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

Ankit Shaw

TL;DR

A modular approach integrates localization, mapping, and trajectory planning to facilitate effective exploration using an OctoMap framework generated from point cloud data, and incorporates obstacle avoidance through potential fields, ensuring safe navigation in dynamic settings.

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.

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

TL;DR

A modular approach integrates localization, mapping, and trajectory planning to facilitate effective exploration using an OctoMap framework generated from point cloud data, and incorporates obstacle avoidance through potential fields, ensuring safe navigation in dynamic settings.

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.

Paper Structure

This paper contains 8 sections, 1 equation, 6 figures.

Table of Contents

  1. Introduction
  2. 2D Exploration Strategies
  3. Nearest Frontier Approach
  4. Cost-Utility Approach
  5. 3D Exploration Strategies
  6. Single Robot Exploration
  7. Multi-Robot Exploration
  8. Conclusion and Future Work

Figures (6)

  • Figure 1: Illustration of the octree data-structure. Left: Example of an octree storing free voxels (shaded white) and occupied voxels (black). Right: The corresponding tree representation (Source: Wurm2012).
  • Figure 2: An unmanned aerial vehicle (UAV) exploring a 3D environment using OctoMap. The colored voxels represent the 3D OctoMap, and the green lines show the UAV’s exploration trajectory (Source: Wang2019).
  • Figure 3: Diagram of the implemented 3D exploration strategy.
  • Figure 4: Execution of the generated trajectory using the OctoMap representation.
  • Figure 5: Ellipse-shaped target points generated from the GPS location of the building center.
  • ...and 1 more figures