Multi-UAS Mission Planning With Adaptive Flight Reassignment
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Solution Overview
Problem
Existing systems lack an efficient method to plan and manage multiple Unmanned Aerial Systems (UAS) performing adaptive missions, particularly in handling unexpected conditions and UAS behavior during data collection tasks.
Innovation Solution
A computing system that manages a fleet of UAS collaboratively performing a mission by receiving mission requirements, calculating data acquisition points and flight plans, verifying UAS locations, and adjusting flight plans based on discrepancies and changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a plurality of UAS are used to perform data collection missions, then productivity and data acquisition capability are improved, but device complexity and coordination difficulty increase
Solution Approach 1:
The patent divides the mission area into discrete geographic cells and assigns specific cells to individual UAS. Each UAS is responsible for covering its assigned cells and collecting data from data acquisition points within those cells. This segmentation reduces coordination complexity by localizing each UAS's responsibilities while maintaining overall mission productivity through parallel operations across multiple cells.
Solution Approach 2:
The system performs preliminary calculation of data acquisition points and flight plans before the mission begins. The central system computes optimal flight paths, assigns cells to UAS, and prepares contingency plans in advance. This preliminary action reduces real-time coordination complexity while enabling high productivity during actual data collection operations.
2Ease of operation
If preprogrammed flight paths are used for UAS, then ease of operation is improved, but adaptability to unexpected field conditions deteriorates
Solution Approach 1:
The system continuously monitors each UAS's actual position during flight and compares it to the expected position from the preprogrammed flight plan. When discrepancies exceed a threshold, the system automatically recalculates flight paths and redistributes cell assignments to accommodate the unexpected condition. This feedback mechanism maintains ease of operation through automated responses while achieving high adaptability to field conditions.
Solution Approach 2:
The flight plans are designed to be dynamic rather than static. While initial flight paths are preprogrammed for ease of operation, the system continuously updates these paths based on real-time UAS performance and environmental conditions. This allows the system to maintain simple preprogrammed structures while adapting dynamically to unexpected situations.
3Adaptability or versatility
If manual planning and division of labor between multiple UAS is performed, then adaptability to specific mission requirements is improved, but loss of time in planning and coordination increases
Solution Approach 1:
The patent replaces manual mechanical planning processes with automated computational systems. The central system automatically calculates data acquisition points, optimizes flight paths, and assigns cells to UAS based on mission requirements and UAS capabilities. This substitution maintains high adaptability to custom mission requirements while eliminating the time loss associated with manual planning and coordination.
4Adaptability or versatility
If flight plans are frequently adjusted during mission, then adaptability to field conditions is improved, but reliability and mission completion certainty deteriorate
Solution Approach 1:
The system employs continuous feedback monitoring of UAS position and performance, triggering flight plan adjustments only when necessary based on objective discrepancy thresholds. This controlled feedback approach maintains reliability by avoiding unnecessary adjustments while achieving necessary adaptability when field conditions actually require changes.
Solution Approach 2:
The system changes flight plan parameters systematically based on measured deviations. Rather than making arbitrary adjustments, the system modifies flight paths and cell assignments based on quantified position discrepancies and performance data. This parameter-based approach maintains mission reliability while enabling systematic adaptation to field conditions.
Data Source
AI summary
A computerized system and method for managing a fleet of Unmanned Aerial Systems (UAS) collaboratively performing a mission. The system receives mission requirements comprising mission goals, area of operation, characteristics of each UAS and calculates data acquisition points in the area of operation according to mission goals, sensor types and constraints. It then calculates a flight plan for each UAS. Once flying in the field, the system verifies for each UAS at predetermined intervals during flight the UAS' aerial location at that time and compares the UAS aerial location to the expected aerial location according to flight plan in that time. If necessary, adjustments are made to the flight plan of one or more UAS. Other considerations for flight adjustments are battery power level, mission goal accomplishments, signal quality and more.