Unmanned aerial vehicle path planning and energy consumption analysis method and system

Abstract

The present application relates to the technical field of unmanned aerial vehicle low-altitude operation and energy management, and particularly relates to a method and system for unmanned aerial vehicle path planning and energy consumption analysis. The method comprises the following steps: step 1: based on historical spatial weather index and satellite positioning observation data, a mapping relationship between different spatial weather intensity and horizontal navigation error is established; step 2: taking a three-dimensional urban scene model as a framework, multiple flight altitudes and horizontal navigation errors based on the mapping relationship are combined to form multiple simulation scenarios; step 3: for each simulation scenario, a feasible flight path from a starting point to an ending point is generated, and the total energy consumption of the path is calculated respectively; and step 4: flight distances and total energy consumption under different simulation scenarios are output. The method can quantitatively evaluate the influence degree of the navigation performance decline caused by spatial weather on the path planning and energy consumption of unmanned aerial vehicles, and provide data support and decision basis for the track optimization and operation management of urban low-altitude unmanned aerial vehicles.

Description

Technical Field

[0001] This invention relates to the field of low-altitude operation and energy management technology for unmanned aerial vehicles (UAVs), specifically to a method and system for UAV path planning and energy consumption analysis. Background Technology

[0002] With the widespread application of drones in urban low-altitude environments, their operational efficiency and energy management have become critical issues. Drones primarily rely on global navigation satellite systems for horizontal positioning, but space weather events (such as geomagnetic storms) can cause ionospheric disturbances, leading to group delays and phase flicker in satellite signals, significantly increasing horizontal positioning errors. Under strong disturbance conditions, horizontal errors can increase from meters to tens of meters, severely impacting the drone's path tracking accuracy and obstacle avoidance capabilities.

[0003] Currently, research on UAV path planning largely focuses on static environments or conventional navigation conditions, lacking a systematic analysis of navigation performance degradation caused by space weather. Existing energy consumption models also often ignore the impact of path deviation and increased flight distance caused by increased navigation errors on overall energy consumption. Therefore, accurately assessing the impact of navigation errors on UAV path planning and energy consumption under the influence of space weather has become an important issue for improving the resilience of urban low-altitude UAV operations. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for UAV path planning and energy consumption analysis, which can quantitatively evaluate the impact of navigation errors on UAV flight paths and energy consumption under different space weather conditions, and provide a basis for UAV path decision-making and energy management in complex urban environments.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a method for UAV path planning and energy consumption analysis, comprising the following steps: Step 1: Based on historical space weather indices and satellite positioning observation data, establish the mapping relationship between different space weather intensities and horizontal navigation errors; Step 2: Using a 3D city scene model as a framework, combine various flight altitudes with horizontal navigation errors based on the mapping relationship to form multiple simulation scenarios; Step 3: For each simulation scenario, generate a feasible flight path from the starting point to the destination, and calculate the total energy consumption of the path respectively; Step 4: Output the flight distance and total energy consumption under different simulation scenarios.

[0006] Accordingly, a second aspect of this application provides a UAV path planning and energy consumption analysis system, comprising: The space weather-navigation error mapping module is used to establish the mapping relationship between different space weather intensities and horizontal navigation errors based on historical space weather indices and satellite positioning observation data; The multi-scenario simulation environment construction module is used to construct a three-dimensional urban scene model containing building obstacles using urban GIS data, and to set various combinations of flight altitude and horizontal navigation error levels to form a set of simulation scenarios; The path planning and energy consumption calculation module is used to generate feasible flight paths that take into account the navigation error buffer for each simulation scenario using a path planning algorithm, and calculate the energy consumption during the ascent, level flight and descent phases based on the UAV dynamics and power model to obtain the total energy consumption. The visualization output module is used to visualize and output flight path, flight distance, and total energy consumption data under different simulation scenarios.

[0007] Compared with the prior art, the present invention has the following beneficial effects: 1. The UAV path planning and energy consumption analysis method of the present invention systematically introduces navigation errors caused by space weather into UAV path planning and energy consumption analysis, which can quantitatively evaluate the impact of navigation errors on flight path and energy consumption under different space weather conditions, and significantly improve the reliability of path schemes and the accuracy of energy consumption prediction.

[0008] 2. The systematic analysis framework established in this invention organically combines navigation error, 3D urban scenes, path planning algorithms, and energy consumption models, realizing full-process simulation analysis from navigation error input to path and energy consumption output. This framework has broad applicability and scalability, providing crucial technical support and data analysis foundation for trajectory optimization, mission planning, and airspace management of urban low-altitude UAVs. Attached Figure Description

[0009] Figure 1 This is a flowchart of the UAV path planning and energy consumption analysis method of the present invention.

[0010] Figure 2 The horizontal positioning error of three continuously operating reference stations (CORS) during the Halloween storm in 2003, according to an embodiment of the present invention.

