让蛇形机器人在定位失效时仍能安全导航，计算速度提升十倍以上。
Risk-aware Integrated Task and Motion Planning for Versatile Snake Robots under Localization Failures
- 将任务与运动规划融合为可求解的凸优化问题
- 仿真与实测显示计算效率提升超10倍，导航时间优化超50%
- 适合复杂地形中对鲁棒性要求高的自主机器人应用
蛇形机器人可在极端地形和狭小空间中实现移动，适用于陆地及太空场景。然而，由于传感器靠近地面且视野受限，其感知与定位仍具挑战。为此，本文提出盲动间歇扫描（BLISS）方法，结合仅依赖本体感觉的移动与间歇性扫描，以应对定位失败与碰撞风险。该方法被建模为集成任务与运动规划（TAMP）问题，形式化为机会约束的混合部分可观马尔可夫决策过程（CC-HPOMDP），但因其历史维度爆炸而难以求解。本文创新性地将其重构成可处理的凸混合整数线性规划问题，显著加速求解并联合生成最优任务-运动规划。在EELS蛇形机器人上的仿真与硬件实验表明，相比现有POMDP规划器，计算效率提升超过一个数量级；与传统两阶段规划相比，导航时间优化率超过50%。
原文摘要 · Abstract (English)
Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated Task and Motion Planning (TAMP) problem that leads to a Chance-Constrained Hybrid Partially Observable Markov Decision Process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex Mixed Integer Linear Program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and $>$ 50\% better navigation time optimality versus classical two-stage planners.
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