CN118838371A - Intelligent automatic flight path planning method - Google Patents

Abstract

The invention discloses an intelligent automatic flight path planning method, comprising the following steps: S1, mission definition and environmental analysis: collecting and analyzing the geographical information, meteorological data, obstacle distribution and flight restrictions of the flight area; S2, environmental modeling; S3, starting and ending point and waypoint setting; S4, path planning algorithm selection and implementation: selecting a suitable path planning algorithm according to the mission requirements and environmental complexity; running the algorithm to generate a preliminary flight path from the starting point to the end point; S5, path optimization: optimizing the preliminary path, including obstacle avoidance adjustment, optimization of flight altitude and speed, and minimization of energy consumption; S6, flight simulation and verification; S7, path execution and monitoring; S8, data analysis and feedback. The invention analyzes the geographical information and meteorological information, performs obstacle avoidance adjustment well, and adjusts the flight posture to reduce the energy loss of the flight.

Description

An intelligent automatic flight path planning method

Technical Field

The invention relates to the technical field of flight path planning, and in particular to an intelligent automatic flight path planning method.

Background Art

Obstacles that hinder traffic are obstacles that cannot be passed directly, including natural obstacles such as cliffs and mountains; man-made obstacles such as buildings and road controls. The only way to pass this type of obstacle is to detour, which will also increase the path cost.

The existing publication number CN115793710A discloses a flight path planning system for an aircraft, comprising: a geographic information collection module for extracting geographic information within a flyable range from a geographic information system; a meteorological information collection module for extracting meteorological information within a flyable range from a meteorological information system; a multi-layer map generation module for generating a multi-layer planning map based on the geographic information and meteorological information within the flyable range; each layer of map information includes the traffic type and traffic cost multiplier of each traffic node in the layer of map; a path planning module for calculating the optimal path from the starting point to the target point based on the multi-layer planning map according to the positions of the flight starting point and the target point, using an improved path planning algorithm to obtain a planned flight path for the aircraft; a storage unit for storing the planned flight path of the aircraft; and a display unit for displaying the flight path of the aircraft from the starting point to the target point.

Although the existing flight path planning system analyzes geographic information and meteorological information, it cannot make good obstacle avoidance adjustments, nor can it adjust the flight attitude to reduce the energy loss of flight. For this reason, we propose an intelligent automatic flight path planning method.

Summary of the invention

The purpose of the present invention is to provide an intelligent automatic flight path planning method to solve the problems in the prior art.

To achieve the above object, the present invention provides the following technical solution: an intelligent automatic flight path planning method, comprising the following steps:

S1. Mission definition and environmental analysis: clarify the objectives of the flight mission; collect and analyze the geographical information, meteorological data, obstacle distribution and flight restrictions of the flight area;

S2, Environmental Modeling: Abstract the actual environment into a mathematical model suitable for algorithm processing; use digital elevation model data to construct a three-dimensional representation of the terrain;

S3. Start, end and waypoint setting: mark the take-off point, target point and any necessary intermediate waypoints; enter the location information of these points in the flight control software;

S4. Path planning algorithm selection and implementation: Select an appropriate path planning algorithm based on mission requirements and environmental complexity; run the algorithm to generate a preliminary flight path from the start point to the end point;

S5, Path Optimization: Optimize the preliminary path, including obstacle avoidance adjustment, optimization of flight altitude and speed, and minimization of energy consumption;

S6, Flight simulation and verification: simulate flight in a virtual environment, check the feasibility of the path, ensure flight safety and avoid collisions;

S7, Path Execution and Monitoring: Upload the planned path to the UAV control system and start the flight mission; monitor the flight status in real time and adjust the flight path or speed based on the feedback;

S8, Data Analysis and Feedback: After the mission is completed, collect flight data and analyze the efficiency and safety of path execution; based on the data analysis results, iteratively optimize the planning algorithm.

Preferably, the collection and analysis of geographic information in S1 comprises the following steps:

Obtain basic map data and basic geographic information on the region’s topography, landforms, water systems, and roads;

Digital processing, converting map information into digital formats, such as GIS (Geographic Information System) data, for subsequent analysis;

Analyze terrain features, identify mountains, rivers, urban buildings, etc., and assess their impact on the flight path, such as the need to detour or adjust the flight altitude.

Preferably, the collection and analysis of meteorological data in S1 comprises the following steps:

Real-time and forecast data, obtain current and forecasted weather conditions through meteorological services (such as NOAA, weather satellite data, local weather stations), including wind speed and direction, temperature, humidity, visibility, precipitation probability, cloud base height, etc.;

Analyze airport weather reports (METAR) and forecasts (TAF) to understand weather conditions at takeoff and landing points;

Special meteorological events: Pay special attention to warnings of extreme weather such as thunderstorms, typhoons, tornadoes, etc., and plan corresponding avoidance measures.

