DEM map dynamic obstacle avoidance path planning method and system based on rime algorithm

Abstract

The invention discloses a DEM map dynamic obstacle avoidance path planning method and system based on a rime algorithm, DEM terrain modeling, rime algorithm meteorological prediction and an improved A * path planning algorithm are combined, safe path planning in a dynamic environment is achieved, the method and system can be applied to unmanned aerial vehicle inspection (such as power line icing monitoring), mountain rescue, automatic driving and other scenes, and the method and system have good application prospects. The problems that a traditional A * algorithm is limited, multi-constraint fusion is difficult, pure real-time meteorological data lags behind and the like are solved, and a safer and more reliable path planning navigation scheme is formed.

Description

Technical Field

[0001] This invention belongs to the fields of geographic information engineering, intelligent path planning, and meteorological data analysis. Specifically, it relates to a dynamic obstacle avoidance path planning method and system based on rime ice algorithm using DEM map, and particularly to a path planning method and system that integrates digital elevation model (DEM), real-time meteorological data prediction, and path search algorithm to achieve dynamic avoidance of meteorological obstacles. Background Technology

[0002] 1. Traditional route planning only considers static obstacles: Most A* or its variants plan routes based solely on static maps (such as terrain and buildings), failing to address dynamic threats such as weather and wind fields. 2. Insufficient consideration of meteorological factors: Existing solutions may simply treat areas with severe weather as fixed no-fly zones, lacking detailed prediction and modeling of the formation, evolution, and dissipation of weather systems (such as rime and cumulonimbus clouds). 3. Lack of a combination of foresight and real-time capabilities: Either relying solely on historical meteorological data results in rigid route planning, or depending solely on real-time data leads to delayed responses and an inability to avoid meteorological obstacles before they form. 4. Limited search space construction: Typically using only two-dimensional or simple three-dimensional spaces, failing to deeply integrate terrain elevation and meteorological data in three-dimensional space, resulting in planned routes that may be impractical in reality (e.g., too close to the ground or flying into clouds).

[0003] Existing path planning methods (such as A*, Dijkstra, etc.) mainly rely on static environment models, and their core shortcomings are: Lack of dynamic obstacle response: Path search is based solely on preset static maps (such as terrain elevation and fixed buildings), which cannot perceive or predict dynamic threats such as weather (such as sudden frost and strong winds) and temporary obstacles (such as construction areas) in real time, resulting in a disconnect between planning results and the actual environment.

[0004] Simplified handling of meteorological factors: Although some schemes incorporate meteorological data, they often adopt static "black and white" rules (such as marking areas with wind speeds >10m / s as no-fly zones), without considering the spatiotemporal evolution characteristics of meteorological phenomena (such as the dynamic process of rime formation and dissipation, and the movement trajectory of cumulonimbus clouds), resulting in frequent path interruptions or excessive conservatism.

[0005] Single-dimensional modeling limitations: Traditional 3D path planning often focuses on terrain elevation (such as ground clearance constraints in UAV obstacle avoidance), but fails to model meteorological parameters (such as temperature, humidity, and visibility) as dynamic attributes in 3D space. For example, different areas at the same altitude may have drastically different travel risks due to meteorological differences (such as low-altitude rime areas and high-altitude clear-sky areas), and existing models cannot distinguish such fine differences.

[0006] Reliance on purely historical data: Some schemes use historical meteorological statistics (such as the monthly probability of rime) to plan routes, resulting in rigid routes that cannot cope with short-term meteorological changes (such as sudden dense fog within 2 hours); Lagging of purely real-time data: When relying solely on real-time sensor data (such as airborne weather radar), due to data transmission delays and limited sensing range, obstacle avoidance may be triggered only after the obstacle zone has formed, missing the opportunity to avoid obstacles in advance (such as high-speed moving thunderstorm clouds).

