Risk-avoiding trajectory planning method for emergency fault of unmanned aerial vehicle
By establishing a drone evasion model and splitting and optimizing the flight trajectory, combined with the emergency factor rating, the problem of drone being difficult to return safely in emergency situations is solved, and the optimal plan with the lowest drone crash losses is achieved.
Patent Information
- Application Number
- CN202510342784.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-21
- Publication Date
- 2025-06-10
AI Technical Summary
The lack of systematic analysis methods in the prior art makes it difficult for drones to return safely when they encounter external factors, and are prone to falling due to operational errors, resulting in casualties and economic losses.
By establishing a drone evasion model, classifying and rating flight obstacles based on historical flight records, splitting and optimizing flight trajectory, formulating a fall trajectory planning plan based on emergency factor ratings, and obtaining the optimal plan for drone falls.
It has achieved rapid self- and environmental analysis of drones when encountering emergencies, formulate optimal flight trajectory plans for falling possibility, and reduce losses suffered in drones' flight trajectory.
Smart Images
Figure CN120122696A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of UAV risk avoidance, and particularly to a method for planning an avoidance trajectory for emergency faults of UAVs. Background Art
[0002] With the rapid development of UAV-related technologies, UAV systems are diverse in types and distinct in wide applications. During daily flight of UAVs, they are remotely controlled by humans, thus having quite large blind spots and operation difficulties. Therefore, during actual operations, UAVs often lose balance due to hitting a small branch, wire, or obstacle, or the propellers are damaged by collision, making it impossible for them to regain balance again and fall. The result of the fall is often that the entire UAV is smashed to pieces and has no value for repair, and the user suffers huge economic losses.
[0003] Currently, during the flight of UAVs, due to the lack of a relatively systematic technical analysis method, when UAV operators encounter external factors, they only want the UAV to return to land around them faster. However, due to unexpected situations of the UAV, it is very difficult for the UAV to return to the takeoff location. Instead, due to the operations of UAV operators, the UAV may directly fall in some dangerous places, resulting in more serious economic losses and even casualties. Therefore, a relatively systematic and scientific method for analyzing and planning an emergency flight route for UAVs to conduct rational analysis in case of emergencies is very necessary. Summary of the Invention
[0004] The present invention aims to provide a method for planning an avoidance trajectory for emergency faults of UAVs, which solves the problem in the prior art that the analysis of the UAV recovery route with the least loss of UAV fall is not considered, resulting in greater casualties and economic losses caused by the fallen UAVs.
[0005] To achieve the above object, the present invention provides the following method: A method for planning an avoidance trajectory for emergency faults of UAVs provided by the present invention is as follows: S1: Establish a UAV avoidance model, obtain UAV flight obstacles according to the historical flight records of the UAV, classify the UAV flight obstacles, and rate the UAV flight obstacles; S2: Plan a UAV flight trajectory scheme, where the UAV trajectory scheme includes multiple UAV flight trajectories, and each UAV flight trajectory is split into multiple UAV flight avoidance trajectory segments; S3: Package the multiple UAV flight avoidance trajectory segments split from each UAV flight trajectory with the UAV flight obstacles in the corresponding UAV flight trajectory scheme to obtain multiple UAV flight obstacle avoidance data packets; S4: Based on each of the UAV flight obstacle avoidance data packets, conduct an emergency factor rating in combination with the UAV flight environment; S5: Based on the emergency factor rating, formulate a UAV fall trajectory planning pre - plan to obtain multiple UAV fall risk avoidance parameters; S6: Based on the UAV fall risk avoidance parameters, obtain the optimal UAV fall plan.
[0006] Preferably, the steps of establishing a UAV avoidance model, obtaining UAV flight obstacles according to the UAV historical flight records, classifying the UAV flight obstacles, and rating the UAV flight obstacles include: establishing a UAV avoidance model, establishing an environmental three - dimensional coordinate system with the UAV starting flight position as the coordinate origin, and constructing a UAV flight curve according to the UAV flight trajectory; obtaining UAV flight obstacles according to the UAV historical flight records, classifying the UAV flight obstacles according to the influence degree on different stages of take - off, landing, and in - flight of the UAV flight process; rating the influence degree of the UAV flight obstacles on the UAV flight, and the greater the influence degree on the UAV flight, the higher the rating of the UAV flight obstacle.
