الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Zhang, H., Zhao, F., Li, Z., Guan, X., Cheng, P., & Li, S. (2026). VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation. https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation
MLA 9
Zhang, Hanbing, et al. "VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation." https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation.
شيكاغو (المؤلف–التاريخ)
Zhang, Hanbing, Fangguo Zhao, Zerui Li, Xin Guan, Peng Cheng, and Shuo Li. 2026. "VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation." https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation.
هارفارد
Zhang, H., Zhao, F., Li, Z., Guan, X., Cheng, P. and Li, S. (2026) 'VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation', Available at: https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation.
فانكوفر
Zhang H, Zhao F, Li Z, Guan X, Cheng P, Li S. VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation. https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation
IEEE
H. Zhang, F. Zhao, Z. Li, X. Guan, P. Cheng, and S. Li, "VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation," https://omanscience.com/ar/articles/vlm-mppi-grounding-natural-language-in-behaviorally-diverse-trajectories-for-aerial-navigation.