[0007] Limitations of the traditional A* algorithm: The cost function of the standard A* algorithm is mostly based on distance or static threats (such as terrain slope), and does not take into account dynamic meteorological costs (such as the risk of equipment failure caused by rime ice, the electromagnetic interference level of cumulonimbus clouds). In complex meteorological environments, it is easy to get trapped in local optima (such as taking a detour to avoid temporary obstacles, which increases the overall energy consumption).

[0008] Challenges of multi-constraint integration: It is difficult to coordinate and optimize multiple constraints such as terrain (elevation, slope), weather (visibility, wind), and equipment performance (battery life, load). Existing solutions often have fixed weight allocation (such as always prioritizing the shortest distance), which leads to an imbalance between safety and efficiency in extreme scenarios (such as crossing low-visibility rime areas to shorten the distance).

[0009] The Rain-In-Snow Melting Effect (RIME) algorithm is a meteorological model primarily used to understand the process by which raindrops affect snow surfaces. In complex mountainous environments, this concept is applied to UAV path planning, mainly to simulate and predict the impact of weather conditions on flight safety. Summary of the Invention

[0010] The technical problem that the present invention aims to solve is that traditional path planning relies heavily on static terrain data and does not take into account dynamic meteorological obstacles such as fog and snow; the A* algorithm is prone to getting trapped in local optima in complex terrain and lacks real-time response to sudden meteorological changes.

[0011] To address the aforementioned technical problems, the present invention provides a dynamic obstacle avoidance path planning method for DEM maps based on a rime ice algorithm, comprising the following steps: Acquire DEM data of the target area, analyze and identify data voids, complete the surface and underground structures, generate a seamlessly integrated 3D terrain model, and rasterize the 3D terrain model into a 3D mesh as the basic search space for path planning. The system integrates real-time meteorological data and short-term forecast data, uses a rime ice algorithm to simulate the rime ice growth process, transforms meteorological parameters into obstacle zone determination indicators, and dynamically outputs the spatial range and time window of the obstacle zone. A cost function is designed by incorporating multi-objective constraints. The meteorological obstacle area data is updated periodically, triggering local replanning of the A* algorithm. The path is smoothed by Bézier curves to satisfy kinematic constraints and realize the construction of a dynamic obstacle avoidance mechanism, thus obtaining the dynamic obstacle avoidance path planning results of the DEM map.

[0012] Preferably, the DEM data of the target area includes elevation, slope, and aspect.

[0013] Preferably, each grid cell of the three-dimensional mesh includes terrain gradient and obstacle threat coefficient attributes.

[0014] Preferably, the real-time meteorological data includes temperature, humidity, wind speed, and visibility, and the short-term forecast data includes the probability of rime formation and duration.

[0015] Preferably, the obstacle zone determination criterion is that a visibility of <500 meters is marked as an obstacle zone.

[0016] Preferably, the formula for the fusion multi-objective constraint design cost function is as follows:

[0017] In the formula, λ is a weighting coefficient that is dynamically adjusted to balance path length and security.

[0018] The present invention also provides a dynamic obstacle avoidance path planning system for DEM maps based on a frost algorithm, employing the dynamic obstacle avoidance path planning method for DEM maps based on a frost algorithm as described above. The system includes: The 3D terrain search space construction module is used to acquire DEM data of the target area, analyze and identify data void areas, complete the surface and underground structures, generate a seamless 3D terrain model, and rasterize the 3D terrain model into a 3D mesh as the basic search space for path planning. The meteorological obstacle zone prediction and construction module is used to integrate real-time meteorological data and short-term forecast data, simulate the rime growth process using the rime algorithm, convert meteorological parameters into obstacle zone judgment indicators, and dynamically output the spatial range and time window of the obstacle zone. An improved path planning module for the 3D A* algorithm is used to design a cost function that incorporates multi-objective constraints, periodically update meteorological obstacle zone data, trigger local replanning of the A* algorithm, smooth the path using Bézier curves, satisfy kinematic constraints to construct a dynamic obstacle avoidance mechanism, and obtain dynamic obstacle avoidance path planning results for the DEM map.

