レシピなしで脱出せよ：粒状体に埋まった火星ローバーの機動非依存な復帰制御
Escape Without a Recipe: Maneuver-Agnostic Mars Rover Recovery from Granular Entrapment
緩いレゴリスに埋まった車輪型ローバーが、決められた機動に頼らずリカレント方策で脱出する手法を提案し、MPMシミュレータ上で従来のスクリプト制御を大きく上回る脱出成功率を示した。
著者: Meraj Hossain Promit, Chandak Chakma, Md Jubair Ahmed Sourov, Sejuti Rahman
分類: cs.RO
原文アブストラクト
Wheeled planetary rovers immobilised in loose regolith have no principled recovery controller: scripted maneuvers are terrain-specific, and naive driving deepens entrapment. We introduce a recurrent policy that learns to escape without a predefined maneuver. The framework rests on a verified trap: in a continuum material point method (MPM) simulator we identify a configuration at sinkage-to-radius ratio z/r = 2.0 where the strongest of ten scripted controllers succeeds in only 7/32 trials, yet the regolith retains traction and continues to shear, confirming granular entrapment rather than a numerical artifact. A frozen entrapment detector, trained on 81,771 windows spanning three materials and six sinkage levels, supplies a three-state belief to the policy observation. A separate simulator mobility latch gates training rewards. Across five training seeds, mean action evaluation achieves 62.5% escape (range 28.1-81.3%; 32 trials per seed) versus 7/32 for the strongest scripted controller and 0/32 for action noise. A numerical audit identifies a coupling-buffer fault that silently discarded 9.7% of contact nodes at 16 parallel environments. Results establish recovery in the tested simulation regime; physical transfer remains unverified.