# RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System

arXiv: https://arxiv.org/abs/2209.08533 · HTML: https://mlsanity.com/paper/2209.08533/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2022.

## Built on by (most cited first, 12 of 12)

- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2303.03811/) (2023)
- [AirHunt: Bridging VLM Semantics and Continuous Planning for Efficient Aerial Object Navigation](https://mlsanity.com/paper/2601.12742/) (2026)
- [Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments](https://mlsanity.com/paper/2602.21967/) (2026)
- [Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks](https://mlsanity.com/paper/2407.10382/) (2024)
- [Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models](https://mlsanity.com/paper/2504.15138/) (2025)
- [COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models](https://mlsanity.com/paper/2509.26324/) (2025)
- [Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone](https://mlsanity.com/paper/2501.12073/) (2025)
- [FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations](https://mlsanity.com/paper/2503.00785/) (2025)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion](https://mlsanity.com/paper/2505.22880/) (2025)
- [Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning](https://mlsanity.com/paper/2603.24238/) (2026)

## Abstract (arXiv)

Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.