Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

Abstract Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents
such that every agent reaches its goal and the agents do not collide. In recent years, there …

The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths
for multiple agents, where the key constraint is that the agents will be able to follow these …

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is
important to model subtle human behaviors and navigation rules (eg, passing on the right) …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly
in non-communicating scenarios where each agent's intent (eg goal) is unobservable to the …

Abstract Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their
goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where …

Path planning for mobile robots in large dynamic environments is a challenging problem, as
the robots are required to efficiently reach their given goals while simultaneously avoiding …

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 …

Navigation is a basic skill for autonomous robots. In the last years human–robot interaction
has become an important research field that spans all of the robot capabilities including …