Tackling Realistic Complexities in Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics with applications across numerous industries. The problem involves finding collision-free paths to navigate a team of simultaneous movement agents from their start locations to goal locations. While significant theoretical progress has been made in MAPF, translating these models to real-world scenarios remains a challenge. This thesis addresses several key challenges that hinder practical implementation: execution robustness, agent diversity, congestion-aware MAPF, and task assignment.