Preferably, the obstacle distribution survey in S1 comprises the following steps:

Use database query to consult existing obstacle databases, such as digital elevation models (DEMs) and aviation obstacle databases, to obtain location and height information of known buildings, towers, mountains, etc.;

On-site inspection: conduct field inspections when necessary, especially for databases that are not updated in a timely manner, to confirm new obstacles;

Build a 3D model and use LiDAR data or drone mapping to construct an accurate 3D obstacle distribution model.

Preferably, the path planning algorithm in S4 is selected to abstract the flight space into a graph structure, where nodes represent key points and edges represent flyable path segments, and then use a graph search algorithm to find the shortest or lowest cost path.

Preferably, the obstacle avoidance adjustment in S5 includes the following steps:

Environmental perception and modeling: Use radar, LiDAR, cameras or other sensors to monitor the surrounding environment in real time, build three-dimensional maps, and identify and track obstacles;

Obstacle avoidance path planning: Based on the current flight status and obstacle information, a path planning algorithm (such as A*, RRT*, or potential field-based methods) is used to calculate a collision-free safe path;

Optimal trajectory generation: Considering dynamic constraints and efficiency, a smooth, continuous obstacle avoidance trajectory that meets the maneuverability of the aircraft is generated;

Execution and feedback: Control the aircraft to maneuver according to the planned trajectory, while continuously monitoring environmental changes and adjusting the path in real time when necessary.

Preferably, the optimization of the flight height and speed in S5 comprises the following steps:

Target setting: Set target altitude and speed according to flight mission (such as cruise, climb, descent or landing);

Performance analysis: Analyze the impact of current flight conditions (such as altitude, temperature, weight) on energy consumption and performance, and determine the most economical or efficient flight altitude and speed;

Altitude and speed adjustment: Achieve smooth transition of altitude and speed by adjusting engine thrust and flight attitude (such as angle of attack and yaw angle);

Real-time monitoring and adjustment: Use flight sensors (such as barometric altimeter and airspeed indicator) to continuously monitor actual altitude and speed, and make necessary fine-tuning after comparing with target values.

Preferably, the minimization of energy consumption in S5 comprises the following steps:

Efficiency analysis: Analyze fuel consumption rates at different flight altitudes, speeds, and weights using flight models and historical data;

Optimization strategy formulation: Based on the results of efficiency analysis, flight strategies are formulated, such as selecting the best flight altitude (usually higher altitudes reduce resistance due to thin air), maintaining economic cruising speed, and reasonably arranging the climb and descent phases;

Dynamic Energy Management: During flight, engine output is dynamically adjusted based on real-time flight conditions to avoid unnecessary energy waste;

Flight attitude optimization: maintain a good aerodynamic shape and reduce additional resistance, such as maintaining a horizontal and stable flight as much as possible during cruising;

System integration: Integrate obstacle avoidance, altitude and speed adjustment, and energy management functions into the flight control system to ensure that all subsystems work in coordination to maximize overall energy efficiency.

Compared with the prior art, the present invention has the following beneficial effects:

1. The geographical information and meteorological information are analyzed to make good obstacle avoidance adjustments and adjust the flight attitude to reduce the energy loss of flight.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

FIG. 1 is a flow chart of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

Referring to FIG. 1 , an intelligent automatic flight path planning method according to an embodiment of the present invention includes the following steps:

S1. Mission definition and environmental analysis: clarify the objectives of the flight mission; collect and analyze the geographical information, meteorological data, obstacle distribution and flight restrictions of the flight area;

S2, Environmental Modeling: Abstract the actual environment into a mathematical model suitable for algorithm processing; use digital elevation model data to construct a three-dimensional representation of the terrain;

S3. Start, end and waypoint setting: mark the take-off point, target point and any necessary intermediate waypoints; enter the location information of these points in the flight control software;

S4. Path planning algorithm selection and implementation: Select an appropriate path planning algorithm based on mission requirements and environmental complexity; run the algorithm to generate a preliminary flight path from the start point to the end point;

S5, Path Optimization: Optimize the preliminary path, including obstacle avoidance adjustment, optimization of flight altitude and speed, and minimization of energy consumption;

S6, Flight simulation and verification: simulate flight in a virtual environment, check the feasibility of the path, ensure flight safety and avoid collisions;

S7, Path Execution and Monitoring: Upload the planned path to the UAV control system and start the flight mission; monitor the flight status in real time and adjust the flight path or speed based on the feedback;

S8, Data Analysis and Feedback: After the mission is completed, collect flight data and analyze the efficiency and safety of path execution; based on the data analysis results, iteratively optimize the planning algorithm.

