Multi-agent pathfinding (MAPF) is a well-studied abstract model for navigation in a multi-
robot system, where every robot finds the path to its goal position without any collision. Due …

We present a distributed model predictive control (DMPC) algorithm to generate trajectories
in real-time for multiple robots. We adopted the on-demand collision avoidance method …

Planning optimal paths for large numbers of robots is computationally expensive. In this
paper, we introduce a new framework for multirobot path planning called subdimensional …

This paper presents a multiagent path planning algorithm based on sequential convex
programming (SCP) that finds locally optimal trajectories. Previous work using SCP …

Motion planning for autonomous surface vehicles (ASVs) is challenging since surface
vessels are nonlinear underactuated kinodynamic systems with often large inertia. Thus …

Electric vertical takeoff and landing vehicles are becoming promising for on-demand air
transportation in urban air mobility (UAM). However, successfully bringing such vehicles and …

In this work, we present Hold Or take Optimal Plan (HOOP), a centralized trajectory
generation algorithm for labeled multi-robot systems operating in obstacle-free, two …

Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting
paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where …

This article proposes two decentralized multiagent optimal control methods that combine the
computational efficiency and scalability of differential dynamic programming (DDP) and the …

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of
agents each with individual start and goal states within a continuous state space. Existing …