Graph-based multi-robot path planning (MRPP) is NP-hard to optimally solve. In this work,
we propose the first low polynomial-time algorithm for MRPP achieving 1--1.5 asymptotic …

We study the problem of sequential task assignment and collision-free routing for large
teams of robots in applications with inter-task precedence constraints (eg, task A and task B …

We study how to use guidance to improve the throughput of lifelong Multi-Agent Path
Finding (MAPF). Previous studies have demonstrated that while incorporating guidance …

Conventional multi-agent path planners typically compute an ensemble of paths while
optimizing a single objective, such as path length. However, many applications may require …

We present a novel reinforcement learning based algorithm for multi-robot task allocation
problem in ware-house environments. We formulate it as a Markov Decision Process and …

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs)
requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In …

Multi-agent pickup and delivery (MAPD) is crucial in intelligent storage systems (ISSs),
where multiple automated guided vehicles (AGVs) are assigned to various and potentially …

The authors present an overview of a hierarchical framework for coordinating task-and
motion-level operations in multirobot systems. Their framework is based on the idea of using …

We focus on the Multi-Robot pickup and delivery problem for logistic scenarios that recently
received significant attention from the research community. In particular we consider an …

We introduce the asprilo1 framework to facilitate experimental studies of approaches
addressing complex dynamic applications. For this purpose, we have chosen the domain of …