VLM-MPPI: 自然言語を多様な飛行行動に接地する空中ナビゲーション
VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation
自然言語の指示を6つの行動条件付きMPPIプランナで生成した多様な軌道候補に結びつけ、VLMがFPV画像から候補を選ぶ階層型UAVナビゲーションを実機とシミュレーションで実証した。
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著者: Hanbing Zhang, Fangguo Zhao, Zerui Li, Xin Guan, Peng Cheng, Shuo Li
分類: cs.RO
原文アブストラクト
We present a hierarchical UAV navigation framework that aligns natural-language intent with dynamically feasible flight behaviors in cluttered indoor environments. To bridge the gap between abstract semantics and low-level control, we employ a parallelized ensemble of six behavior-conditioned Model Predictive Path Integral (MPPI) planners. Crucially, by designing mode-specific guiding costs and sampling biases, we induce distinct trajectory modes that converge to unique behavioral means, yielding a compact set of intentionally diverse candidates rather than mere stochastic variations. We project these 3D candidates onto the onboard first-person-view RGB stream, turning language grounding into a visual action selection problem. A pretrained vision--language model (VLM) asynchronously selects the candidate index given the overlaid FPV image and a natural-language prompt, while MPPI replans at 20Hz and a PID-based low-level controller tracks the selected trajectory. We implement the full pipeline in NVIDIA Isaac Sim and on a real-world quadrotor platform equipped with LiDAR and RGB sensing. Experiments in both simulation and real-world flights show semantically meaningful behavior diversity, robust language alignment despite VLM latency, and safe, repeatable flight across all modes, achieving 100% task success in our evaluated scenarios.