3D Multi-Factor Pathfinding Using Weighted RF Costmaps
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Solution Overview
Problem
Existing pathfinding systems, such as FoxTEN and ATAK, fail to account for radio frequency (RF) propagation data and multiple physical properties in determining optimal paths, leading to potential mission failures, equipment damage, or loss of life due to unaddressed obstructions and vulnerabilities in complex environments.
Innovation Solution
The implementation of a multi-layer costmap system that incorporates RF propagation data and various physical properties, allowing for weighted data layers to prioritize or deprioritize specific environmental factors, thereby determining the lowest-cost path based on user-specified criteria and updated data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing pathfinding systems (FoxTEN, ATAK) are used, then path determination is simple and fast, but RF propagation data and multiple physical properties are not accounted for, leading to mission failures and equipment damage
Solution Approach 1:
The pathfinding system is segmented into multiple independent costmap layers, each representing a specific physical property (terrain, RF propagation, hostile forces, friendly forces, etc.). Each layer processes and evaluates one aspect of the environment independently, then their costs are combined to determine the overall optimal path. This segmentation allows comprehensive environmental analysis without creating a monolithic complex system.
Solution Approach 2:
The costmap framework is designed as a universal multi-functional system that can incorporate various types of data (physical terrain, RF propagation characteristics, location of forces, environmental obstacles) into a unified pathfinding approach. The same basic algorithm structure handles diverse data types by weighting different costmap layers, making the system adaptable to multiple mission requirements and environmental conditions.
2Reliability
If multiple physical properties and RF propagation data are incorporated into pathfinding, then path optimality improves, but data processing time and computational requirements increase
Solution Approach 1:
Costmap layers are pre-generated and stored for different environmental factors before pathfinding is required. RF propagation data, terrain information, and force locations are processed into standardized costmap formats in advance, so that during actual path determination, the system only needs to combine pre-computed layers using weighted sums rather than processing raw data from scratch, significantly reducing real-time computational requirements.
3Adaptability or versatility
If weighted data layers are used to prioritize physical properties, then user preferences are accommodated, but system complexity increases
Solution Approach 1:
User preferences are accommodated by changing the weight parameters assigned to different costmap layers rather than altering the fundamental system architecture. Each layer maintains a weight value that can be adjusted to reflect user priorities (e.g., higher weight for avoiding hostile forces, lower weight for terrain difficulty). This parameter-based approach allows flexible adaptation to different mission requirements while keeping the underlying system structure relatively simple and manageable.
Data Source
AI summary
Techniques for multi-factor pathfinding are disclosed. A system receives multiple data sets that represent multiple physical properties associated with multiple three-dimensional coordinates of a physical environment. The system generates multiple weighted data layers corresponding to the physical properties, at least by applying weights to the data sets. The system generates a multi-factor costmap for the three-dimensional environment, based at least on the weighted data layers. The system determines a path through the three-dimensional environment, based at least on the multi-factor costmap.