacles or complex environments, they often cannot respond quickly, and the efficiency of path planning is low. Moreover, it may even lead to excessive energy consumption of the operation equipment. In addition, existing obstacle avoidance technologies mostly focus on obstacle avoidance in a single motion mode, such as air obstacle avoidance or ground obstacle avoidance, and fail to effectively combine the two modes, especially there are great challenges during the switching process.

[0005] Currently, some technologies for obstacle avoidance of drones and robots have emerged on the market, including obstacle avoidance systems that integrate multiple sensors such as lidar (LiDAR), computer vision, and ultrasonic sensors. These technologies can perceive the environment in real time and detect and avoid obstacles based on the perceived data. However, existing technologies mainly focus on a single working mode, such as air obstacle avoidance or ground obstacle avoidance, and often cannot dynamically adjust the obstacle avoidance strategy according to the motion mode of the operation equipment.

[0006] Specifically, the common problems in existing technologies include:

[0007] Lack of flexibility in motion mode switching: In a multi-mode operation environment, existing technologies usually lack dynamic adaptability and cannot switch motion modes in real time according to environmental changes. For example, in a complex ground environment, the operating equipment may need to switch from ground travel to air flight mode, but traditional obstacle avoidance systems cannot efficiently complete this switch, resulting in low efficiency.

[0008] High energy consumption: Existing path planning methods do not fully consider the optimization of energy consumption. Especially in path planning in complex environments, it often leads to unnecessary energy waste.

[0009] The obstacle avoidance algorithm is not intelligent enough: Although sensor technology and obstacle avoidance algorithms have made great progress, most obstacle avoidance systems still rely on preset rules and models and lack adaptability based on real-time environmental feedback. Existing obstacle avoidance algorithms usually ignore the dynamic changes in the distribution of obstacles, resulting in low path planning efficiency.

[0010] Insufficient handling of dynamic obstacles: Most existing systems perform well in a static obstacle environment, but have weak handling capabilities for dynamic obstacles (such as moving vehicles, crowds, etc.). They cannot quickly respond to these changes, thus affecting the real-time performance and safety of operations.

[0011] The current technical problems to be solved are as follows:

[0012] Adaptive dynamic obstacle avoidance: How to design an obstacle avoidance system that can adaptively adjust according to real-time environmental changes and operation modes (such as air flight, ground travel) is still a difficult problem in current technology. This system should not only be able to avoid static obstacles, but also effectively predict and avoid dynamic obstacles, and adaptively adjust the obstacle avoidance strategy according to different obstacle densities to achieve a balance between obstacle avoidance efficiency and accuracy, ensuring that the operating equipment can complete tasks quickly and safely.

[0013] Motion mode fusion: How to efficiently switch between two motion modes, ground and air, and ensure the maximization of obstacle avoidance effect and energy efficiency during the switching process is an urgent problem to be solved in current technology. Different operation modes (such as flight and travel) have different physical characteristics, so different obstacle avoidance strategies are required.

[0014] Intelligent path planning and energy efficiency optimization: During obstacle avoidance, how to reasonably plan paths and reduce energy consumption, especially in complex environments, is still a direction that needs in-depth research. Existing path planning methods often ignore energy optimization, resulting in excessive energy consumption of operating equipment when performing tasks. Summary of the Invention

[0015] The present invention addresses the problems in the prior art and provides an adaptive dynamic autonomous obstacle avoidance method for a land-air amphibious operation equipment. This method designs an adaptive dynamic window mechanism that can adaptively change the size of the sampling window according to different obstacle environments. When there are many obstacles, a large-range speed sampling / 3D sampling is adopted to predict more motion trajectories to avoid collisions and improve the planning accuracy; when the obstacles are sparse, a small-range speed sampling / 2D sampling is adopted to reduce the number of samples and improve the planning efficiency. The above mechanism effectively achieves the balance between planning efficiency and accuracy. In addition, this method improves the trajectory evaluation function. Considering the land-air dual-mode motion characteristics of the land-air amphibious operation equipment, it designs a running distance evaluation term and a motion energy consumption evaluation term for different motion modes to achieve efficient and energy-saving dynamic path planning.