[0011] Figure 3 This is a design diagram of the research area and simulation scene in an embodiment of the present invention.

[0012] Figure 4 This is a schematic diagram of the flight path and buffer zone design according to an embodiment of the present invention.

[0013] Figure 5 This is a schematic diagram showing the flight distance results of the UAV under different navigation errors and flight altitude levels in an embodiment of the present invention.

[0014] Figure 6 This is a schematic diagram showing the energy consumption results of UAV operation under different navigation errors and flight altitudes in an embodiment of the present invention. Detailed Implementation

[0015] The present invention is described below based on embodiments, but the invention is not limited to these embodiments. In the following detailed description of the invention, certain specific details are described in detail to facilitate understanding by those skilled in the art.

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0017] See Figure 1 In one embodiment, the present invention proposes a method for UAV path planning and energy consumption analysis, comprising the following steps: Step 1: Based on historical space weather indices and satellite positioning observation data, establish the mapping relationship between different space weather intensities and horizontal navigation errors; Step 2: Using a 3D city scene model as a framework, combine various flight altitudes with horizontal navigation errors based on the mapping relationship to form multiple simulation scenarios; Step 3: For each simulation scenario, generate a feasible flight path from the starting point to the destination, and calculate the total energy consumption of the path respectively; Step 4: Output the flight distance and total energy consumption under different simulation scenarios.

[0018] In step 1 of one embodiment, for example, Figure 2 This paper illustrates the mapping relationship between different space weather intensities and horizontal navigation errors during the 2003 Halloween storm. Figure a shows the variation of the Dst index, and figures b, c, and d show the horizontal positioning errors observed at three continuously operating reference stations (CORS) in the United States: ALBH, MHCB, and TXAU. The results show that under normal circumstances, the satellite horizontal positioning error is within 5 meters, while during periods of low Dst index, the horizontal positioning error can reach as high as 35 meters. Based on these observations, a quantitative basis can be provided for setting the horizontal navigation error in subsequent step 2.

[0019] Furthermore, step 2 specifically includes the following steps: Step 21: Based on urban GIS data and building height information, construct a 3D urban scene model that includes building obstacles; Step 22: Using fishing net segmentation technology, the study area is divided into a three-dimensional grid to structurally represent the available low-altitude airspace of the city; Step 23: In the three-dimensional scene model, set multiple flight altitude layers, and combine each altitude layer with different horizontal navigation error levels corresponding to different space weather conditions in Step 1 to construct a set of simulation scenarios for path planning and energy consumption analysis.

[0020] In one embodiment, the different horizontal navigation error levels corresponding to different space weather conditions include: multiple horizontal navigation error levels set by the system based on historical observation data, characterizing space weather disturbances from normal, slight, moderate to severe to extreme.

[0021] In one embodiment, the Baijiahu area of ​​Jiangning District, Nanjing City, was selected as the case study area. The upper limit of the scene modeling height was set to 120 meters, the height layer interval was set to 10 meters, and different buffer zones were set for buildings according to different levels of navigation error to ensure the safety of UAV operation. Figure 3 Images (a), (b), and (c) respectively demonstrate the 3D modeling rendering of the study area, the distribution of building heights, and sixteen simulated scenarios composed of four flight altitudes (30, 60, 90, and 120 meters) and four horizontal navigation error levels (5, 10, 30, and 50 meters). The four horizontal navigation error levels (5, 10, 30, and 50 meters) are based on historical observation data. Figure 2 The system is designed to characterize space weather disturbances ranging from normal, minor, moderate to severe to extreme. The selection of the four flight altitudes (30, 60, 90, and 120 meters) follows the definition of low-altitude uncontrolled airspace in my country's "Interim Regulations on the Management of Unmanned Aircraft Flights," which sets an upper limit of 120 meters. Within this regulatory framework, four altitude layers are set at equal intervals of 30 meters to systematically cover typical operating airspace from near the ground to the upper limit of the regulations.

[0022] Furthermore, in one embodiment, the drone model selected in step 3 is a DJI Matrice 210 RTK V2. Step 3 specifically includes the following steps: Step 31: For each simulation scenario, the A* path planning algorithm (also known as the A-star algorithm) is used to generate feasible flight paths. The algorithm searches based on the grid nodes generated by the 3D fishing net segmentation. The evaluation function of the A* algorithm is: in, From the starting node to the current node The cumulative actual cost is calculated using the following formula: in, Represents discrete three-dimensional positions on the path. Indicates from node to adjacent nodes The transfer cost is calculated using the three-dimensional Euclidean distance: From the current node n to the target node The heuristic cost estimation uses straight-line distance calculation: During planning, corresponding buffer zones need to be set for buildings based on the horizontal navigation error R in the current simulation scenario to ensure that the path meets the safety interval. The obstacle buffer zone is constructed by expanding the outer contour of the building in the horizontal direction by a factor of R according to the navigation error R, forming an expanded obstacle boundary. Figure 4 The diagram illustrates the design of the buffer zone and the effect of path planning in this embodiment.