[0019] This invention provides a dynamic obstacle avoidance path planning method and system for DEM maps based on the frost algorithm. It combines DEM terrain modeling, frost algorithm weather prediction, and an improved A* path planning algorithm to achieve safe path planning in dynamic environments. It can be applied to scenarios such as UAV inspection (e.g., power line icing monitoring), mountain rescue, and autonomous driving. It solves the problems of limitations of traditional A* algorithms, difficulty in multi-constraint fusion, and lag in pure real-time meteorological data, forming a safer and more reliable path planning and navigation scheme. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a dynamic obstacle avoidance path planning method for DEM maps based on a rime ice algorithm. Figure 2 The results of dynamic obstacle avoidance path planning on the DEM map; Figure 3 To simulate a three-dimensional terrain mesh map of a DEM; Figure 4 To simulate the results of DEM combined with meteorological rime ice algorithm; Figure 5 To simulate the DEM, the path calculation results were obtained by combining the meteorological rime algorithm and the A* algorithm. Detailed Implementation

[0021] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0022] like Figure 1 As shown in the figure, an embodiment of the present invention provides a dynamic obstacle avoidance path planning method for DEM maps based on the rime algorithm, which includes the following steps: DEM data (including elevation, slope, aspect, etc.) of the target area is acquired. Density clustering and elevation gradient analysis are used to identify data voids. A neural implicit inference model is then employed to complete the surface and subsurface structures, generating a seamlessly integrated 3D terrain model. This 3D terrain model is then rasterized into a 3D mesh, with each mesh cell containing attributes such as terrain gradient and obstacle threat coefficient, serving as the basic search space for path planning. The DEM data is sourced from publicly available geographic information platforms (such as the USGS).

[0023] The system integrates real-time meteorological data (temperature, humidity, wind speed, visibility) and short-term forecast data (such as the probability and duration of rime formation). A rime formation algorithm is used to simulate the rime growth process (initialization-growth-competition-convergence), converting meteorological parameters into obstacle zone identification indicators (e.g., marking an obstacle zone when visibility is <500 meters). The spatial extent and time window of the obstacle zone are dynamically output. Meteorological data must comply with the "Meteorological Data Usage Specifications." The parameter settings of the rime formation algorithm, such as the number of growth iterations and the competition elimination threshold, are optimized.

[0024] The cost function is designed using a fusion of multi-objective constraints:

[0025] In the formula, λ is a weighting coefficient that is dynamically adjusted to balance path length and security.

[0026] Periodically (e.g., every 100ms) updates weather obstacle zone data, triggering local replanning of the A* algorithm. The path is smoothed using Bézier curves to satisfy kinematic constraints (such as UAV turning radius and slope limitations), thus constructing a dynamic obstacle avoidance mechanism. The dynamic obstacle avoidance path planning result on the DEM map is shown below. Figure 2 As shown.

[0027] Example 1 Input DEM data (30-meter resolution), and generate a 3D terrain mesh using the Python GDAL library, such as... Figure 3 As shown.

[0028] The system uses the rime ice algorithm API to process meteorological data and outputs a raster map of the obstacle area (GeoTIFF format), such as... Figure 4 As shown.

[0029] The improved A* algorithm is implemented in Python and integrated into the path planning engine, outputting path coordinates in JSON format, such as... Figure 5 As shown.

[0030] This invention also provides a dynamic obstacle avoidance path planning system for DEM maps based on a rime ice algorithm, comprising: The 3D terrain search space construction module is used to acquire DEM data of the target area, identify data void areas through density clustering and elevation gradient analysis, complete the surface and underground structure using a neural implicit inference model, generate a seamlessly integrated 3D terrain model, and rasterize the 3D terrain model into a 3D mesh. Each mesh cell contains attributes such as terrain gradient and obstacle threat coefficient, which serve as the basic search space for path planning.

[0031] The meteorological obstacle zone prediction and construction module is used to integrate real-time meteorological data and short-term forecast data, simulate the rime ice growth process using the rime ice algorithm, convert meteorological parameters into obstacle zone judgment indicators, and dynamically output the spatial range and time window of the obstacle zone.

