An open-source, risk-aware planetary pathfinding engine that integrates Chandrayaan-2 Orbiter High-Resolution Camera (OHRC) imagery data via Shape-from-Shading (SfS) models with an optimized navigation kernel to proctor safe lunar traverses inside Doubly Shadowed Craters (DPSRs).
Our navigation framework dynamically computes path variations based on fine-scale micro-topography metrics. The dashboard pipeline enables seamless transition across all operational stages:
| Phase 1: Multi-Objective Rover Path Trajectories | Phase 2: Multi-Criteria Touchdown Site Assessment |
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| Phase 3: Deep Radar Subsurface Inversion Profile | Phase 4: PSR Local Climatology & Survival Metrics |
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Figure 1: Complete 4-Phase Operational Dashboard mapping optimized trajectories, touchdown suitability heatmaps, polarimetric ice volumes, and localized micro-cold trap survival metrics.
- Shivam Kumar Jha - IIIT Manipur
- Abhinav Jha - IIIT Manipur
- Abhinav Kumar Jha - IIIT Manipur
- Arihant Mishra - IIIT Manipur
- Sub-meter Micro-Topography Analysis: Implements a Shape-from-Shading (SfS) kernel to resolve sub-meter micro-hazards from grazing illumination data.
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Native Cloud
$A^*$ Optimization Engine: High-performance heap priority-queue execution evaluating continuous localized slope gradients and applying safe scalar energy penalties. -
Landing Suitability Indexing (LSS): Multi-criteria touchdown evaluator sorting target coordinates via a weighted safety-to-proximity matrix layout (
$LSS = 0.6 \times \text{Safety} + 0.4 \times \text{Proximity}$ ). - Resource Inversion Profiling: Dynamic volumetric evaluation simulating Chandrayaan-2 DF-SAR polarimetric mosaic layers to compute subsurface ice shelf metrics and purity confidence profiles.
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PSR Thermal Survival Telemetry: Specialized local environment monitoring evaluating micro-cold trap conditions (
$40\text{ K}$ surface limits) for rover thermal readiness validation.
app.py— Main entry point running the web dashboard, layout matrix nodes, and native pathfinding workflows.requirements.txt— Environment definition file specifying required Python packages (streamlit,matplotlib,numpy).pathfinder_sfs.cpp— Native C++ core engine handling priority queues, neighbor evaluations, and SfS cost-sweeps.
To host and test the system locally on your environment terminal window:
pip install -r requirements.txt
python -m streamlit run app.py