[0007] Preferably, for the step of planning the UAV flight trajectory plan, where the UAV trajectory plan includes multiple UAV flight trajectories and each UAV flight trajectory is split into multiple UAV flight avoidance trajectory segments, it includes: planning the UAV flight curve, and listing each UAV flight curve as a UAV flight trajectory; equally dividing and cutting the UAV flight trajectory according to the length of the UAV flight curve to obtain the first UAV flight trajectory segment; equally dividing and cutting the flight trajectory according to the duration of the UAV flight curve to obtain the second UAV flight trajectory segment; integrating the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments.
[0008] Preferably, the step of integrating the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments includes: overlapping the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain an overall UAV flight trajectory segment; judging whether the distance of each overall UAV flight trajectory segment is < UAV flight trajectory / the number of overall UAV flight trajectory segments; if the distance of the overall UAV flight trajectory segment is < UAV flight trajectory / the number of overall UAV flight trajectory segments, then merge this overall UAV flight trajectory segment with the adjacent overall UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments.
[0009] Preferably, the step of encapsulating data of each of the multi-segment UAV flight avoidance trajectory segments obtained by splitting the UAV flight trajectory with the corresponding UAV flight obstacles in the UAV flight trajectory plan to obtain a plurality of UAV flight obstacle avoidance data packets includes: determining the UAV flight obstacles for each of the multi-segment UAV flight avoidance trajectory segments obtained by splitting the UAV flight trajectory; marking the UAV flight avoidance trajectory segments where the rating of the UAV flight obstacles appears higher than the average rating of the UAV flight obstacles as high-risk UAV flight avoidance trajectory segments; marking the UAV flight avoidance trajectory segments where the rating of the UAV flight obstacles appears lower than the average rating of the UAV flight obstacles as low-risk UAV flight avoidance trajectory segments; calculating the proportion of the high-risk UAV flight avoidance trajectory segments and the low-risk UAV flight avoidance trajectory segments in each UAV flight trajectory to obtain the UAV flight avoidance trajectory risk proportion; encapsulating data of the plurality of UAV flight avoidance trajectory risk proportions, the UAV flight avoidance trajectory segments, and the corresponding UAV flight obstacles to obtain a plurality of UAV flight obstacle avoidance data packets.
[0010] Preferably, the step of rating the emergency situation factors according to each of the UAV flight obstacle avoidance data packets in combination with the UAV flight environment includes: judging and rating according to the UAV flight avoidance trajectory risk proportion and the UAV flight duration of the UAV flight avoidance trajectory segment in the UAV flight obstacle avoidance data packet; sorting each of the UAV flight trajectory risk proportions in ascending order according to the ratio of high-risk UAV flight avoidance trajectory segments to low-risk UAV flight avoidance trajectory segments to obtain the UAV flight trajectory safety ranking; sorting the UAV flight durations of each of the UAV flight avoidance trajectory segments in ascending order according to the length of time to obtain the UAV flight duration ranking; analyzing according to the UAV flight environment to obtain the UAV flight trajectory environment coefficient ranking; performing cumulative rating of the UAV flight obstacle avoidance data packets corresponding to the UAV flight trajectory safety ranking, the UAV flight duration ranking, and the UAV flight trajectory environment coefficient ranking, and the lower the cumulative order obtained, the higher the rating of the emergency situation factors of the UAV flight obstacle avoidance data packet, to obtain the emergency situation factor rating.
[0011] Preferably, the step of analyzing according to the UAV flight environment to obtain the sorting of the UAV flight trajectory environment coefficients includes: collecting the environment of the UAV flight, and collecting the environment of the UAV flight according to the UAV flight obstacle avoidance data packets in three aspects: UAV takeoff, UAV flight in the middle, and UAV landing; the influence degrees of different UAV flight obstacle avoidance data packets on UAV takeoff, UAV flight in the middle, and UAV landing are comprehensively considered according to the ratio of UAV takeoff: UAV flight in the middle: UAV landing = 2:6:2 to obtain the sorting of the UAV flight trajectory environment coefficients.
