# Multiple Drones driven Hexagonally Partitioned Area Exploration: Simulation and Evaluation

## TL;DR

This paper presents a simulation of a cooperative multi-drone exploration method over a large, unknown, obstacle-filled area using hexagonal partitioning, dynamic mapping, and communication strategies to improve efficiency and resilience.

## Contribution

It introduces a novel multi-drone exploration approach utilizing hexagonal partitioning, dynamic mapping, and weighted RNN for subarea selection, enhancing scalability and fault tolerance.

## Key findings

- Weighted RNN improves exploration efficiency.
- Hexagonal partitioning enhances scalability.
- Method is resilient to communication failures.

## Abstract

In this paper, we simulated a distributed, cooperative path planning technique for multiple drones (~200) to explore an unknown region (~10,000 connected units) in the presence of obstacles. The map of an unknown region is dynamically created based on the information obtained from sensors and other drones. The unknown area is considered a connected region made up of hexagonal unit cells. These cells are grouped to form larger cells called sub-areas. We use long range and short range communication. The short-range communication within drones in smaller proximity helps avoid re-exploration of cells already explored by companion drones located in the same subarea. The long-range communication helps drones identify next subarea to be targeted based on weighted RNN (Reverse nearest neighbor). Simulation results show that weighted RNN in a hexagonal representation makes exploration more efficient, scalable and resilient to communication failures.