Team Planning Algorithm for Reconnaissance Area Scanning
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Solution Overview
Problem
Current route and sensor planning algorithms for teams of entities in dynamic mission scenarios are inefficient and fail to integrate real-time coordination, safety, and aesthetic considerations, leading to suboptimal mission success.
Innovation Solution
The TeamPlanning algorithm partitions the search area into cells and corridors, determines starting cells for each entity, initiates scans, and calculates costs to achieve target coverage while ensuring safety and efficiency, using parameters like PathWeight, ScanWeight, and NewCellWeight to optimize route and sensor planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are deployed to scan a predefined area, then the coverage and reliability of area scanning is improved, but the coordination complexity and computational burden increase significantly
Solution Approach 1:
The patent divides the predefined area into multiple cells and assigns each sensor to scan specific cells, creating independent scan tasks. This segmentation reduces coordination complexity by allowing each sensor to operate autonomously within its assigned cells while maintaining reliable coverage through systematic cell assignment.
Solution Approach 2:
The system pre-calculates scan plans and cell assignments before sensors begin scanning. By determining the scan sequence and cell allocation in advance, the system reduces real-time coordination burden while ensuring reliable and efficient area coverage.
2Adaptability or versatility
If real-time route and sensor planning is performed for dynamic mission scenarios, then mission adaptability and success rate are improved, but the computational time and processing requirements increase
Solution Approach 1:
The patent implements dynamic replanning capabilities that allow the system to adapt to changing mission scenarios in real-time. The system can recalculate scan plans and sensor routes based on new information or changing conditions, maintaining high mission adaptability while using efficient algorithms to minimize computational time.
Solution Approach 2:
The system adjusts planning parameters such as scan cell selection, sensor assignment, and route optimization based on mission requirements. By changing these parameters dynamically, the system achieves adaptability to different mission scenarios while maintaining computational efficiency through parameter-based control rather than complete recalculation.
3Measurement precision
If comprehensive scan coverage is achieved by multiple sensors, then the area analysis accuracy is improved, but the mission cost and resource consumption increase
Solution Approach 1:
The patent combines the scan coverage of multiple sensors to achieve comprehensive area analysis. By coordinating sensors to scan different cells and merging their observations, the system achieves high area analysis accuracy while optimizing resource consumption through efficient sensor utilization and avoiding redundant scans.
Solution Approach 2:
The system performs scans on selected cells based on mission requirements rather than scanning every possible cell. By identifying and scanning only the necessary cells for the mission objective, the system achieves sufficient area analysis accuracy while reducing resource consumption and avoiding excessive scanning actions.
Data Source
AI summary
A method routes a predefined number of entities (200) through a predetermined area for scanning the content of the predetermined area. The method comprises the steps of: partitioning the predetermined area into a first set of cells (100); grouping the first set of cells (100) into a number of corridors (201) equal to the number of entities (200); determining a starting cell for each entity (200) in each corridor (201); initiating a scan of cells of each of the corridors (201) from the starting cell of each of the entities (200) and determining the content of each of the scanned cells; and determining a total cost for each entity (200) to travel to each of the scanned cells scanned by each entity (200) and performing a scan from each of the scanned cells by each entity (200).