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This research presents DART-S, a control algorithm that uses suspension preconditioning and reachability auditing to optimize off-road vehicle jump performance in the BeamNG simulation environment. By shifting state budgets before liftoff, the method achieves perfect success rates at critical velocity boundaries where static presets fail entirely, demonstrating advanced applications of machine learning in robotics control.
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DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.20275v1 Announce Type: new 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{\deg}/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

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 this https URL

Robotics (cs.RO)

arXiv:2608.20275 [cs.RO]

(or arXiv:2608.20275v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.20275 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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