[0016] To achieve the above invention objectives, the technical solutions adopted by the present invention are as follows:

[0017] An adaptive dynamic autonomous obstacle avoidance method for a land-air amphibious operation equipment, comprising the following steps:

[0018] S1: Collision risk assessment stage: Grid the map, input the starting point and the ending point, use sensors to sense the surrounding environment in real time, obtain obstacle information, evaluate the collision risk between the operation equipment and the obstacles, and determine the high-risk area and low-risk area of the obstacles;

[0019] S2: Adaptive dynamic sampling stage: According to the collision risk assessment result of S1, use dynamic windows of different sizes for speed sampling. Among them, a large-range speed sampling window is used in the high-risk area for diverse trajectory prediction, that is, dynamic window A, and a small-range speed sampling window is used in the low-risk area for efficient trajectory prediction, that is, dynamic window B;

[0020] S3: Motion trajectory prediction stage: Based on the sampling results of S2, predict multiple motion trajectories of the operation equipment, use the improved trajectory evaluation function to evaluate different trajectories, and select the optimal trajectory to perform the obstacle avoidance operation.

[0021] Further, in the collision risk assessment stage, by modeling the gridded map of the obstacles and using the real-time data of the sensors, the positions of the dynamic obstacles are predicted, and the minimum distance between the operation equipment and the obstacles is calculated to judge the collision risk. The sensors include lidar, cameras, and ultrasonic sensors.

[0022] Further, the dynamic window A and dynamic window B in the adaptive dynamic sampling stage are adaptively adjusted according to the change of the obstacle density. Three-dimensional velocity space sampling is used in the area with dense obstacles, and two-dimensional velocity space sampling is used in the area with sparse obstacles.

[0023] Furthermore, the trajectory evaluation function in the motion trajectory prediction stage includes an azimuth evaluation term, a speed evaluation term, a running distance evaluation term, and a motion energy consumption evaluation term. The azimuth evaluation term considers the deviation between the current heading of the operation equipment and the target heading. The speed evaluation term considers the ground travel speed and the air flight speed of the operation equipment. The running distance evaluation term considers the ground travel distance and the air flight distance required for the operation equipment to move. The motion energy consumption evaluation term considers the energy consumption of the operation equipment in different motion modes.

[0024] Furthermore, the formula of the trajectory evaluation function is as follows:

[0025]

[0026] where H n is the azimuth evaluation term, V n is the speed evaluation term, D n is the running distance evaluation term, E n is the motion energy consumption evaluation term, a 1 , b 1 , c 1 , d 1 are the evaluation term parameters of the dynamic window A; a 2 , b 2 , c 2 , d 2 are the evaluation term parameters of the dynamic window B.

[0027] Furthermore, the azimuth evaluation term H n is calculated by the following formula:

[0028] H n = π - Y n - λ n

[0029] where Y n is the yaw correction term, representing the deviation angle between the current heading and the ideal heading, and θ n is the traditional azimuth evaluation term, representing the angle between the end point and the current heading of the operation equipment.

[0030] Furthermore, the formula of the speed evaluation term V n is as follows:

[0031]

[0032] where is the ground travel speed; is the vertical takeoff speed; is the forward flight speed; is the vertical landing speed; h nThe takeoff height required for the current obstacle faced; h m is the maximum takeoff height of the amphibious operation equipment.

[0033] Further, D n The specific formula of is as follows.

[0034]

[0035] Among them, D n-1 is the cumulative running distance; is the future predicted distance; is the required ground detour distance for the current obstacle faced; is the required forward flight distance for the current obstacle faced.

[0036] Further, E n The specific formula of is as follows.

