This paper focuses on data fusion, which is fundamental to one of the most important
modules in any autonomous system: perception. Over the past decade, there has been a …

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 describe a method for multirobot trajectory planning in known, obstacle-rich
environments. We demonstrate our approach on a quadrotor swarm navigating in a …

In the multi-agent pathfinding problem (MAPF) we are given a set of agents each with
respective start and goal positions. The task is to find paths for all agents while avoiding …

We study optimal multirobot path planning on graphs (MPP) over four minimization
objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance …

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents,
each with a different start and goal position, such that agents do not collide. A successful …

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 …

Abstract Conflict-Based Search (CBS) and its generalization, Meta-Agent CBS are amongst
the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper …

In autonomous multirobot systems one of the concerns is how to prevent collisions between
the individual robots. One approach to this problem involves finding coordinated trajectories …

Abstract Conflict-Based Search (CBS) and its enhancements are among the strongest
algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to …