Autonomous Data Collection Mission Control for Dynamic Fleet Routing
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Solution Overview
Problem
Current autonomous vehicle systems require human direction for data collection missions, leading to inefficiencies and delays, especially as the fleet size increases, due to semi-automated data acquisition strategies that need human input to determine target objects and locations.
Innovation Solution
Implementing a system that dynamically updates data collection mission objectives and parameters using machine learning models and real-world data, allowing for automated resource allocation and prioritization of target objects, reducing the need for human intervention by using computing devices to adjust data collection operations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semi-automated data acquisition strategies are used with human direction, then data collection can be performed with current systems, but delays and inefficiencies increase as fleet size grows
Solution Approach 1:
The system enables data collection vehicles to autonomously determine target objects and optimize their own data collection missions without human intervention. The onboard computing devices automatically receive mission objectives, identify target objects using sensor data and machine learning models, and adjust collection parameters in real-time, allowing the fleet to self-manage data acquisition operations
Solution Approach 2:
The system dynamically updates data collection mission parameters during operation based on real-time sensor data, machine learning model predictions, and fleet status. Mission objectives, target object selections, and collection parameters are continuously adjusted to optimize productivity and minimize delays as conditions change
2Quantity of substance
If more data collection vehicles are deployed to increase data volume, then more data can be collected, but human coordination requirements scale proportionally
Solution Approach 1:
The system implements a universal centralized server that can manage and coordinate any number of data collection vehicles through a single platform. This server handles mission distribution, real-time monitoring, and parameter updates for the entire fleet, allowing data volume to scale with fleet size without proportionally increasing coordination complexity
Solution Approach 2:
Each vehicle autonomously manages its own data collection operations by receiving high-level objectives from the centralized server and independently identifying target objects, adjusting parameters, and executing missions without requiring direct human coordination for each vehicle
3Ease of operation
If human operators manually direct data collection targets and locations, then data collection can be controlled, but the process requires significant human input and intervention
Solution Approach 1:
The system replaces manual human direction with autonomous onboard computing devices that automatically determine target objects and optimize data collection missions. Vehicles use their own sensors, machine learning models, and real-time data to self-direct their operations while maintaining centralized oversight
Solution Approach 2:
The system continuously receives feedback from vehicle sensors, onboard processors, and centralized servers to automatically adjust mission parameters and target selections. This closed-loop feedback mechanism enables automated control that adapts to changing conditions without human intervention
Data Source
AI summary
Disclosed herein are systems, methods, and computer program products for controlling data collection by resources. The methods comprise: receiving real-world data collected by the resources in accordance with data collection mission (DCM) parameters; receiving user defined DCM goal(s); updating goal(s) for DCM mission(s) based on the real-world data and the user defined DCM goal(s); modifying the data DCM parameter(s) based on the updated goal(s) and which ones of the resources are still available for DCMs; and causing data collection operations (which are currently being performed by the resource(s)) to change in accordance with the modified DCM parameter(s).