# Priority-Aware Multi-Robot Coverage Path Planning

arXiv: https://arxiv.org/abs/2601.00580 · HTML: https://mlsanity.com/paper/2601.00580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner](https://mlsanity.com/paper/2409.20560/) (2024)
- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [CMP: Cooperative Motion Prediction with Multi-Agent Communication](https://mlsanity.com/paper/2403.17916/) (2024)
- [MSTC*:Multi-robot Coverage Path Planning under Physical Constraints](https://mlsanity.com/paper/2108.04632/) (2021)
- [CoMamba: Real-time Cooperative Perception Unlocked with State Space Models](https://mlsanity.com/paper/2409.10699/) (2024)
- [Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm](https://mlsanity.com/paper/2503.03796/) (2025)
- [Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment](https://mlsanity.com/paper/2201.10150/) (2022)
- [Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction](https://mlsanity.com/paper/2411.01707/) (2024)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction](https://mlsanity.com/paper/1908.09236/) (2019)
- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Multi-Robot Persistent Monitoring: Minimizing Latency and Number of Robots with Recharging Constraints](https://mlsanity.com/paper/2303.08935/) (2023)
- [Online Concurrent Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2403.10460/) (2024)
- [Near-Optimal Coverage Path Planning with Turn Costs](https://mlsanity.com/paper/2310.20340/) (2023)

## Built on by (most cited first, 2 of 2)

- [Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.23179/) (2026)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by generating non-overlapping paths for full-area coverage. However, most existing methods assume uniform importance across regions, limiting their effectiveness in scenarios where some zones require faster attention. We introduce the Priority-Aware MCPP (PA-MCPP) problem, where a subset of the environment is designated as prioritized zones with associated weights. The goal is to minimize, in lexicographic order, the total priority-weighted latency of zone coverage and the overall makespan. To address this, we propose a scalable two-phase framework combining (1) greedy zone assignment with local search, spanning-tree-based path planning, and (2) Steiner-tree-guided residual coverage. Experiments across diverse scenarios demonstrate that our method significantly reduces priority-weighted latency compared to standard MCPP baselines, while maintaining competitive makespan. Sensitivity analyses further show that the method scales well with the number of robots and that zone coverage behavior can be effectively controlled by adjusting priority weights.