[0012] Preferably, the step of formulating a UAV fall trajectory planning plan according to the emergency situation factor rating to obtain multiple UAV fall risk avoidance parameters includes: simulating the UAV fall point according to the emergency situation factor rating combined with the environmental three-dimensional coordinate system; analyzing the environmental coefficients of different UAV flight obstacle avoidance data packets when the UAV falls at different positions; formulating a UAV fall trajectory planning plan according to the environmental coefficients; obtaining the UAV collision coefficient according to the damage degree of the UAV when it collides with the environment at different positions during the UAV fall; obtaining the UAV recovery coefficient according to the difficulty degree of UAV recovery when the UAV falls at different positions; obtaining the UAV secondary damage coefficient according to the possibility of the UAV experiencing secondary damage after falling.
[0013] Preferably, the environmental coefficients include: the UAV fall coefficient caused by sudden collisions encountered by the UAV at different positions; the UAV sudden environment coefficient when the UAV flight process is affected by environmental changes during the UAV flight; the weather influence coefficient when the UAV is affected by weather changes during the flight; the signal interference coefficient when the UAV is affected by electrical signal interference during the flight.
[0014] Preferably, the step of obtaining the optimal UAV fall plan according to the UAV fall risk avoidance parameters includes: screening and sorting the UAV flight obstacle avoidance data packets according to the UAV collision coefficient, the UAV recovery coefficient, and the UAV secondary damage coefficient to obtain the sorting of the UAV fall risk avoidance parameters; optimizing the UAV flight trajectory according to the sorting of the UAV fall risk avoidance parameters, and optimizing the UAV flight avoidance trajectory segments in the UAV flight trajectory where the UAV collision coefficient, the UAV recovery coefficient, and the UAV secondary damage coefficient are higher than the average UAV fall risk avoidance parameters to obtain the optimal UAV fall plan.
[0015] The beneficial effects of the present invention are reflected in that: by establishing a UAV avoidance model, the present invention scores the obstacles during the UAV flight process, enabling the UAV to perform optimal avoidance according to the scores of the obstacles during obstacle avoidance, rather than blindly avoiding, which has a greater impact on the UAV flight process. Then, the UAV flight obstacles, routes, and risk ratios are encapsulated, making the relevant data of each UAV trajectory presented in a modularized manner, which is more convenient for data modification and comparison. Then, the UAV flight trajectory is divided into multiple small segments for split analysis, making the analysis of the UAV trajectory's falling situation more thorough, rather than generally analyzing the entire flight trajectory. The screening of the UAV flight trajectory situation in this application is more systematic. Finally, through multiple UAV coefficients, the UAV falling risk avoidance parameters are obtained, considering the situation of UAV falling impact, the difficulty of UAV recovery, and the analysis of the possibility of secondary damage to the UAV. Ultimately, the optimal UAV falling plan is obtained, enabling the UAV to quickly analyze its own and environmental factors in case of emergencies and make the corresponding optimal falling possibility flight trajectory plan, minimizing the losses suffered in the UAV flight trajectory. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to actual scale.
[0017] Figure 1 It is a schematic flowchart of a method for planning an avoidance trajectory of an emergency failure of a UAV provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0018] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0019] In the description, claims, and above-mentioned drawings of the present invention, terms such as "first", "second", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or terminal that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products, or terminals.
[0020] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.
[0021] Currently, during the flight of an unmanned aerial vehicle (UAV), due to the lack of a relatively systematic technical analysis method, when UAV operators encounter external factors, they only want the UAV to return and land around them faster. However, due to unexpected situations encountered by the UAV, it is very difficult for the UAV to return to the takeoff location. Instead, due to the operations of the UAV operator, the UAV may directly crash in some dangerous places, resulting in more serious economic losses and even casualties. Therefore, a relatively systematic and scientific emergency flight route planning and analysis method for rational analysis of UAVs in case of emergencies is very necessary.
[0022] The present invention aims to provide a method for planning an avoidance trajectory for UAV emergency failures, which solves the problem in the prior art that the analysis of the UAV recovery route without considering the least loss of UAV crashes leads to greater casualties and economic losses caused by the crashed UAVs.
