# Time-Independent Planning for Multiple Moving Agents

arXiv: https://arxiv.org/abs/2005.13187 · HTML: https://mlsanity.com/paper/2005.13187/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 5 of 5)

- [Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks](https://mlsanity.com/paper/2201.06014/) (2022)
- [Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions](https://mlsanity.com/paper/2412.11678/) (2024)
- [Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations](https://mlsanity.com/paper/2608.05588/) (2026)
- [From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding](https://mlsanity.com/paper/2511.21886/) (2025)
- [Conflict-Based Search for Multi Agent Path Finding with Asynchronous Actions](https://mlsanity.com/paper/2603.18866/) (2026)

## Abstract (arXiv)

Typical Multi-agent Path Finding (MAPF) solvers assume that agents move synchronously, thus neglecting the reality gap in timing assumptions, e.g., delays caused by an imperfect execution of asynchronous moves. So far, two policies enforce a robust execution of MAPF plans taken as input: either by forcing agents to synchronize or by executing plans while preserving temporal dependencies. This paper proposes an alternative approach, called time-independent planning, which is both online and distributed. We represent reality as a transition system that changes configurations according to atomic actions of agents, and use it to generate a time-independent schedule. Empirical results in a simulated environment with stochastic delays of agents' moves support the validity of our proposal.