Drone Flight Planning with Active Learning for Data Collection
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Solution Overview
Problem
Current drone control systems lack the ability to efficiently respond to requests for information from spatial locations by effectively generating and executing flight plans to collect and analyze data, especially when existing sources are insufficient, leading to incomplete or uncertain responses.
Innovation Solution
A computer-implemented method that receives requests for information, generates data requests, configures flight plans for drones to collect data, and analyzes the obtained data to provide accurate responses, using techniques like natural language processing, terrain-aware flight planning, and active learning with support vector machines to ensure reliable data collection and labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If drones are deployed to collect data from spatial locations, then information completeness improves, but flight time and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by generating comprehensive flight plans that anticipate all necessary data collection needs before drone deployment. The flight plan includes multiple spatial locations and data types to be collected, ensuring that the drone can gather all required information in a single optimized mission, thereby improving information completeness while managing flight time efficiently.
Solution Approach 2:
The drone system is designed with multi-functionality to collect various data types (images, videos, sensor data) from multiple spatial locations using a single flight plan. This universal approach allows one drone to perform multiple data collection tasks simultaneously, improving information completeness without proportionally increasing flight time, as the drone optimizes its path to cover all required locations and data types.
2Measurement precision
If multiple data requests are generated to ensure complete information, then response accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of information gathering into multiple discrete data requests, each targeting specific data types from specific spatial locations. This segmentation allows the system to manage complexity by breaking down the overall mission into manageable flight plan components, while still achieving comprehensive data collection for accurate responses. Each segmented data request can be independently optimized and executed.
Solution Approach 2:
The system employs feedback mechanisms where the analysis of obtained sensor data determines whether the request for information can be answered with sufficient confidence. If confidence is below the threshold, the system generates additional data requests based on feedback from the initial analysis, creating a closed-loop system that iteratively improves response accuracy while managing complexity through intelligent decision-making.
3Reliability
If additional data requests are generated when confidence is below threshold, then answer reliability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis of obtained sensor data immediately after collection to determine if the confidence threshold is met. This preliminary action allows for rapid decision-making about whether additional data requests are needed, reducing the time loss by avoiding unnecessary delays. The system is prepared in advance with protocols for generating additional data requests only when truly necessary, based on objective confidence measurements.
Solution Approach 2:
The system uses feedback from analyzing obtained sensor data to dynamically determine whether additional data requests are needed. This feedback-driven approach ensures that additional data collection occurs only when necessary to achieve the required confidence level, optimizing the balance between answer reliability and time efficiency. The feedback loop prevents unnecessary additional flights while ensuring sufficient data collection when needed.
Data Source
AI summary
A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.