# Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

arXiv: https://arxiv.org/abs/2208.02993 · HTML: https://mlsanity.com/paper/2208.02993/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.06343/) (2020)
- [On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2111.05992/) (2021)
- [MSTC*:Multi-robot Coverage Path Planning under Physical Constraints](https://mlsanity.com/paper/2108.04632/) (2021)
- [Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas](https://mlsanity.com/paper/2103.14709/) (2021)
- [Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations](https://mlsanity.com/paper/1704.00079/) (2017)
- [Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations](https://mlsanity.com/paper/1908.05727/) (2019)

## Built on by (most cited first, 3 of 3)

- [Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction](https://mlsanity.com/paper/2411.01707/) (2024)
- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We aim to solve the mCPP problem for the worker-station MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station. Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for worker-station MRS in metric of task finish time.