Cooperative Indoor Exploration Leveraging a Mixed-Size UAV Team with Heterogeneous Sensors

TL;DR

This paper introduces a novel cooperative indoor exploration method using a heterogeneous UAV team with different sizes and sensors, employing frontier-based exploration and task allocation strategies validated through simulations and real-world tests.

Contribution

It presents a new approach combining frontier-based exploration with task allocation strategies and the SphereMap algorithm for heterogeneous UAV teams in indoor environments.

Findings

Effective exploration of unknown indoor spaces demonstrated.

Improved path planning with obstacle and collision avoidance.

Validation through both simulation and real-world experiments.

Abstract

Heterogeneous teams of Unmanned Aerial Vehicles (UAVs) can enhance the exploration capabilities of aerial robots by exploiting different strengths and abilities of varying UAVs. This paper presents a novel method for exploring unknown indoor spaces with a team of UAVs of different sizes and sensory equipment. We propose a frontier-based exploration with two task allocation strategies: a greedy strategy that assigns Points of Interest (POIs) based on Euclidean distance and UAV priority and an optimization strategy that solves a minimum-cost flow problem. The proposed method utilizes the SphereMap algorithm to assess the accessibility of the POIs and generate paths that account for obstacle distances, including collision avoidance maneuvers among UAVs. The proposed approach was validated through simulation testing and real-world experiments that evaluated the method's performance on board the UAVs.