[0037]

[0038]

[0039] Among them, E n-1 is the cumulative motion energy consumption; is the ground detour energy consumption corresponding to the current obstacle faced; is the takeoff and landing energy consumption corresponding to the current obstacle faced; is the vertical takeoff energy consumption; is the vertical landing energy consumption; is the forward flight energy consumption corresponding to the current obstacle faced. is the time required for ground detour; is the ground driving power; F g is the total ground driving force; a g is the ground driving acceleration; is the time required for vertical takeoff; is the vertical takeoff power; is the time required for vertical landing; is the vertical landing power; is the ducted propeller lift in the vertical takeoff stage; is the vertical takeoff acceleration; is the ducted propeller lift in the vertical landing stage; is the vertical landing acceleration; is the time required for forward flight; is the forward flight power; F f is the ducted propeller lift in the forward flight stage; a f is the forward flight acceleration.

[0040] Furthermore, the method is applicable to the autonomous obstacle avoidance of land-air amphibious operation equipment in an environment with dense obstacles, and dynamically adjusts the trajectory planning according to different motion modes, namely ground driving and air flight, so as to optimize the balance between obstacle avoidance efficiency and energy consumption.

[0041] Compared with the prior art, the advantages of the present invention are as follows:

[0042] 1. Strong adaptive obstacle avoidance ability

[0043] By real-time sensing the changes in the surrounding environment, the present invention can dynamically adjust the obstacle avoidance strategy. An adaptive dynamic window mechanism is designed, which can adaptively change the size of the sampling window according to different obstacle environments, plan the obstacle avoidance path, and effectively achieve the balance between obstacle avoidance efficiency and accuracy. This adaptive obstacle avoidance ability enables the operation equipment to efficiently and safely execute tasks in complex and changing environments, greatly improving the task completion efficiency and success rate.

[0044] 2. Efficient motion mode switching

[0045] The present invention takes into account the motion characteristics of the land-air dual-mode operation equipment, conducts cross-sampling in the two-dimensional velocity space and the three-dimensional velocity space, and can realize the efficient switching of the operation equipment between the two motion modes of ground and air. At the same time, the running distance and energy consumption of the land-air dual-mode are considered in the trajectory evaluation function, which can ensure the maximization of obstacle avoidance effect and energy efficiency during the switching process.

[0046] 3. Optimized energy consumption

[0047] The present invention takes into account the energy consumption of the operation equipment during the path planning process, enabling the equipment to minimize energy waste to the greatest extent when executing tasks. For example, during the obstacle avoidance process, the system will intelligently select the optimal driving route according to the type, position and dynamic changes of the obstacles, thereby improving the energy utilization efficiency. Especially when performing long-term and complex tasks, the present invention can effectively extend the battery life of the operation equipment and improve the overall operation ability.

[0048] 4. Enhanced dynamic obstacle handling ability

[0049] The obstacle avoidance system of the present invention can not only effectively handle static obstacles (such as buildings, trees, etc.), but also particularly optimize the detection and avoidance of dynamic obstacles (such as pedestrians, vehicles, etc.). It predicts the position of dynamic obstacles in real time, calculates the distance between the current moving position of the operation equipment and the nearest obstacle in real time, evaluates the collision risk, and then plans the obstacle avoidance path to avoid the occurrence of collision accidents.

[0050] 5. Adapt to complex and changing environments

[0051] Whether in the complex environment of high-rise buildings in the city or in the open fields or mountainous terrains, the obstacle avoidance method of the present invention can maintain high stability and accuracy. Through multi-sensor data fusion, environmental feature recognition, and adaptive algorithm adjustment, this method can operate efficiently in various complex environments, enhancing the versatility and adaptability of the operation equipment.

[0052] 6. Improve task safety

[0053] Through a comprehensive obstacle avoidance system and intelligent path planning, when the operation equipment faces unknown or dynamically changing obstacles, it can quickly respond and make a safe avoidance. In this way, the risks of collisions and accidents during the operation process are greatly reduced, ensuring the safety of the equipment and the lives of the operation personnel.

[0054] 7. Flexible multi-task execution ability

[0055] Due to the good self-adaptability and efficient path planning ability of the obstacle avoidance system of the present invention, the operation equipment can execute multiple tasks simultaneously or automatically adjust in different task modes. For example, during the inspection task, the operation equipment can efficiently avoid obstacles and complete the task, and at the same time, it can quickly switch to other task modes (such as data collection, emergency response, etc.) according to needs, greatly improving the flexibility and response speed of task processing.

