Distributed Drone Flight Path Builder for Terrain-Aware Surveying
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Solution Overview
Problem
Existing drone flight planning technologies face challenges in optimizing the use of multiple drone types with different capabilities to efficiently cover large areas, particularly in varying terrain, while minimizing costs and ensuring autonomous navigation.
Innovation Solution
The Distributed Drone Flight Path Builder System (DIFPL) divides areas into subregions based on terrain and drone capabilities, using the MapReduce framework to distribute flight plan construction and optimize subregion merging, allowing for autonomous navigation with cost-effective drone deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a single drone is used to cover large areas, then the system is simple to operate, but the coverage area and efficiency are limited
Solution Approach 1:
The system divides the large survey area into multiple subregions and assigns different flight plan portions to different drones. The flight planning system segments the overall flight plan into manageable portions that can be distributed across multiple drone units, enabling parallel survey operations and significantly increasing coverage area and efficiency
Solution Approach 2:
The system combines the capabilities of multiple drones to cover large areas that would be impossible for a single drone. By merging the operational capacity of multiple drones working in parallel on different subregions, the system achieves both extensive coverage and high survey efficiency
2Productivity
If multiple drones with different capabilities are deployed, then the survey efficiency and coverage improve, but the system complexity and coordination difficulty increase
Solution Approach 1:
The system assigns specific subregions to drones based on their individual capabilities and characteristics. Each drone operates autonomously within its assigned subregion with flight plans tailored to its specific performance parameters, eliminating the need for complex real-time coordination while optimizing survey efficiency
Solution Approach 2:
Each drone autonomously executes its assigned flight plan portion without requiring continuous external coordination. The system enables self-service operation where drones independently navigate and survey their designated areas, reducing system complexity while maintaining high productivity
3Reliability
If the entire flight plan is stored in one drone, then data management is simple, but the autonomy and reliability of individual drones are reduced
Solution Approach 1:
The flight plan data is segmented into separate portions and distributed to different drones. Each drone stores only the flight plan portion relevant to its assigned subregion, enabling autonomous operation while simplifying data management through localized storage rather than centralized keeping
4Productivity
If flight plans are optimized for each subregion, then the number of flights is minimized, but the computational complexity increases
Solution Approach 1:
The computational task of flight plan optimization is segmented by subregion rather than processing the entire area as one large problem. This division into smaller computational units reduces the complexity of optimization calculations while still achieving efficient flight paths that minimize the number of required flights
Data Source
AI summary
Drones have become ubiquitous in performing risky and labor intensive areal tasks cheaply and safely. To allow them to be autonomous, their flight plan needs to be pre-built for them. Existing works do not precalculate flight paths but instead focus on navigation through camera based image processing techniques, genetic or geometric algorithms to guide the drone during flight. That makes flight navigation complex and risky. We present automated flight plan builder DIFPL which pre-builds flight plans for drones to survey a large area. The flight plans are built for subregions and fed into drones which allow them to navigate autonomously. DIFPL employs distributed paradigm on Hadoop MapReduce framework. Distribution is achieved by processing sections or subregions in parallel. Experiments performed with network and elevation datasets validate the efficiency of DIFPL in building optimal flight plans.