A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System

TL;DR

This paper introduces a cost-effective, environment-agnostic cooperative exploration and inspection strategy for heterogeneous UAV swarms, enabling full 3D coverage without prior environment knowledge, outperforming benchmarks.

Contribution

The paper presents a novel rule-based path planning and task allocation method for heterogeneous UAV swarms that does not require prior environment data.

Findings

Surpassed all benchmark methods in experiments

Achieved full 3D surface coverage of objects of any shape

Demonstrated effectiveness in challenging scenarios

Abstract

In this paper, we propose a cost-effective strategy for heterogeneous UAV swarm systems for cooperative aerial inspection. Unlike previous swarm inspection works, the proposed method does not rely on precise prior knowledge of the environment and can complete full 3D surface coverage of objects in any shape. In this work, agents are partitioned into teams, with each drone assign a different task, including mapping, exploration, and inspection. Task allocation is facilitated by assigning optimal inspection volumes to each team, following best-first rules. A voxel map-based representation of the environment is used for pathfinding, and a rule-based path-planning method is the core of this approach. We achieved the best performance in all challenging experiments with the proposed approach, surpassing all benchmark methods for similar tasks across multiple evaluation trials. The proposed method is open source at https://github.com/ntu-aris/caric_baseline and used as the baseline of the Cooperative Aerial Robots Inspection Challenge at the 62nd IEEE Conference on Decision and Control 2023.