International Journal For Multidisciplinary Research

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FloodNav — Liquid Neural Networks with Reinforcement Learning for Precision Drone Flight in Disaster Zones

Author(s) Mr. Yash Yogesh Potdar, Mr. Ganesh Hari Wani, Mr. Sahil Yashvant Pawar, Ms. Akanksha Premnarayan Singh
Country India
Abstract Unmanned Aerial Vehicles (UAVs) are increasingly being deployed in applications such as surveillance, transportation, disaster response, and smart city infrastructure. Their autonomy relies heavily on two key capabilities: reliable navigation through dynamic and unpredictable environments, and real-time object detection using resource-constrained on-board hardware.
This review paper examines advancements in global and local path planning algorithms, with particular emphasis on A* and Timed Elastic Band (TEB), which together enable both efficient global trajectory generation and adaptive local obstacle avoidance.
In parallel, the paper explores embedded artificial intelligence approaches that leverage lightweight YOLO architectures on edge platforms such as Raspberry Pi, emphasizing optimization techniques including quantization, pruning, and hardware acceleration.
Comparative analysis reveals that hybrid planning strategies significantly improve trajectory efficiency, while quantized and hardware-accelerated YOLO models achieve real-time inference with reduced power consumption. However, challenges persist in thermal management, dataset generalization, and achieving consistent performance across heterogeneous hardware.
By synthesizing the latest research trends, this review highlights the convergence of robust navigation frameworks with optimized lightweight deep learning models as the foundation for next-generation UAVs. These advancements mark a decisive step toward fully
autonomous systems that are efficient, adaptive, and reliable in real-world missions.
Keywords UAV, Path Planning, YOLO, Edge AI, Raspberry Pi, Embedded Systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-18
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60406
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