Autonomous traversal of wilderness and broken terrain requires predictive optimization that accounts for vehicle ground clearance, rollover limits, and soil compliance.
Conventional 2D costmaps fail when confronted with tall grass versus solid boulders. Our stochastic risk engine assigns probabilistic traversability costs based on geometric roughness, terrain slope, and multispectral vegetation indices.
The trajectory planner solves a receding-horizon optimization problem over a 5-second horizon at 50 Hz. Cost penalties incorporate roll-over thresholds, suspension travel limits, and power budget constraints, guaranteeing vehicle stability at speeds up to 45 km/h.
In multi-kilometer field trials across boulder-strewn washes and steep clay embankments, the hybrid MPC framework achieved a 99.4% autonomous traversal success rate with zero roll-over incidents and an average speed increase of 38% compared to reactive planners.