Abstract Multi-Agent Path Finding has been widely studied in the past few years due to its
broad application in the field of robotics and AI. However, previous solvers rely on several …

Multi-agent path-finding (MAPF) is the problem of finding a plan for moving a set of agents
from their initial locations to their goals without collisions. Following this plan, however, may …

Abstract Multi-Agent Path Finding (MAPF) is the planning problem of finding collision-free
paths for a team of agents. We focus on Conflict-Based Search (CBS), a two-level tree …

The development of a real-world Unmanned Aircraft System (UAS) Traffic Management
(UTM) system to ensure the safe integration of Unmanned Aerial Vehicles (UAVs) in low …

Abstract Purpose of Review Planning collision-free paths for multiple robots is important for
real-world multi-robot systems and has been studied as an optimization problem on graphs …

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical
computer science and operations research. We discuss issues that arise when generalizing …

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world
problems, eg, automation warehouses. The learning-based, fully decentralized framework …

Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale
autonomous robot networks, are widely used in logistic and industrial applications. In this …

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic
systems, where agents must plan their collision-free paths to their given goal positions …

High-tech giants and start-ups are investing in drone technologies to provide urban air
delivery service, which is expected to solve the last-mile problem and mitigate road traffic …