| Autor: |
Cai, Zhixi, Cardenas, Cristian Rojas, Leo, Kevin, Zhang, Chenyuan, Backman, Kal, Li, Hanbing, Li, Boying, Ghorbanali, Mahsa, Datta, Stavya, Qu, Lizhen, Santiago, Julian Gutierrez, Ignatiev, Alexey, Li, Yuan-Fang, Vered, Mor, Stuckey, Peter J, de la Banda, Maria Garcia, Rezatofighi, Hamid |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones. The UAV must perceive, reason, and make decisions with limited and uncertain information. We propose NEUSIS, a compositional neuro-symbolic system designed for interpretable UAV search and navigation in realistic scenarios. NEUSIS integrates neuro-symbolic visual perception, reasoning, and grounding (GRiD) to process raw sensory inputs, maintains a probabilistic world model for environment representation, and uses a hierarchical planning component (SNaC) for efficient path planning. Experimental results from simulated urban search missions using AirSim and Unreal Engine show that NEUSIS outperforms a state-of-the-art (SOTA) vision-language model and a SOTA search planning model in success rate, search efficiency, and 3D localization. These results demonstrate the effectiveness of our compositional neuro-symbolic approach in handling complex, real-world scenarios, making it a promising solution for autonomous UAV systems in search missions. |
| Databáze: |
arXiv |
| Externí odkaz: |
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