[0032] An improved path planning module for the 3D A* algorithm is used to design a cost function that incorporates multi-objective constraints, periodically update meteorological obstacle zone data, trigger local replanning of the A* algorithm, smooth the path using Bézier curves to satisfy kinematic constraints, and construct a dynamic obstacle avoidance mechanism to obtain dynamic obstacle avoidance path planning results for the DEM map.

[0033] Beneficial effects of this invention: 1. DEM terrain modeling error < 0.5 meters, and prediction accuracy for meteorological obstacle areas > 90%; 2. Real-time performance: Path replanning response time <200ms, adapting to dynamic weather changes; 3. Versatility: Applicable to complex terrains such as mountains and cities, and supports multiple platforms such as drones and unmanned vehicles.

Claims

1. A dynamic obstacle avoidance path planning method for DEM maps based on rime ice algorithm, characterized in that, Includes the following steps: Acquire DEM data of the target area, analyze and identify data voids, complete the surface and underground structures, generate a seamlessly integrated 3D terrain model, and rasterize the 3D terrain model into a 3D mesh as the basic search space for path planning. The system integrates real-time meteorological data and short-term forecast data, uses a rime ice algorithm to simulate the rime ice growth process, transforms meteorological parameters into obstacle zone determination indicators, and dynamically outputs the spatial range and time window of the obstacle zone. A cost function is designed by incorporating multi-objective constraints, and meteorological obstacle area data is updated periodically to trigger local replanning of the A* algorithm. The path is smoothed by Bézier curves to satisfy kinematic constraints and a dynamic obstacle avoidance mechanism is constructed to obtain the dynamic obstacle avoidance path planning results of the DEM map.

2. The dynamic obstacle avoidance path planning method for DEM maps based on the frost algorithm as described in claim 1, characterized in that, The target area DEM data includes elevation, slope, and aspect.

3. The dynamic obstacle avoidance path planning method for DEM maps based on the frost algorithm as described in claim 1, characterized in that, Each grid cell of the three-dimensional mesh contains attributes such as terrain gradient and obstacle threat coefficient.

4. The dynamic obstacle avoidance path planning method for DEM maps based on the frost algorithm as described in claim 1, characterized in that, The real-time meteorological data includes temperature, humidity, wind speed, and visibility, while the short-term forecast data includes the probability of rime formation and its duration.

5. The dynamic obstacle avoidance path planning method for DEM maps based on the frost algorithm as described in claim 1, characterized in that, The obstacle zone is defined as a zone with visibility less than 500 meters.

6. The dynamic obstacle avoidance path planning method for DEM maps based on the frost algorithm as described in claim 1, characterized in that, The formula for the cost function of the fusion multi-objective constraint design is as follows: In the formula, λ is a weighting coefficient that is dynamically adjusted to balance path length and security.

7. A dynamic obstacle avoidance path planning system for DEM maps based on rime ice algorithm, characterized in that, The system employs a dynamic obstacle avoidance path planning method for DEM maps based on a frost algorithm as described in claim 1, the system comprising: The 3D terrain search space construction module is used to acquire DEM data of the target area, analyze and identify data void areas, complete the surface and underground structures, generate a seamless 3D terrain model, and rasterize the 3D terrain model into a 3D mesh as the basic search space for path planning. The meteorological obstacle zone prediction and construction module is used to integrate real-time meteorological data and short-term forecast data, simulate the rime growth process using the rime algorithm, convert meteorological parameters into obstacle zone judgment indicators, and dynamically output the spatial range and time window of the obstacle zone. An improved path planning module for the 3D A* algorithm is used to design a cost function that incorporates multi-objective constraints, periodically update meteorological obstacle zone data, trigger local replanning of the A* algorithm, smooth the path using Bézier curves, satisfy kinematic constraints to construct a dynamic obstacle avoidance mechanism, and obtain dynamic obstacle avoidance path planning results for the DEM map.

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