[0023] As Figure 1 shown, the specific implementation manner of the present invention provides a method for planning an avoidance trajectory for UAV emergency failures, including the following steps: S1: Establish a UAV avoidance model, obtain UAV flight obstacles according to the historical flight records of the UAV, classify the UAV flight obstacles, and rate the UAV flight obstacles.
[0024] In an embodiment of the present invention, a UAV avoidance model is established. An environmental three-dimensional coordinate system is established with the starting flight position of the UAV as the coordinate origin, and a UAV flight curve is constructed according to the UAV flight trajectory; the UAV flight obstacles are obtained from the UAV historical flight records, and the UAV flight obstacles are classified according to the influence degrees on different stages of takeoff, landing, and in-flight of the UAV flight process; the influence degrees of the UAV flight obstacles on the UAV flight are rated, and the greater the influence degree on the UAV flight, the higher the rating of the UAV flight obstacle.
[0025] S2: Plan a UAV flight trajectory plan. The UAV trajectory plan includes multiple UAV flight trajectories, and each UAV flight trajectory is split into multiple UAV flight avoidance trajectory segments.
[0026] In an embodiment of the present invention, the UAV flight curve is planned. Each UAV flight curve is listed as a UAV flight trajectory; the UAV flight trajectory is equally divided according to the length of the UAV flight curve to obtain the first UAV flight trajectory segment; the flight trajectory is equally divided according to the duration of the UAV flight curve to obtain the second UAV flight trajectory segment; the steps of integrating the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments include: overlapping the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain an overall UAV flight trajectory segment; judging whether the distance of each overall UAV flight trajectory segment is < UAV flight trajectory / number of overall UAV flight trajectory segments; if the distance of the overall UAV flight trajectory segment is < UAV flight trajectory / number of overall UAV flight trajectory segments, then merge the overall UAV flight trajectory segment with the adjacent overall UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments.
[0027] S3: Package the multiple UAV flight avoidance trajectory segments split from each UAV flight trajectory with the UAV flight obstacles in the corresponding UAV flight trajectory plan to obtain multiple UAV flight obstacle avoidance data packets.
[0028] In an embodiment of the present invention, for each of the multiple segments of the UAV flight avoidance trajectory obtained by splitting the UAV flight trajectory, a UAV flight obstacle determination is performed; the rating of the UAV flight obstacle that appears in the UAV flight avoidance trajectory segment is compared with the average rating of the UAV flight obstacle. If the rating is higher than the average rating of the UAV flight obstacle, the UAV flight avoidance trajectory segment is marked as a high-risk UAV flight avoidance trajectory segment; if the rating is lower than the average rating of the UAV flight obstacle, the UAV flight avoidance trajectory segment is marked as a low-risk UAV flight avoidance trajectory segment; the proportion of the high-risk UAV flight avoidance trajectory segments and the low-risk UAV flight avoidance trajectory segments in each UAV flight trajectory is calculated to obtain the risk ratio of the UAV flight avoidance trajectory; the risk ratios of multiple UAV flight avoidance trajectories, the UAV flight avoidance trajectory segments, and the corresponding UAV flight obstacles are encapsulated to obtain multiple UAV flight obstacle avoidance data packets.
[0029] S4: According to each UAV flight obstacle avoidance data packet, combined with the UAV flight environment, an emergency factor rating is performed.
