DART-S：面向越野车辆跳跃的可达性审计主动悬架预处理
DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps
- Research Center for Intelligent Computing Systems, Institute of Computing Technology, CAS（中国科学院计算技术研究所智能计算系统研究中心）
- Dong Feng Off-Road Vehicle Co., Ltd（东风越野车有限公司）
- School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对越野车辆跳跃的起飞误差问题，提出DART-S方法，通过主动悬架预处理调整离地前状态并结合可达性审计，在BeamNG测试中大幅提升触地后姿态标准成功率，且未超限传动系硬极限
AI中文摘要：
空中扭矩反应无法恢复超出车轮角动量预算的起飞误差。DART-S应用坡道面悬架预处理，在离地前改变俯仰角、俯仰率和车轮转速，从而调整查询状态并改变剩余权限预算。为预测每个悬架动作如何重塑该状态-预算对，DART-S采用局部校准图。支持感知选择器将预测的变化与局部结果证据及区间可达性筛选结合；精确对审计报告剩余权限。在BeamNG的72个独立会话中，针对600次新运行，所有正、负及边界查询均遵循其预设分支。在确认的40°/13 m/s边界处，DART-S的触地后姿态标准成功率为24/24，而DART为0/24（会话级Holm校正后p=0.0234）；在11.5 m/s时，0.35秒的时序动作成功率为23/24，静态预设为0/24（p=0.0156）。200 rad/s的指令防护使所有600次运行的传动系硬极限超限次数为0。源代码将在此URL处提供
英文摘要：
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
