Exploring the widespread applications of unmanned aerial vehicles (UAVs) in Internet of
Things has become a current research hotspot. In some tasks related to UAV-based …

Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the
goal is to find a set of tours, one for each agent, to collectively visit all the cities while …

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple
agents from their start to goal locations, while visiting a set of intermediate target locations in …

The Combined Target-Assignment and Path-Finding (TAPF) problem requires
simultaneously assigning targets to agents and planning collision-free paths for them from …

Abstract Multi-Agent Path Finding (MAPF), ie, finding collision-free paths for multiple robots,
plays a critical role in many applications. Sometimes, assigning a target to each agent also …

Given a set of agents and a set of pickup-delivery requests located on a two-dimensional
grid map, the multi-agent pickup and delivery problem assigns the requests to the agents …

Multi-Agent Path Finding () seeks collision-free paths for multiple agents from start to goal
locations. This paper considers a generalization of called Multi-Agent Combinatorial Path …

This paper considers a generalization of the Path Finding (PF) problem with refuelling
constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where …

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple
robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where …