Preferably, the collection and analysis of geographic information in S1 comprises the following steps:

Obtain basic map data and basic geographic information on the region’s topography, landforms, water systems, and roads;

Digital processing, converting map information into digital formats, such as GIS (Geographic Information System) data, for subsequent analysis;

Analyze terrain features, identify mountains, rivers, urban buildings, etc., and assess their impact on the flight path, such as the need to detour or adjust the flight altitude.

Preferably, the collection and analysis of meteorological data in S1 comprises the following steps:

Real-time and forecast data, obtain current and forecasted weather conditions through meteorological services (such as NOAA, weather satellite data, local weather stations), including wind speed and direction, temperature, humidity, visibility, precipitation probability, cloud base height, etc.;

Analyze airport weather reports (METAR) and forecasts (TAF) to understand weather conditions at takeoff and landing points;

Special meteorological events: Pay special attention to warnings of extreme weather such as thunderstorms, typhoons, tornadoes, etc., and plan corresponding avoidance measures.

Preferably, the obstacle distribution survey in S1 comprises the following steps:

Use database query to consult existing obstacle databases, such as digital elevation models (DEMs) and aviation obstacle databases, to obtain location and height information of known buildings, towers, mountains, etc.;

On-site inspection: conduct field inspections when necessary, especially for databases that are not updated in a timely manner, to confirm new obstacles;

Build a 3D model and use LiDAR data or drone mapping to construct an accurate 3D obstacle distribution model.

Preferably, the path planning algorithm in S4 is selected to abstract the flight space into a graph structure, where nodes represent key points and edges represent flyable path segments, and then use a graph search algorithm to find the shortest or lowest cost path.

Preferably, the obstacle avoidance adjustment in S5 includes the following steps:

Environmental perception and modeling: Use radar, LiDAR, cameras or other sensors to monitor the surrounding environment in real time, build three-dimensional maps, and identify and track obstacles;

Obstacle avoidance path planning: Based on the current flight status and obstacle information, a path planning algorithm (such as A*, RRT*, or potential field-based methods) is used to calculate a collision-free safe path;

Optimal trajectory generation: Considering dynamic constraints and efficiency, a smooth, continuous obstacle avoidance trajectory that meets the maneuverability of the aircraft is generated;

Execution and feedback: Control the aircraft to maneuver according to the planned trajectory, while continuously monitoring environmental changes and adjusting the path in real time when necessary.

Preferably, the optimization of the flight height and speed in S5 comprises the following steps:

Target setting: Set target altitude and speed according to flight mission (such as cruise, climb, descent or landing);

Performance analysis: Analyze the impact of current flight conditions (such as altitude, temperature, weight) on energy consumption and performance, and determine the most economical or efficient flight altitude and speed;

Altitude and speed adjustment: Achieve smooth transition of altitude and speed by adjusting engine thrust and flight attitude (such as angle of attack and yaw angle);

Real-time monitoring and adjustment: Use flight sensors (such as barometric altimeter and airspeed indicator) to continuously monitor actual altitude and speed, and make necessary fine-tuning after comparing with target values.

Preferably, the minimization of energy consumption in S5 comprises the following steps:

Efficiency analysis: Analyze fuel consumption rates at different flight altitudes, speeds, and weights using flight models and historical data;

Optimization strategy formulation: Based on the results of efficiency analysis, flight strategies are formulated, such as selecting the best flight altitude (usually higher altitudes reduce resistance due to thin air), maintaining economic cruising speed, and reasonably arranging the climb and descent phases;

Dynamic Energy Management: During flight, engine output is dynamically adjusted based on real-time flight conditions to avoid unnecessary energy waste;

Flight attitude optimization: maintain a good aerodynamic shape and reduce additional resistance, such as maintaining a horizontal and stable flight as much as possible during cruising;

System integration: Integrate obstacle avoidance, altitude and speed adjustment, and energy management functions into the flight control system to ensure that all subsystems work in coordination to maximize overall energy efficiency.

Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (8)

1. An intelligent automatic flight path planning method, characterized in that it comprises the following steps: S1. Mission definition and environmental analysis: clarify the objectives of the flight mission; collect and analyze the geographical information, meteorological data, obstacle distribution and flight restrictions of the flight area; S2, Environmental Modeling: Abstract the actual environment into a mathematical model suitable for algorithm processing; use digital elevation model data to construct a three-dimensional representation of the terrain; S3. Start, end and waypoint setting: mark the take-off point, target point and any necessary intermediate waypoints; enter the location information of these points in the flight control software; S4. Path planning algorithm selection and implementation: Select an appropriate path planning algorithm based on mission requirements and environmental complexity; run the algorithm to generate a preliminary flight path from the start point to the end point; S5, Path Optimization: Optimize the preliminary path, including obstacle avoidance adjustment, optimization of flight altitude and speed, and minimization of energy consumption; S6, Flight simulation and verification: simulate flight in a virtual environment, check the feasibility of the path, ensure flight safety and avoid collisions; S7, Path Execution and Monitoring: Upload the planned path to the UAV control system and start the flight mission; monitor the flight status in real time and adjust the flight path or speed based on the feedback; S8, Data Analysis and Feedback: After the mission is completed, collect flight data and analyze the efficiency and safety of path execution; based on the data analysis results, iteratively optimize the planning algorithm. 2. The intelligent automatic flight path planning method according to claim 1, characterized in that the collection and analysis of geographic information in S1 comprises the following steps: Obtain basic map data and basic geographic information on the region’s topography, landforms, water systems, and roads; Digital processing, converting map information into digital format; Analyze terrain features, identify mountains, rivers, and urban buildings, evaluate their impact on the flight path, and determine whether it is necessary to detour or adjust the flight altitude. 3. The intelligent automatic flight path planning method according to claim 1, characterized in that the collection and analysis of meteorological data in S1 comprises the following steps: Real-time and forecast data, obtain current and forecasted weather conditions through weather services, including wind speed and direction, temperature, humidity, visibility, precipitation probability, and cloud base height; Analyze airport weather reports and forecasts to understand weather conditions at take-off and landing points; Special meteorological events: extreme weather warnings for thunderstorms, typhoons, and tornadoes, and planning of corresponding avoidance measures. 4. The intelligent automatic flight path planning method according to claim 1, characterized in that the obstacle distribution survey in S1 comprises the following steps: Use database query to consult the existing obstacle database, including digital elevation model and aviation obstacle database, to obtain the location and height information of known buildings, towers and mountains; On-site inspection: conduct field inspections, especially for databases that are not updated in a timely manner, to confirm new obstacles; Build a 3D model and use LiDAR data or drone mapping to construct an accurate 3D obstacle distribution model. 5. An intelligent automatic flight path planning method according to claim 1, characterized in that the path planning algorithm selected in S4 is to abstract the flight space into a graph structure, where nodes represent key points and edges represent flyable path segments, and then use a graph search algorithm to find the shortest or lowest cost path. 6. The intelligent automatic flight path planning method according to claim 1, characterized in that the obstacle avoidance adjustment in S5 comprises the following steps: Environmental perception and modeling: Use radar, LiDAR, cameras or sensors to monitor the surrounding environment in real time, build three-dimensional maps, and identify and track obstacles; Obstacle avoidance path planning: Based on the current flight status and obstacle information, a path planning algorithm is used to calculate a safe path without collision; Optimal trajectory generation: Considering dynamic constraints and efficiency, a smooth, continuous obstacle avoidance trajectory that meets the maneuverability of the aircraft is generated; Execution and feedback: Control the aircraft to maneuver according to the planned trajectory, while continuously monitoring environmental changes and adjusting the path in real time when necessary. 7. The intelligent automatic flight path planning method according to claim 1, characterized in that the optimization of the flight altitude and speed in S5 comprises the following steps: Target setting: Set the target altitude and speed according to the flight mission; Performance analysis: Analyze the impact of current flight conditions on energy consumption and performance, and determine the most economical or efficient flight altitude and speed; Altitude and speed adjustment: Achieve smooth transition of altitude and speed by adjusting engine thrust and flight attitude; Real-time monitoring and adjustment: Use flight sensors to continuously monitor actual altitude and speed, and make necessary fine-tuning after comparing them with target values. 8. The intelligent automatic flight path planning method according to claim 1, wherein the energy consumption minimization in S5 comprises the following steps: Efficiency analysis: Analyze fuel consumption rates at different flight altitudes, speeds, and weights using flight models and historical data; Optimization strategy formulation: Based on the results of efficiency analysis, formulate flight strategies, select the best flight altitude, maintain economic cruising speed, and reasonably arrange the climb and descent phases; Dynamic Energy Management: During flight, engine output is dynamically adjusted based on real-time flight conditions to avoid unnecessary energy waste; Flight attitude optimization: maintain good aerodynamic shape and reduce additional resistance; System integration: Integrate obstacle avoidance, altitude and speed adjustment, and energy management functions into the flight control system to ensure that all subsystems work in coordination to maximize overall energy efficiency.