Abstract Multi-Agent Path Finding (MAPF) is the problem of planning collision-free paths for
multiple agents in a shared environment. In this paper, we propose a novel algorithm MAPF …

We consider the problem of routing a large fleet of drones to deliver packages
simultaneously across broad urban areas. Besides flying directly, drones can use public …

In robotic mobile fulfillment systems, human pickers don't go to the inventory area to search
for and pick the ordered items. Instead, robots carry shelves (called “pods”) containing …

This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding
(MAPF). LaCAM is a sub-optimal search-based algorithm that uses lazy successor …

Robotic mobile fulfillment systems is a large dynamic system where information such as
backlog, inventory, pod location, and robot state will change with time during its operation. In …

This paper deals with a new type of warehousing system, Robotic Mobile Fulfillment
Systems (RMFS). In such systems, robots are sent to carry storage units, so-called" pods" …

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 …

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent
pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm …

This paper presents a collection of path planning algorithms for real-time movement of
multiple robots across a Robotic Mobile Fulfillment System (RMFS). Robots are assigned to …

Multi-agent reinforcement learning based methods are significant for online planning of
feasible and safe paths for agents in dynamic and uncertain scenarios. Although some …