4D Mission Analytics Visualization for Multi-Vehicle Plan Tradeoffs
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Solution Overview
Problem
Current mission planning and control systems for manned and unmanned vehicles are limited in their ability to dynamically analyze and visualize the impact of environmental changes on future mission plans, requiring manual and time-consuming comparisons, and struggle to coordinate multiple vehicles, especially heterogeneous and mixed manned-unmanned teams.
Innovation Solution
A system that includes an analytics collector, asset filter, analytic aggregator, and rendering pipeline to generate dynamic visualizations of mission analytics for manned and unmanned vehicles or vehicle swarms, allowing real-time status and future projections of fuel supply, payload actions, and communication availability, enabling rapid tradeoffs between current and alternate mission plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coordination of mission plans for multiple vehicles is performed, then each vehicle plan can be individually optimized, but the planning process becomes time-consuming and creates a bottleneck when managing three or more vehicles
Solution Approach 1:
The system creates computational models (copies) of mission plans and vehicles to simulate and evaluate performance without requiring manual coordination. These digital twins allow automated assessment of multiple vehicle scenarios, eliminating the time-consuming manual process while maintaining evaluation accuracy through virtual experimentation and analysis.
Solution Approach 2:
The patent replaces the manual mechanical process of coordinate planning with an automated computational system. The analytics collector, asset filter, analytic aggregator, and rendering pipeline work together to automatically evaluate mission plans for multiple vehicles, substituting human operators with an automated information processing system that handles complex multi-vehicle coordination efficiently.
2Productivity
If computational metrics are used to quantify mission plan performance, then evaluation speed improves, but the system becomes tied to single-vehicle planners and loses ability to evaluate alternatives for heterogeneous vehicle teams
Solution Approach 1:
The system implements a universal analytics framework that can evaluate mission plans for any vehicle type through standardized interfaces. The asset filter and analytic aggregator are designed to work with heterogeneous vehicle teams by accepting diverse input formats and normalizing them into common performance metrics, enabling the same computational engine to handle single vehicles, homogeneous teams, or mixed manned-unmanned formations equally effectively.
3Measurement precision
If detailed visual comparison of current and previous mission plan iterations is performed, then plan quality can be assessed, but the process is manually cumbersome and slow
Solution Approach 1:
The system implements automated feedback loops that continuously compare current mission plan iterations against previous versions and defined success criteria. The analytics collector gathers performance data, the analytic aggregator computes differences and trends, and the rendering pipeline presents visual feedback to operators, creating an automated iterative improvement process that maintains high comparison accuracy while eliminating manual effort through continuous automated evaluation and reporting.
Data Source
AI summary
A system includes an analytics collector that receives world state data to provide status relating to a plurality of mission analytics of an unmanned vehicle or an unmanned vehicle mission planner. An asset filter filters the status from the analytics collector with respect to mission analytics of a subset of selected assets. An analytic aggregator collects the filtered status from the asset filter and generates a visual analytics file based on one or more selected analytics for the subset of selected assets. A rendering pipeline processes the visual analytics file from the analytic aggregator and generates a formatted output file describing a visualization of the plurality of mission analytics from the visual analytics file.