Problem
Current off-world mission planning relies on static, manual interpretation of environmental data. This creates a dangerous gap between raw sensor imagery and the actual physics-based limits of colonial hardware.
The PSA matters because it automates the extraction of affordances—turning a map into a set of engineering boundaries. This prevents total system loss caused by environmental miscalculations in long-duration autonomous missions.
Solution
Acting as the Sensory Cortex of the STC, the PSA provides the validated environment models required for all downstream simulations.
- Consumes: Topographic maps, multi-spectral imagery, and NASA PDS data.
- Produces: Creates and modifies
all_environments.yamlyaml files and schemas. - Interfaces: Feeds the Simulation Engine and MILP Optimizer.
Method
*In Development and Concept Stage of Planning*
Tools & Technologies
Diagrams / Visuals
[Architecture Diagram: Raw Data → Extraction Engine → STC Environment Schema + STC Environment yaml data]
Results & Outcomes
Currently, the framework successfully handles structural validation and manual environment selection. Phase 2 of the STC successfully shows that any environment that meets schema validation can be used for the system.
Current Credibility: Schema and validation layers are fully implemented; simple solar flux logic has been implimented
Next Steps
- Implementation of computer vision to analyse datasets and provide geospatial data.
- Create autonmated engine to generate environment and sub environment yaml.