[0056] 8. Improve operation efficiency

[0057] Through intelligent obstacle avoidance and path planning, the present invention can help the operation equipment reduce unnecessary detours and shorten the task completion time. Especially in the case of complex or dynamically changing environments, it can maintain high efficiency, enhance the comprehensive performance of the operation equipment, and enable it to achieve the best performance in various practical applications.

[0058] 9. Reduce costs

[0059] The technology of the present invention can significantly improve the efficiency and energy efficiency of the operation equipment, thereby reducing resource waste and energy consumption during the operation process and lowering the operation cost. In addition, by improving the safety and stability of the operation equipment, the occurrence of equipment damage and failures can be reduced, further lowering the maintenance and repair costs. Brief description of the drawings

[0060] Figure 1 is the flowchart of the adaptive dynamic autonomous obstacle avoidance method for the land-air amphibious operation equipment in the embodiment of the present invention.

[0061] Figure 2 is the schematic diagram of the adaptive dynamic window in the embodiment of the present invention. Detailed implementation manners

[0062] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the following further describes the present invention in detail according to the accompanying drawings and by way of examples.

[0063] As Figure 1 shown, the present invention provides an adaptive dynamic autonomous obstacle avoidance method for an amphibious land-air operation equipment, including the following steps:

[0064] Step1: Collision risk assessment stage. Grid the map, input the starting point and the ending point, use sensors (such as lidar, camera, ultrasonic sensor, etc.) to sense the surrounding environment in real time, detect the positions of static obstacles, and predict the positions of dynamic obstacles in real time. Set an obstacle collision threshold, and calculate the distance between the current moving position of the amphibious land-air operation equipment and the nearest obstacle in real time to evaluate the collision risk. When the above distance is lower than the collision threshold, the amphibious land-air operation equipment has a high collision risk; otherwise, it has a low collision risk.

[0065] Step2: Adaptive dynamic sampling stage. Design an adaptive dynamic window mechanism. For the obstacle area with high collision risk, use dynamic window A for speed sampling; for the obstacle area with low collision risk, sample dynamic window B for speed sampling.

[0066] Generally, in the obstacle area with high collision risk, the obstacles are dense. Dynamic window A is a large-range speed sampling, and the window contains more speed combinations (linear velocity and angular velocity). More motion trajectories can be predicted under different speed combinations, and the amphibious land-air operation equipment can select the best motion trajectory from them to achieve optimal dynamic obstacle avoidance in the dense obstacle environment. In addition, the amphibious land-air operation equipment has both land and air motion modes. Facing dense obstacles, it has two options: ground detour obstacle avoidance and air flight over obstacle. During the air flight over obstacle process, the amphibious land-air operation equipment needs to go through motion stages such as vertical takeoff, forward flight, and vertical landing. In the above stages, dynamic window A is a three-dimensional speed space sampling. In this window, multiple groups of linear velocity and angular velocity are randomly sampled to predict the flight motion trajectories under different speed combinations to achieve optimal air flight over obstacle.

[0067] In the obstacle area with low collision risk, the obstacles are sparse, and the amphibious land-air operation equipment does not need to avoid obstacles frequently. At this time, the amphibious land-air operation equipment does not need too much speed sampling, and it can move forward quickly along the motion trajectory towards the ending point. Therefore, dynamic window B is a small-range speed sampling, and the sampling number is reduced by means of this window to improve the planning efficiency. In addition, the air flight over obstacle of the amphibious land-air operation equipment will cause a large amount of energy consumption. From the perspective of energy conservation, ground travel is the main motion mode of the amphibious land-air operation equipment. Facing sparse obstacles, the amphibious land-air operation equipment will maintain the ground travel mode and take ground detour obstacle avoidance. At this time, dynamic window B is a two-dimensional speed space sampling.

[0068] The schematic diagram of the adaptive dynamic window is as follows Figure 2 shown as

[0069] Step3: Motion trajectory prediction stage. Based on the velocity combinations randomly sampled in Step2, multiple possible motion trajectories of the amphibious operation equipment at the next moment are predicted. The improved trajectory evaluation function is used to quantify the advantages and disadvantages of different predicted trajectories, and the optimal motion trajectory is found from them to achieve the optimal dynamic obstacle avoidance of the amphibious operation equipment.