[0030] In an embodiment of the present invention, a judgment rating is performed according to the risk ratio of the UAV flight avoidance trajectory in the UAV flight obstacle avoidance data packet and the UAV flight duration of the UAV flight avoidance trajectory segment; the risk ratio of each UAV flight trajectory is sorted in ascending order according to the ratio of high-risk UAV flight avoidance trajectory segments to low-risk UAV flight avoidance trajectory segments to obtain the safety ranking of the UAV flight trajectory; the UAV flight durations of each UAV flight avoidance trajectory segment are sorted in ascending order according to the length of time to obtain the UAV flight duration ranking; an analysis is performed according to the UAV flight environment to obtain the UAV flight trajectory environment coefficient ranking; the steps of obtaining the UAV flight trajectory environment coefficient ranking by analyzing the UAV flight environment include: collecting the environment of the UAV flight, and collecting the environment of the UAV flight obstacle avoidance data packet of the UAV flight according to three aspects: UAV takeoff, UAV flight in the air, and UAV landing; the influence degrees of the UAV takeoff, UAV flight in the air, and UAV landing of different UAV flight obstacle avoidance data packets on the environment are comprehensively considered according to the ratio of UAV takeoff: UAV flight in the air: UAV landing = 2:6:2 to obtain the UAV flight trajectory environment coefficient ranking; the UAV flight obstacle avoidance data packets corresponding to the UAV flight trajectory safety ranking, the UAV flight duration ranking, and the UAV flight trajectory environment coefficient ranking are cumulatively rated by order. The lower the cumulative order, the higher the emergency factor rating of the UAV flight obstacle avoidance data packet, and the emergency factor rating is obtained.
[0031] S5: According to the emergency factor rating, a UAV fall trajectory planning pre-scheme is formulated to obtain multiple UAV fall avoidance parameters.
[0032] In an embodiment of the present invention, the falling point of the unmanned aerial vehicle (UAV) is simulated according to the emergency factor rating in combination with the three-dimensional environmental coordinate system; the environmental coefficients of the UAV falling at different positions are analyzed for different UAV flight obstacle avoidance data packets; the environmental coefficients include: the UAV falling coefficient caused by sudden collisions encountered by the UAV at different positions; the UAV sudden environmental coefficient affected by environmental changes during the UAV flight process; the weather influence coefficient affected by weather changes during the UAV flight process; the signal interference coefficient affected by electrical signal interference during the UAV flight process; a UAV falling trajectory planning pre-plan is formulated according to the environmental coefficients; the UAV collision coefficient is obtained according to the damage degree of the UAV when the UAV falls at different positions and collides with the environment; the UAV recovery coefficient is obtained according to the difficulty of UAV recovery when the UAV falls at different positions; the UAV secondary damage coefficient is obtained according to the possibility of the UAV experiencing secondary damage after falling.
[0033] S6: Obtain the optimal UAV falling plan according to the UAV falling risk avoidance parameters.
[0034] In an embodiment of the present invention, according to the UAV collision coefficient, the UAV recovery coefficient, and the UAV secondary damage coefficient, the UAV flight obstacle avoidance data packets are screened and sorted to obtain the sorting of the UAV falling risk avoidance parameters; the UAV flight trajectory is optimized according to the sorting of the UAV falling risk avoidance parameters, and the UAV flight avoidance trajectory segments with UAV collision coefficients, UAV recovery coefficients, and UAV secondary damage coefficients higher than the average UAV falling risk avoidance parameters in the UAV flight trajectory are optimized to obtain the optimal UAV falling plan.
[0035] The beneficial effects of the present invention are reflected in: by establishing a UAV avoidance model, the present invention scores the obstacles in the UAV flight process, enabling the UAV to perform optimal avoidance according to the scores of the obstacles during obstacle avoidance, rather than blindly avoiding, which may have a greater impact on the UAV flight process. Then, the UAV flight obstacles, routes, and risk ratios are encapsulated, making the relevant data of each UAV trajectory presented in a modularized manner, facilitating data modification and comparison. Then, the UAV flight trajectory is divided into multiple small segments for splitting and analysis, making the analysis of the UAV trajectory falling situation more thorough, rather than generally analyzing the entire flight trajectory. The screening of the UAV flight trajectory situation in this application is more systematic. Finally, the UAV falling risk avoidance parameters are obtained through various UAV coefficients, considering the UAV falling impact situation, the difficulty of UAV recovery, and the analysis of the possibility of UAV secondary damage. Ultimately, the optimal UAV falling plan is obtained, enabling the UAV to quickly analyze its own and environmental factors in case of emergencies and make the corresponding optimal falling possibility flight trajectory plan, minimizing the losses suffered in the UAV flight trajectory.
[0036] The above are only embodiments of the present invention, and specific technical solutions or common knowledge such as characteristics known in the art are not described in detail herein. It should be noted that for those skilled in the art, without departing from the scope of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effects of the present invention and the practicality of the patent. The protection scope claimed in this application shall be subject to the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.
