ML Trajectory Deployment Optimizer
Compare deployment strategies using neural network trajectory estimation for optimal swarm deployment sequencing. Evaluates sequential, batch, greedy, and NN-guided approaches across Monte Carlo runs.
Deployment Parameters
Strategy Comparison
Compares 4 strategies deploying 500 units with 20 tugs over 50 Monte Carlo iterations.
Deployment Timeline
Run simulation to see deployment timeline
Propellant Usage by Strategy
Run simulation to see propellant comparison
Strategy Comparison
Run a simulation to compare deployment strategies
Simulation Methodology
This simulator models the deployment of collector units from an assembly node (L1 or L4) to evenly-distributed orbital slots around a heliocentric orbit. A trained neural network approximates delta-V costs for each transfer, replacing expensive Lambert solver iterations.
- Sequential: Deploy units one-by-one in slot order, round-robin across tugs
- Batch: Tugs carry 3 units per trip, deploying in angular clusters
- Greedy: At each step, pick the nearest undeployed slot to assembly node
- NN-Guided: Use NN delta-V estimates to globally optimize deployment sequence
Propellant consumption follows the Tsiolkovsky rocket equation with ion propulsion (Isp 3000s). Monte Carlo uncertainty comes from stochastic fuel efficiency (+/-5%).
This simulator investigates RQ-1-43: ML trajectory deployment optimization
View Swarm Control System BOM Item