[0023] Step 32: Energy Consumption During the Ascent Phase The calculation formula is: in, The instantaneous power rise is expressed as: Its calculation is based on benchmark hovering power. The calculation formula is: in, Indicates the altitude. Indicates the rate of ascent. This represents the blade profile power related to rotor resistance. This represents the induced power required for the drone to generate thrust while hovering. It is the section drag coefficient; It is the rotor solidity; It refers to the number of rotor blades; It is air density; It is the rotor disk area; This is the weight of the drone (unit: kg). It is the thrust coefficient; It is the incremental correction factor for induced power. This represents the equivalent area of ​​the fuselage flat panel in a vertical orientation.

[0024] Step 33: Energy Consumption During Level Flight The calculation formula is: in, The instantaneous horizontal flight power is expressed as follows: in, Indicates horizontal flight distance. Indicates horizontal flight speed. This represents the average rotor-induced velocity of the drone while it is hovering. This represents the equivalent flat panel area of ​​the fuselage in a horizontal position.

[0025] Step 34: Energy consumption during the descent phase The calculation formula is: in, The instantaneous power decrease is expressed as follows: Step 35: Total Energy Consumption The sum of energy consumption for the three phases of ascent, horizontal flight, and descent is calculated using the following formula: .

[0026] Furthermore, in one embodiment, step 4 specifically includes the following steps: Step 41: Summarize the flight distance and total energy consumption data from all simulation scenarios to create a dataset that includes flight altitude, navigation error level, flight distance, and total energy consumption; Step 42: Based on the dataset, output the UAV three-dimensional path planning map under different combinations of flight altitude layers and different satellite navigation error levels, and determine the path with the lowest energy consumption and the path with the shortest flight distance.

[0027] In one embodiment, Figure 5 (a), (b), (c), and (d) show the flight paths at four flight altitudes (30, 60, 90, and 120 meters) under different satellite navigation errors (5, 10, 30, and 50 meters) within the study area, respectively. Figure 5 Figure (e) shows the specific values ​​of flight distances for all simulation scenarios, achieving a three-dimensional visualization of flight paths under different scenarios. The "×" label below S13 indicates that no suitable flight path exists when the flight altitude is set to 30 meters and the satellite navigation error is 50 meters. Under all four positioning error conditions, the shortest path is generated at a flight altitude of 120 meters. This provides a clear flight strategy for scenarios prioritizing mission efficiency and can provide a direct basis for rapid subsequent UAV trajectory planning. For example, in UAV delivery missions, if the navigation error caused by current weather conditions is within acceptable limits, the system can prioritize flight at a 120-meter altitude to maximize mission execution efficiency.

[0028] In one embodiment, Figure 6 The study presents the specific values ​​for ascent energy consumption, cruise energy consumption, descent energy consumption, and total energy consumption under 16 scenarios within the research area. Under four different positioning error conditions, the lowest energy-consuming paths occurred at altitudes of 30 meters, 60 meters, and 90 meters, respectively. This energy consumption distribution pattern provides crucial support for subsequent adaptive altitude decision-making for UAVs aimed at energy efficiency optimization. For example, a real-time lookup table can be constructed based on this data: when the system detects a current navigation error of 30 meters, it can automatically switch the flight altitude to 60 meters, thereby achieving globally optimal flight energy consumption.

[0029] In one embodiment of this application, a UAV path planning and energy consumption analysis system is also provided, comprising: The space weather-navigation error mapping module is used to establish the mapping relationship between different space weather intensities and horizontal navigation errors based on historical space weather indices and satellite positioning observation data. The multi-scenario simulation environment construction module is used to construct a three-dimensional urban scene model containing building obstacles using urban GIS data, and to set various combinations of flight altitude and horizontal navigation error levels to form a set of simulation scenarios; The path planning and energy consumption calculation module is used to generate feasible flight paths that take into account the navigation error buffer for each simulation scenario using a path planning algorithm, and calculate the energy consumption during the ascent, level flight and descent phases based on the UAV dynamics and power model to obtain the total energy consumption. The visualization output module is used to visualize and output flight path, flight distance, and total energy consumption data under different simulation scenarios.

[0030] In this embodiment, the working methods of each module of the UAV path planning and energy consumption analysis system can be implemented using the UAV path planning and energy consumption analysis method described above, and will not be repeated here.

[0031] This invention has many specific applications, and the above description is only a preferred embodiment of this invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of this invention, and these improvements should also be considered within the scope of protection of this invention.

