Drone Route Safety Matrix for Hazard Avoidance
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Solution Overview
Problem
Existing drone delivery systems face challenges in ensuring the safety of drones and parcels during flight, particularly due to obstacles like roads, traffic, birds, and water bodies, which can result in damage or loss of parcels and pose risks to drivers and drones.
Innovation Solution
A computer-implemented method that generates an improved route for drone flights by utilizing a safety matrix that represents geographical areas, incorporating sensor information and artificial intelligence to analyze safety factors along potential routes, and recommending safer or safest flight paths to enhance safety and reduce risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a direct route is used for drone delivery, then delivery time is reduced, but safety is compromised due to obstacles like roads, traffic, birds, and water bodies
Solution Approach 1:
The system performs preliminary safety analysis by creating a safety matrix that pre-identifies hazardous areas (roads, water bodies, bird zones) along the direct route before the drone departs. This allows the drone to take the direct route while avoiding pre-identified dangers, thus maintaining both speed and safety
Solution Approach 2:
The safety matrix acts as an intermediary layer between the direct route and the drone flight. It processes geographical data and hazard information to generate safety scores for different areas, enabling the drone to navigate the direct route safely by avoiding regions with low safety scores
2Reliability
If safety analysis is performed along the direct route, then flight safety is improved, but computational complexity increases
Solution Approach 1:
The geographical area is segmented into a grid system where each cell in the safety matrix represents a discrete portion of the map. This segmentation allows the system to analyze safety factors for manageable sections rather than the entire continuous space, reducing computational complexity while maintaining comprehensive safety coverage
Solution Approach 2:
The system transforms complex geographical and hazard data into simplified numerical safety factors that are stored in the safety matrix. By converting qualitative hazard assessments into quantitative parameters, the system enables efficient computational analysis of multiple routes without excessive complexity
3Reliability
If multiple safety factors are analyzed, then route safety is improved, but data processing requirements increase
Solution Approach 1:
The system merges multiple safety factors (road proximity, water body distance, bird presence, terrain features) into a single integrated safety matrix. Each cell in the matrix contains a composite safety score that combines all relevant factors, allowing the system to evaluate multiple parameters simultaneously without proportionally increasing data processing requirements
Data Source
AI summary
A computer-implemented method, a computer system, and a computer program product are provided. A first computer receives a message that indicates a destination location for a drone flight. The first computer generates a first recommendation for a route from a departure location to the destination location for the drone flight. The generating includes relying on a first safety matrix that represents a geographical area that includes the departure and destination locations. The first safety matrix includes rows and columns of numbers. Each number represents a first safety factor for a respective portion of a map that illustrates the geographical area. The generating also includes relying on a first analysis regarding a direct route between the departure and destination locations. The first analysis includes analyzing values of the first safety matrix along portions representing the direct route.