Autonomous Multi-Robot Exploration Strategies for 3D Environments with Fire Detection Capabilitie

TL;DR

This paper reviews multi-robot exploration strategies in 3D environments with a focus on fire detection, highlighting limitations of traditional methods and proposing a modular approach with future research directions.

Contribution

It introduces a modular exploration framework integrating localization, mapping, and planning for multi-robot systems in 3D environments with fire detection capabilities.

Findings

Effective exploration using OctoMap from point cloud data

Obstacle avoidance via potential fields in dynamic settings

Discussion of decentralized mapping and UAV coordination

Abstract

This paper presents a comprehensive overview of exploration strategies utilized in both 2D and 3D environments, focusing on autonomous multi-robot systems designed for building exploration and fire detection. We explore the limitations of traditional algorithms that rely on prior knowledge and predefined maps, emphasizing the challenges faced when environments undergo changes that invalidate these maps. Our modular approach integrates localization, mapping, and trajectory planning to facilitate effective exploration using an OctoMap framework generated from point cloud data. The exploration strategy incorporates obstacle avoidance through potential fields, ensuring safe navigation in dynamic settings. Additionally, I propose future research directions, including decentralized map creation, coordinated exploration among unmanned aerial vehicles (UAVs), and adaptations to time-varying environments. This work serves as a foundation for advancing coordinated multi-robot exploration algorithms, enhancing their applicability in real-world scenarios.