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

**Authors:** Xinhang Xu, Muqing Cao, Shenghai Yuan, Thien Hoang Nguyen, Thien-Minh Nguyen, Lihua Xie  
**arXiv:** [2403.01225](https://arxiv.org/abs/2403.01225)  
**Date:** 2024-03-02  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2403.01225v1/

## Key points

- Authors propose a novel approach for heterogeneous UAV swarm systems for cooperative aerial inspection
- Strategy achieves full 3D surface coverage of objects regardless of shape without relying on precise prior knowledge of the environment
- Agents are partitioned into teams with assigned tasks (mapping, exploration, inspection) and task allocation is optimized using best-first rules
- Voxel map-based representation of the environment facilitates pathfinding while rule-based path-planning method is core of approach
- Proposed strategy outperforms all benchmark methods across multiple evaluation trials and adopted as baseline for Cooperative Aerial Robots Inspection Challenge at IEEE Conference on Decision and Control 2023
- Method openly available as open-source code on GitHub at https://github.com/ntu-aris/caric_baseline

## Summary

In their paper titled "A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System," authors Xinhang Xu, Muqing Cao, Shenghai Yuan, Thien Hoang Nguyen, Thien-Minh Nguyen, and Lihua Xie propose a novel approach for heterogeneous UAV swarm systems aimed at cooperative aerial inspection. The strategy does not rely on precise prior knowledge of the environment and achieves full 3D surface coverage of objects regardless of their shape. The key innovation lies in partitioning agents into teams with assigned tasks such as mapping, exploration, or inspection. Task allocation is optimized using best-first rules to assign optimal inspection volumes to each team. A voxel map-based representation of the environment facilitates pathfinding while a rule-based path-planning method forms the core of this approach. Through rigorous experimentation, the authors demonstrate that their proposed strategy outperforms all benchmark methods for similar tasks across multiple evaluation trials. This success has led to its adoption as the baseline for the Cooperative Aerial Robots Inspection Challenge at the prestigious 62nd IEEE Conference on Decision and Control 2023. Additionally, the proposed method is openly available as open-source code on GitHub at https://github.com/ntu-aris/caric_baseline. This research offers a cost-effective solution that excels in challenging scenarios without requiring detailed prior information about the environment. It makes significant strides in advancing cooperative aerial inspection capabilities using UAV swarm systems.