Claims

1. A method for UAV path planning and energy consumption analysis, characterized in that, Includes the following steps: Step 1: Based on historical space weather indices and satellite positioning observation data, establish the mapping relationship between different space weather intensities and horizontal navigation errors; Step 2: Using a 3D city scene model as a framework, combine various flight altitudes with horizontal navigation errors based on the mapping relationship to form multiple simulation scenarios; Step 3: For each simulation scenario, generate a feasible flight path from the starting point to the destination, and calculate the total energy consumption of the path respectively; Step 4: Output the flight distance and total energy consumption under different simulation scenarios; Step 2 includes: Step 21: Based on urban GIS data and building height information, construct a 3D urban scene model that includes building obstacles; Step 22: Using fishing net segmentation technology, the study area is divided into a three-dimensional grid to structurally represent the available low-altitude airspace of the city; Step 23: In the three-dimensional city scene model, multiple flight altitude layers are set, and each altitude layer is combined with the horizontal navigation error corresponding to different space weather intensities in Step 1 to construct a set of simulation scenarios for path planning and energy consumption analysis; In Step 23, the different horizontal navigation error levels corresponding to different space weather conditions include: multiple horizontal navigation error levels set by the system to characterize space weather disturbances from normal, slight, moderate to strong to extreme. Step 3 includes: Step 31: For each simulation scenario, the A* path planning algorithm is used and an obstacle buffer is set according to the navigation error to generate a feasible flight path. The algorithm searches based on the grid nodes generated by the 3D fishing net segmentation. Step 32: Calculate the energy consumption during the ascent phase ; Step 33: Calculate energy consumption during the level flight phase ; Step 34: Calculate the energy consumption during the descent phase. ; Step 35: Calculate total energy consumption The calculation formula is: .

2. The UAV path planning and energy consumption analysis method according to claim 1, characterized in that, In step 31, the evaluation function of the A* algorithm is: in, From the starting node to the current node The cumulative actual cost is calculated using the following formula: in, Represents discrete three-dimensional positions on the path. Indicates from node to adjacent nodes The transfer cost is calculated using the three-dimensional Euclidean distance: From the current node n to the target node The heuristic cost estimation uses straight-line distance calculation: 。 3. The UAV path planning and energy consumption analysis method according to claim 2, characterized in that, In step 32, the energy consumption during the ascent phase The calculation formula is: in, The instantaneous rise power is expressed as: Its calculation is based on benchmark hovering power. The calculation formula is: in, Indicates the altitude. Indicates the rate of ascent. This represents the blade profile power related to rotor resistance. This represents the induced power required for the drone to generate thrust while hovering. It is the section drag coefficient. It is the rotor solidity. It refers to the number of rotor blades. It is air density. It is the rotor disk area. It's the weight of the drone. It is the thrust coefficient. It is the incremental correction factor for induced power. This represents the equivalent area of ​​the fuselage flat panel in a vertical orientation.

4. The UAV path planning and energy consumption analysis method according to claim 3, characterized in that, Step 33: Energy consumption during the horizontal flight phase The calculation formula is: in, The instantaneous horizontal flight power is expressed as follows: in, Indicates horizontal flight distance. Indicates horizontal flight speed. This represents the average rotor-induced velocity of the drone while it is hovering. This represents the equivalent flat panel area of ​​the fuselage in a horizontal position.

5. The UAV path planning and energy consumption analysis method according to claim 4, characterized in that, Step 34: Energy consumption during the descent phase The calculation formula is: in, The instantaneous power decrease is expressed as follows: 。 6. The method for UAV path planning and energy consumption analysis according to claim 1, characterized in that, Step 4 includes: Step 41: Summarize the flight distance and total energy consumption data from all simulation scenarios to create a dataset that includes flight altitude, navigation error level, flight distance, and total energy consumption; Step 42: Based on the dataset, output the UAV three-dimensional path planning map under different combinations of flight altitude layers and different satellite navigation error levels, and determine the path with the lowest energy consumption and the path with the shortest flight distance.

7. A UAV path planning and energy consumption analysis system employing the UAV path planning and energy consumption analysis method as described in any one of claims 1-6, characterized in that, include: The space weather-navigation error mapping module is used to establish the mapping relationship between different space weather intensities and horizontal navigation errors based on historical space weather indices and satellite positioning observation data. The multi-scenario simulation environment construction module is used to construct a three-dimensional urban scene model containing building obstacles using urban GIS data, and to set various combinations of flight altitude and horizontal navigation error levels to form a set of simulation scenarios; The path planning and energy consumption calculation module is used to generate feasible flight paths that take into account the navigation error buffer for each simulation scenario using a path planning algorithm, and calculate the energy consumption during the ascent, level flight and descent phases based on the UAV dynamics and power model to obtain the total energy consumption. The visualization output module is used to visualize and output flight path, flight distance, and total energy consumption data under different simulation scenarios.

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