Conventional multiagent path finding (MAPF) problems aim to compute an ensemble of
collision-free paths for multiple agents from their respective starting locations to preallocated …

We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and Delivery (DD-
MAPD), which models the multi-robot shelf rearrangement problem in automated …

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one
needs to find collision-free paths for agents with given start and goal locations. In video …

We present a multi-robot integrated task and motion method capable of handling sequential
subtask dependencies within multiply decomposable tasks. We map the multi-robot …

Trajectory planning for multiple robots in shared environments is a challenging problem
especially when there is limited communication available or no central entity. In this article …

Unmanned aerial vehicles (UAVs) are expected to provide a wide range of services,
whereby UAV fleets will be managed by several independent service providers in shared …

Dynamical systems consisting of a set of autonomous agents face the challenge of having to
accomplish a global task, relying only on local information. While centralized controllers are …

The challenge of finding an optimal solution to a multi-agent path finding (MAPF) problem
has attracted significant academic and industrial interest in recent years. While the problem …

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production
systems in the context of Industry 4.0. This paper investigates an optimization model and a …

Deep reinforcement learning in continuous domains focuses on learning control policies that
map states to distributions over actions that ideally concentrate on the optimal choices in …