[0070] Considering the motion characteristics of the amphibious operation equipment in both land and air modes, the improved trajectory evaluation function adds the running distance evaluation item and the motion energy consumption evaluation item for different motion modes. In addition, the improved trajectory evaluation function also includes the improved azimuth angle evaluation item and the velocity evaluation item. The specific formula is as follows.

[0071]

[0072] Among them, a 1 , b 1 , c 1 , and d 1 are the evaluation item parameters of the dynamic window A; a 2 , b 2 , c 2 , and d 2 are the evaluation item parameters of the dynamic window B; a 1 ≤a 2 b 1 <<b 2 c 1 >>c 2 d 1 >>d 2 ; G n is the improved azimuth angle evaluation item; V n is the improved velocity evaluation item; D n is the newly added running distance evaluation item; E n is the newly added motion energy consumption evaluation item.

[0073] H n The specific formula is as follows.

[0074] H n =π - Y n - θ n

[0075]

[0076] Among them, θ n is the traditional azimuth angle evaluation item, which represents the angle of the end point relative to the heading of the amphibious operation equipment; Y nIt is a newly added yaw correction item after improvement, which represents the deviation angle between the current heading and the ideal heading (the line connecting the starting point and the ending point). This improvement helps to achieve large yaw correction for the land-air amphibious operation equipment in the direction away from the ending point; P E is the ending point coordinate; P S is the starting point coordinate; P n is the current position coordinate.

[0077] V n The specific formula is as follows.

[0078]

[0079] Among them, is the ground travel speed; is the vertical takeoff speed; is the forward flight speed; is the vertical landing speed; h n is the required takeoff height for the current obstacle faced; h m is the maximum takeoff height of the land-air amphibious operation equipment. Compared with the traditional speed evaluation item, the improved speed evaluation item adds the consideration of speeds in different land-air movement stages, which is more in line with the movement characteristics of the land-air dual-mode of the land-air amphibious operation equipment.

[0080] D n The specific formula of D is as follows.

[0081]

[0082] Among them, D n-1 is the cumulative running distance; is the future predicted distance; is the required ground detour distance for the current obstacle faced; is the required forward flight distance for the current obstacle faced.

[0083] E n The specific formula of E is as follows.

[0084]

[0085]

[0086] Among them, E n-1 is the cumulative motion energy consumption; is the ground detour energy consumption corresponding to the current obstacle faced; is the takeoff and landing energy consumption corresponding to the current obstacle faced; is the vertical takeoff energy consumption; is the vertical landing energy consumption; is the forward flight energy consumption corresponding to the current obstacle faced. is the time required for ground detouring; is the ground driving power; F g is the total driving force for ground driving; a g is the ground driving acceleration; is the time required for vertical takeoff; is the vertical takeoff power; is the time required for vertical landing; is the vertical landing power; is the ducted propeller lift during the vertical takeoff phase; is the vertical takeoff acceleration; is the ducted propeller lift during the vertical landing phase; is the vertical landing acceleration; is the time required for forward flight; is the forward flight power; F f is the ducted propeller lift during the forward flight phase; a f is the forward flight acceleration.

[0087] The method according to the present invention described above can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or be implemented as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored on such a software process on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (such as a RAM, ROM, flash memory, etc.) that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, an adaptive dynamic autonomous obstacle avoidance method for an amphibious land-air operation equipment described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the process shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the process shown herein.

[0088] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the implementation method of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. An adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment, characterized in that: The following steps are involved: S1: Collision risk assessment stage: rasterize the map, input the starting point and end point, use sensors to perceive the surrounding environment in real time, obtain obstacle information, assess the collision risk between the operating equipment and obstacles, and determine the high-risk and low-risk areas of obstacles; S2: Adaptive dynamic sampling stage: According to the collision risk assessment results of S1, dynamic windows of different sizes are used for speed sampling. A large-scale speed sampling window is used in high-risk areas for diversified trajectory prediction, namely dynamic window A, and a small-scale speed sampling window is used in low-risk areas for efficient trajectory prediction, namely dynamic window B. S3: Motion trajectory prediction stage: Based on the sampling results of S2, predict the multiple motion trajectories of the operating equipment, use the improved trajectory evaluation function to evaluate different trajectories, and select the optimal trajectory to perform obstacle avoidance operations.

2. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 1 is characterized by: The collision risk assessment stage determines the collision risk by modeling the raster map of obstacles, using the real-time data of sensors, predicting the position of dynamic obstacles, and calculating the minimum distance between the operating equipment and the obstacles. The sensors include: lidar, camera and ultrasonic.

3. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 1 is characterized by: The dynamic window A and the dynamic window B in the adaptive dynamic sampling stage are adaptively adjusted according to the change of obstacle density. The obstacle-dense area uses three-dimensional speed space sampling, and the obstacle-sparse area uses two-dimensional speed space sampling.

4. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 1, characterized in that: The trajectory evaluation function in the motion trajectory prediction stage includes an azimuth evaluation item, a speed evaluation item, a running distance evaluation item and a motion energy consumption evaluation item. The azimuth evaluation item takes into account the deviation between the current heading of the operating equipment and the target heading. The speed evaluation item takes into account the ground driving speed and the air flying speed of the operating equipment. The running distance evaluation item takes into account the ground driving distance and the air flying distance required for the movement of the operating equipment. The motion energy consumption evaluation item takes into account the energy consumption of the operating equipment under different motion modes.

5. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 1, characterized in that: The formula of the trajectory evaluation function is as follows: Among them, H n is the azimuth evaluation item, V n is the speed evaluation item, D n is the running distance evaluation item, E n is the evaluation item of sports energy consumption, a1, b1, c1, d1 are the evaluation item parameters of dynamic window A; a2, b2, c2, d2 are the evaluation item parameters of dynamic window B.

6. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 5, characterized in that: The azimuth evaluation item H n Calculated by the following formula: H n =π-Y n -θ n Among them, Y n is the yaw correction term, which indicates the deviation angle between the current heading and the ideal heading, θ n It is a traditional azimuth evaluation item, which indicates the angle between the end point and the current heading of the operating equipment.

7. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 5, characterized in that: The speed evaluation item V n The formula is as follows: in, is the ground speed; is the vertical takeoff speed; is the forward flight speed; is the vertical landing speed; h n h is the required take-off height due to the current obstacles; m The maximum take-off altitude is equipped for amphibious operations.

8. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 5, characterized in that: D n The specific formula is as follows; Among them, D n-1 is the cumulative running distance; predicting distances for the future; The ground detour distance required for the current obstacle; The forward flight distance required to meet the current obstacle.

9. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 5, characterized in that: E n The specific formula is as follows; Among them, E n-1 To accumulate exercise energy consumption; The ground detour energy consumption corresponding to the current obstacle; The take-off and landing energy consumption corresponding to the current obstacle; is the energy consumption for vertical takeoff; is the energy consumption for vertical landing; is the forward flight energy consumption corresponding to the current obstacle; The time required for ground orbit; is the ground driving power; F g is the total driving force for ground travel; a g is the ground acceleration; The time required for vertical takeoff; is the vertical take-off power; The time required for vertical landing; is the vertical landing power; is the ducted propeller lift during the vertical takeoff phase; is the vertical takeoff acceleration; is the ducted propeller lift during the vertical landing phase; is the vertical landing acceleration; The time required for forward flight; is the forward flight power; F f is the ducted propeller lift during the forward flight phase; a f is the forward flight acceleration.

10. The adaptive dynamic autonomous obstacle avoidance method for amphibious operation equipment according to claim 1, characterized in that: The method is suitable for autonomous obstacle avoidance of amphibious operation equipment in an obstacle-dense environment, and dynamically adjusts trajectory planning according to different motion modes, namely ground driving and air flight, to optimize the balance between obstacle avoidance efficiency and energy consumption.

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