Adaptive Drone Routing Using Local Surveillance Objectives
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Solution Overview
Problem
Current drone navigation systems lack the ability to dynamically adjust their paths based on real-time surveillance objectives and environmental data, such as camera blind spots and priority levels, which can lead to inefficiencies in capturing critical evidence during events like trespassing.
Innovation Solution
A drone system that generates a navigational model using local monitoring system data to identify surveillance objectives, prioritize tasks, and adjust its navigation path to optimize the capture of evidence, such as images of trespassers or vehicles, by integrating data on camera fields of view and blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the drone follows an initial navigation path to the property, then it can reach the destination, but it may miss critical surveillance objectives in blind spots or low-priority areas
Solution Approach 1:
The navigation path is made dynamic by allowing real-time adjustments based on detected surveillance objectives. The drone transitions from a static initial path to an adaptive path that dynamically incorporates new information from monitoring systems, prioritizing areas with high-value surveillance objectives while maintaining overall mission efficiency.
Solution Approach 2:
The system implements feedback loops where the drone continuously receives monitoring data from local systems, identifies new surveillance objectives, and adjusts its navigation path accordingly. This closed-loop control ensures that the drone responds to real-time conditions, capturing evidence in blind spots and prioritizing critical areas without completely abandoning the original mission trajectory.
2Loss of information
If the drone adjusts its path to capture all possible surveillance objectives, then evidence collection improves, but navigation time and energy consumption increase
Solution Approach 1:
The drone applies local quality by prioritizing surveillance objectives based on their importance and characteristics. Different areas are treated differently: high-priority objectives in blind spots receive focused attention with path adjustments, while low-priority areas are covered efficiently without excessive deviation. This selective approach ensures complete information collection while minimizing unnecessary navigation time.
Solution Approach 2:
The system changes navigation parameters dynamically based on the detected surveillance objectives. When high-priority objectives are identified in blind spots, the drone adjusts its path parameters to capture these areas. The prioritization mechanism changes navigation parameters adaptively, balancing information completeness with time efficiency by focusing resources on critical surveillance needs.
3Adaptability or versatility
If the drone uses a simple navigation system, then device complexity is low, but it cannot adapt to real-time surveillance objectives and environmental data
Solution Approach 1:
The navigation system achieves multi-functionality by integrating multiple capabilities: initial path planning, real-time objective detection, prioritization algorithms, and dynamic path adjustment. This universal system handles both routine navigation and adaptive response to surveillance objectives, eliminating the need for separate specialized systems while maintaining high adaptability.
Solution Approach 2:
The drone's navigation system performs self-service by automatically detecting surveillance objectives from monitoring data, prioritizing them based on predefined criteria, and adjusting its own path without external intervention. This autonomous decision-making reduces the need for complex external control systems while maintaining high adaptability to real-time conditions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a storage device, for using a drone to monitor a community. The drone may include a processor and a storage device storing instructions that, when executed by the processor, cause the one or more processors to perform operations. The operations may include receiving an instruction to deploy based on a determination, by a community monitoring system that an event was detected at a property of the community, navigating towards the property along an initial navigation path, obtaining local monitoring system data from a local monitoring system of a property of the community, generating based on the local monitoring system data a navigational model that identifies a location of each of one or more surveillance objectives, determining an adjusted navigation path to a location of a surveillance objective of the one or more surveillance objectives, and navigating along the adjusted navigation path.