Claims
1. A method for avoiding trajectory planning for emergency failure of unmanned aerial vehicles, characterized in that: The method comprises: S1: Establish a drone avoidance model, obtain drone flight obstacles based on the drone's historical flight records, classify the drone flight obstacles, and rate the drone flight obstacles; S2: planning a UAV flight trajectory plan, wherein the UAV trajectory plan includes multiple UAV flight trajectories, and splitting each UAV flight trajectory into multiple UAV flight avoidance trajectory segments; S3: encapsulating the multiple drone flight avoidance trajectory segments split from each drone flight trajectory with the drone flight obstacles in the corresponding drone flight trajectory scheme to obtain multiple drone flight obstacle avoidance data packets; S4: Rating emergency factors according to each of the UAV flight obstacle avoidance data packets combined with the UAV flight environment; S5: Formulate a UAV crash trajectory planning plan based on the emergency factor rating to obtain multiple UAV crash avoidance parameters; S6: Obtaining an optimal plan for the drone crash according to the drone crash avoidance parameters.
2. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 1 is characterized in that: The steps of establishing a drone avoidance model, obtaining drone flight obstacles according to the drone historical flight records, classifying the drone flight obstacles, and rating the drone flight obstacles include: Establish a drone avoidance model, establish an environmental three-dimensional coordinate system with the drone's starting flight position as the coordinate origin, and construct the drone's flight curve based on the drone's flight trajectory; Obtaining drone flight obstacles based on the drone's historical flight records, and classifying the drone flight obstacles according to their impact on the drone's takeoff, landing, and different stages of flight; The degree of influence of the UAV flight obstacle on the UAV flight is rated. The greater the influence on the UAV flight, the higher the rating of the UAV flight obstacle.
3. The method for avoiding danger trajectory of an unmanned aerial vehicle emergency failure according to claim 2 is characterized in that: The step of planning a UAV flight trajectory plan, wherein the UAV trajectory plan includes a plurality of UAV flight trajectories, and splitting each UAV flight trajectory into a plurality of UAV flight avoidance trajectory segments, comprises: Planning the UAV flight curves, and listing each UAV flight curve as a UAV flight trajectory; Dividing the UAV flight trajectory into equal parts according to the length of the UAV flight curve to obtain a first UAV flight trajectory segment; Divide the flight trajectory into equal parts according to the duration of the UAV flight curve to obtain a second UAV flight trajectory segment; The first UAV flight trajectory segment and the second UAV flight trajectory segment are integrated to obtain a plurality of UAV flight avoidance trajectory segments.
4. The method for avoiding a risk trajectory of an emergency failure of a UAV according to claim 3 is characterized in that: The step of integrating the first UAV flight trajectory segment and the second UAV flight trajectory segment to obtain a plurality of UAV flight avoidance trajectory segments includes: Overlapping the first UAV flight trajectory segment with the second UAV flight trajectory segment to obtain an overall UAV flight trajectory segment; Determine whether the distance of each segment of the overall UAV flight trajectory is less than the UAV flight trajectory / the number of segments of the overall UAV flight trajectory; If the distance of the entire UAV flight trajectory segment is less than the UAV flight trajectory / the number of the entire UAV flight trajectory segments, the entire UAV flight trajectory segment is merged with the adjacent entire UAV flight trajectory segment to obtain multiple UAV flight avoidance trajectory segments.
5. The method for avoiding trajectory planning of an emergency failure of a UAV according to claim 3 is characterized in that: The step of encapsulating the multiple drone flight avoidance trajectory segments split from each drone flight trajectory with the drone flight obstacles in the corresponding drone flight trajectory scheme to obtain multiple drone flight obstacle avoidance data packets includes: Performing the UAV flight obstacle judgment on each of the multiple UAV flight avoidance trajectory segments split from the UAV flight trajectory; Marking the drone flight avoidance trajectory segment in which the drone flight obstacle rating appears and is higher than the drone flight obstacle average rating as a high-risk drone flight avoidance trajectory segment; Marking the drone flight avoidance trajectory segment in which the drone flight obstacle rating appears and is lower than the drone flight obstacle average rating as a low-risk drone flight avoidance trajectory segment; Calculating the ratio of the high-risk UAV flight avoidance trajectory segments to the low-risk UAV flight avoidance trajectory segments in each UAV flight trajectory to obtain the UAV flight avoidance trajectory risk ratio; Data is encapsulated on a plurality of the UAV flight avoidance trajectory risk ratios, the UAV flight avoidance trajectory segments and the corresponding UAV flight obstacles to obtain a plurality of UAV flight obstacle avoidance data packets.
6. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 5 is characterized in that: The step of rating emergency factors according to each of the UAV flight obstacle avoidance data packets in combination with the UAV flight environment includes: The rating is determined based on the risk ratio of the UAV flight avoidance trajectory in the UAV flight obstacle avoidance data packet and the UAV flight duration of the UAV flight avoidance trajectory segment; The risk ratio of each UAV flight trajectory is sorted in the order of high-risk UAV flight avoidance trajectory segment: low-risk UAV flight avoidance trajectory segment size from small to large, to obtain the safety ranking of the UAV flight trajectory; Sorting the flight time of each of the UAV flight avoidance trajectory segments in order from short to long, to obtain a UAV flight time ranking; According to the analysis of the UAV flight environment, the ranking of the UAV flight trajectory environmental coefficients is obtained; The UAV flight obstacle avoidance data packets corresponding to the UAV flight trajectory safety ranking, the UAV flight duration ranking and the UAV flight trajectory environmental coefficient ranking are ranked cumulatively in order. The lower the accumulated ranking, the higher the emergency factor rating of the UAV flight obstacle avoidance data packet, thereby obtaining an emergency factor rating.
7. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 6 is characterized in that: The step of analyzing the UAV flight environment to obtain the ranking of the UAV flight trajectory environmental coefficients includes: Collect the flying environment of the drone, and collect the flying environment of the drone according to the drone flight obstacle avoidance data package in three aspects: drone take-off, drone flight and drone landing; The degree of environmental influence on drone takeoff, drone flight and drone landing of different drone flight obstacle avoidance data packets is calculated according to the ratio of drone takeoff: drone flight: drone landing = 2:6:2, and the ranking of drone flight trajectory environmental coefficients is obtained.
8. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 6 is characterized in that: The step of formulating a drone crash trajectory planning plan according to the emergency factor rating to obtain multiple drone crash avoidance parameters includes: Simulating the drone crash point based on the emergency factor rating and the three-dimensional environmental coordinate system; Analyze the environmental coefficients of the drone falling at different locations according to the different drone flight obstacle avoidance data packets; Formulate a plan for the drone's falling trajectory based on the environmental coefficient; The collision coefficient of the drone is obtained according to the damage degree of the drone when it falls at different positions and collides with the environment; The recovery coefficient of the drone is obtained according to the difficulty of recovering the drone when it falls at different locations; According to the possibility of the drone experiencing secondary damage after falling, the secondary damage coefficient of the drone is obtained.
9. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 8, characterized in that: The environmental factors include: The fall coefficient of the drone caused by sudden collisions at different locations; The sudden environmental coefficient of the drone that affects the flight process of the drone due to environmental changes encountered during the flight of the drone; The weather impact coefficient of the UAV being affected by weather changes during flight; The signal interference coefficient of the UAV being interfered by electrical signals during flight.
10. The method for avoiding trajectory planning of emergency failure of unmanned aerial vehicle according to claim 8, characterized in that: The step of obtaining the optimal plan for the drone crash according to the drone crash risk avoidance parameters includes: According to the UAV collision coefficient, the UAV recovery coefficient and the UAV secondary damage coefficient, the UAV flight obstacle avoidance data packets are screened and sorted to obtain the UAV fall risk avoidance parameter sorting; The drone flight trajectory is optimized according to the ranking of the drone fall risk avoidance parameters, and the drone flight avoidance trajectory segment in which the drone collision coefficient, the drone recovery coefficient and the drone secondary damage coefficient are higher than the average drone fall risk avoidance parameters in the drone flight trajectory is optimized to obtain the optimal drone fall plan.