# Alternating Target-Path Planning for Scalable Multi-Agent Coordination

arXiv: https://arxiv.org/abs/2605.07744 · HTML: https://mlsanity.com/paper/2605.07744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 8 of 8)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [Optimal Target Assignment and Path Finding for Teams of Agents](https://mlsanity.com/paper/1612.05693/) (2016)
- [Iterative Refinement for Real-Time Multi-Robot Path Planning](https://mlsanity.com/paper/2102.12331/) (2021)
- [Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution](https://mlsanity.com/paper/2109.04264/) (2021)
- [Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree](https://mlsanity.com/paper/2307.00663/) (2023)
- [LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control](https://mlsanity.com/paper/2507.11464/) (2025)
- [ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem](https://mlsanity.com/paper/2404.05223/) (2024)

## Abstract (arXiv)

The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to agents. Prior work on TAPF has relied exclusively on Conflict-Based Search (CBS), which tightly couples target assignment and pathfinding, resulting in compute-intensive, non-scalable solutions. In contrast, we propose an iterative refinement framework that decouples target assignment from pathfinding. Our framework builds on modern, fast, suboptimal MAPF solvers, such as LaCAM. Specifically, within a given time budget, it repeatedly solves MAPF for the current target assignment, identifies bottleneck agents via MAPF feedback, and refines the assignment. Empirical results show that feedback-driven reassignment loop is effective, enabling our framework to scale well beyond the reach of the state-of-the-art CBS-based solver while maintaining decent solution quality. This represents a solid step toward practical, large scale TAPF suitable for real-world setups.