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

Neutrosophic set theory provides a new tool to handle the uncertainties in shortest path
problem (SPP). This paper introduces the SPP from a source node to a destination node on …

We describe an attempt to unify search-based and compilation-based approaches to multi-
agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is …

Mutex propagation is a form of efficient constraint propagation popularly used in AI planning
to tightly approximate the reachable states from a given state. We utilize this idea in the …

Multi-agent pathfinding (MAPF) represents a core problem in robotics. In its abstract form,
the task is to navigate agents in an undirected graph to individual goal vertices so that …

We address the problem of multi-goal multi robot path planning (MG-MRPP) via
counterexample guided abstraction refinement (CEGAR) framework. MG-MRPP generalizes …

Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents'
specified initial and goal positions in a shared environment. We focus on compilation-based …

针对关隘地形下多机器人路径规划存在的高度耦合性, 引入调度次序优化思想构造了分层的多
机器人路径求解与优化算法. 首先, 在优化层采用遗传算法生成所有机器人的调度次序 …

Aiming at the high coupling of multi-robot paths in pass terrain, a hierarchical multi-robot
path planning and optimization algorithm was constructed by introducing the idea of …

We address the problem of multiple agents finding their paths from respective sources to
destination nodes in a graph (also called MAPF). Most existing approaches assume that all …