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Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor.

Liang Lu¹, Carlos Redondo¹, Pascual Campoy¹

¹Centre for Automation and Robotics (C.A.R.), Computer Vision and Aerial Robotics Group (CVAR), Universidad Politécnica de Madrid (UPM-CSIC), Calle José Gutiérrez Abascal 2, 28006 Madrid, Spain.

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|November 18, 2020

Summary

This summary is machine-generated.

This study introduces an improved autonomous exploration algorithm for aerial robots in search and rescue. The new method enhances pathfinding efficiency and reduces exploration time, validated in simulations and real-world flights.

Keywords:

RGB-D sensor aerial robot autonomous exploration information gain

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Aerial robots offer advantages in search and rescue due to maneuverability.
Autonomous exploration on Unmanned Aerial Vehicles (UAVs) is challenging due to payload and computational limits.

Purpose of the Study:

To present an improved autonomous exploration algorithm for aerial robots in search and rescue operations.
To address the limitations of onboard computing and payload capacity in UAVs.

Main Methods:

Utilized an RGB-D sensor for environmental data acquisition.
Employed OctoMap for environment representation (obstacles, free, unknown spaces).
Applied clustering and information gain-based cost function for frontier selection, with A* path planner and safe corridor generation for path planning.

Main Results:

The proposed algorithm demonstrated a shorter exploration path compared to baseline methods.
The algorithm achieved significant savings in exploration time.
Validated through experiments in diverse environments and real flight tests.

Conclusions:

The developed autonomous exploration algorithm is effective for search and rescue tasks.
The algorithm offers improved efficiency in terms of path length and time.
Successful real-world flight validation confirms its practical applicability.