Drone Navigation Path Adjustment for Surveillance Blind Spots
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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, even when they are in camera blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the drone follows a fixed initial navigation path to the property, then the navigation is simple and direct, but the drone cannot capture evidence in camera blind spots or adjust to changing surveillance objectives
Solution Approach 1:
The navigation path is transformed from a static fixed route to a dynamic adaptive path. The system continuously receives monitoring system data during flight, identifies surveillance objectives in real-time, and adjusts the navigation path dynamically to capture evidence in camera blind spots while maintaining operational simplicity through automated processing.
Solution Approach 2:
The system implements feedback by continuously receiving monitoring system data from cameras and sensors during drone operation. This real-time feedback enables the drone to identify surveillance objectives, determine camera blind spots, and adjust its navigation path accordingly, resolving the contradiction between adaptability and system complexity.
2Reliability
If the drone captures evidence in camera blind spots by adjusting its path, then surveillance coverage is improved, but the navigation time and computational processing increase
Solution Approach 1:
The system performs preliminary identification of surveillance objectives and camera blind spots using monitoring system data before the drone arrives at locations. This advance preparation enables the drone to efficiently navigate to high-priority evidence locations without unnecessary delays, maintaining reliability while minimizing navigation time.
Solution Approach 2:
The automated system independently processes monitoring data, identifies surveillance objectives, determines camera blind spots, and generates adjusted navigation paths without human intervention. This self-service capability ensures reliable evidence capture in blind spots while minimizing navigation time through automated real-time decision-making.
3Productivity
If the drone prioritizes multiple surveillance objectives simultaneously, then comprehensive coverage is achieved, but the system complexity and processing requirements increase
Solution Approach 1:
The system applies local quality by assigning different priority levels to different surveillance objectives based on their specific characteristics and locations. High-priority objectives such as those in camera blind spots receive focused attention, while lower-priority areas are covered efficiently, optimizing productivity without requiring complex management of all objectives simultaneously.
Solution Approach 2:
The system changes parameters by dynamically adjusting priority levels and navigation paths based on real-time monitoring data. This parameter adjustment enables the drone to efficiently manage multiple surveillance objectives by focusing resources on high-priority targets while maintaining comprehensive coverage, resolving the contradiction between productivity and system complexity.
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.