DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps

2026-08-20

AI Summary

DART-S通过起跳前调整悬架预设改变车辆姿态和轮速，利用局部校准图预测效果并结合可达性审计选择最佳动作，提高越野车跳跃后着陆稳定性。

Abstract

Airborne torque reaction cannot recover takeoff errors beyond the wheel angular-momentum budget. DART-S applies ramp-face suspension preconditioning to change pitch, pitch rate, and wheel spin before liftoff, thereby shifting the queried state and altering the remaining authority budget. To predict how each suspension action reshapes this state-budget pair, DART-S employs a local calibration map. A support-aware selector combines the predicted shift with local outcome evidence and an interval-reachability screen; an exact-pair audit reports residual authority. Across 600 new runs in 72 independent BeamNG sessions, every positive, negative, and boundary query follows its prespecified branch. At the confirmed 40°/13 m/s boundary, DART-S attains 24/24 post-touchdown attitude-criterion successes versus 0/24 for DART (session-level Holm-adjusted p=0.0234). At 11.5 m/s, a 0.35 s timing action attains 23/24 versus 0/24 for the static preset (p=0.0156). The 200 rad/s command guard keeps drivetrain hard-limit exceedance at zero across all 600 runs. The source code will be available at https://github.com/MeridianCAS/DART-S

Problem

• off-road vehicle jumps
• suspension preconditioning
• takeoff errors
• wheel angular-momentum budget

Innovation

• suspension preconditioning
• local calibration map
• support-aware selector
• reachability audit