Multi-Agent Path Finding with Deadlines

06/11/2018
by   Hang Ma, et al.
4

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/13/2018

Multi-Agent Path Finding with Deadlines: Preliminary Results

We formalize the problem of multi-agent path finding with deadlines (MAP...
research
08/02/2022

Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

We formalize and study the multi-goal task assignment and path finding (...
research
07/24/2019

An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

We consider multi-agent transport task problems where, e.g. in a factory...
research
06/10/2019

Automatic Algorithm Selection In Multi-agent Pathfinding

In a multi-agent pathfinding (MAPF) problem, agents need to navigate fro...
research
02/24/2021

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

Solving the Multi-Agent Path Finding (MAPF) problem optimally is known t...
research
06/08/2017

Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in ar...
research
08/02/2023

Optimal Sensor Deception to Deviate from an Allowed Itinerary

In this work, we study a class of deception planning problems in which a...

Please sign up or login with your details

Forgot password? Click here to reset