Analysis using a Model-Based Approach of Path Planning for Multiple Uavs in the Context of Surveying Building Damage Following a Disaster
Battula Karthikeya1, Gangisetti sathwik1, Aluru Shashidhar Reddy1, Suraj Tumuluri1, Talluri V Lakshmi Bhavani Lalith1, Boyapati Jnana Venkata Subhash1, Nettem Nikhil Chowdary2.
1 School of Computer Science and Engineering, VIT-AP University, Inavolu,
Andhra Pradesh, India.
2 Department of Computer Science and Engineering, Amrita Vishwa
Vidyapeetham, Amritapuri,India.
Abstract: In the wake of disasters, efficient and timely assessment of building damage is of paramount importance for effective disaster response and recovery efforts. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as indispensable tools for postdisaster surveying due to their ability to rapidly cover large areas and provide high-resolution imagery. The effective utilization of multiple UAVs in such scenarios necessitates optimal path planning to maximize coverage, minimize mission time, and ensure safe navigation. This paper presents a comprehensive model-based analysis of multi-UAV path planning strategies tailored for surveying building damage in postdisaster environments. The study commences by emphasizing the critical role of UAVs in disaster management, especially in the context of assessing structural damage. Acknowledging the challenges posed by complex urban landscapes and the need for swift response, the research focuses on the development and evaluation of path planning strategies that leverage advanced modeling techniques. The proposed model integrates spatial data, building blueprints, and environmental information to generate a realistic representation of the disaster-affected area, facilitating informed decision-making for path planning. A range of path planning approaches is examined within the model-based framework. These include grid-based methods, which discretize the area into cells and deploy UAVs to cover designated regions, and graph-based approaches such as Rapidly-exploring Random Trees (RRT), which generate paths through probabilistic exploration. The paper also delves into machine learning-enhanced strategies, leveraging reinforcement learning algorithms to adapt UAV paths based on real-time observations and evolving disaster dynamics. Furthermore, the study underscores the scalability of the model-based approach, demonstrating its applicability to scenarios involving a varying number of UAVs. Through comprehensive experimentation, the paper provides insights into the optimal deployment of UAV teams, shedding light on the balance between increased coverage and potential communication overhead.
