E-commerce retailers face the challenge to assemble large numbers of time-critical picking
orders each consisting of just a few order lines with low order quantities. Traditional picker-to …

Robotic handling systems are increasingly applied in distribution centers. They require little
space, provide flexibility in managing varying demand requirements, and are able to work …

Abstract Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their
goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where …

This paper presents an overview of the inaugural Amazon Picking Challenge along with a
summary of a survey conducted among the 26 participating teams. The challenge goal was …

In the last years, Multi-Robot Systems (MRS) have experienced considerable recognition
due to various possible real-world applications. Multi-Robot Task Allocation (MRTA) is …

Exploration in multi-agent reinforcement learning is a challenging problem, especially in
environments with sparse rewards. We propose a general method for efficient exploration by …

Self-assembly enables nature to build complex forms, from multicellular organisms to
complex animal structures such as flocks of birds, through the interaction of vast numbers of …

The unmanned warehouse dispatching system of the 'goods to people'model uses a
structure mainly based on a handling robot, which saves considerable manpower and …

We study optimal multirobot path planning on graphs (MPP) over four minimization
objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance …

The multi-agent path-finding (MAPF) problem has recently received a lot of attention.
However, it does not capture important characteristics of many real-world domains, such as …