Recent advances in robotics have enabled researchers and practitioners to deploy large-scale multi-robot systems, where hundreds to thousands of robots operate simultaneously in a shared environment. These systems are increasingly used in applications that require high efficiency and reliability, such as automated warehouses, robotic sorting systems, and autonomous transportation. A fundamental challenge in such systems is to enable many robots to move efficiently while sharing limited space, avoiding congestion, and continually finishing new tasks.

While the community has spent a significant amount of attention on studying how to more efficiently and effectively coordinate robots in multi-robot systems, very few works focus on the environment where the systems are deployed. By environment, we refer to both physical elements, such as spatial arrangement of storage shelves in automated warehouses, and virtual elements, such as traffic rules that influence how robots move and interact. A well-designed environment can significantly reduce congestion, foster implicit coordination among robots, and improve system throughput. In this thesis, we study the problem of Environment Optimization for large-scale multi-robot systems. We identify three main components of the environment that we find optimizable. We formally define each of them as black-box optimization problems and apply evolutionary-based optimizers to solve them.

We first discuss Task Mapping Optimization in the context of robotic sorting systems (RSS), where robots transport packages from induct workstations to eject chutes according to shipping destinations. The destination-to-chute assignment directly shapes traffic demand: poor assignments overload certain regions, create conflicts, and reduce throughput. To fill this gap, we formally define the problem of Task Mapping Optimization (TMO) and propose methods to search for high-quality task mappings that improve throughput by balancing robot traffic.

We then discuss Layout Optimization in the context of automated warehouses, where robots transport packages or inventory pods between locations. In these systems, layout strongly affects robot coordination: narrow passages create bottlenecks and poorly placed shelves induce congestion, even when state-of-the-art planning algorithms are applied. Existing warehouse layouts often follow grid-like structures designed for human accessibility, but such patterns are not necessarily suitable for robots, which do not share human ergonomic constraints. This motivates the question of how a warehouse should be designed when robot coordination is the primary objective. Inspired by scenario generation techniques, we formally define the problem of Layout Optimization (LO) and propose methods to optimize layouts for maximum throughput. We then discuss our first proposed work: Multi-Objective Warehouse Layout Optimization for Throughput and Storage Capacity, which addresses the trade-off between denser storage and efficient robot movement. We search for Pareto-optimal layouts that balance storage capacity and throughput.

Next, we investigate Guidance Graph Optimization in the context of multi-robot coordination in general. When robots are continuously receiving new tasks, requiring them to move indefinitely, existing planning algorithms inevitably make short-horizon decisions. In particular, individually efficient paths may still create long-term congestion. To mitigate this limitation, we introduce global movement guidance that encourages long-term cooperation among robots. We formally define the guidance graph as a representation of such guidance and the problem of Guidance Graph Optimization (GGO), and discuss several methods to optimize guidance graphs for throughput. However, these methods suffer from the curse of dimensionality, making existing GGO methods scale poorly to large graphs. Therefore, we then discuss our second proposed work: Guidance Graph Optimization for Ultra Large Graphs, which scales GGO methods to large graphs.

Finally, we address a common limitation shared by all environment optimization methods: their reliance on simplified simulation. Because realistic simulation is computationally expensive while evolutionary optimization requires many evaluations, directly scaling these methods to realistic settings is difficult. To improve sample efficiency, we discuss our third proposed work: Deep Surrogate Assisted Realistic Environment Optimization, where we investigate data-driven surrogate models that approximate simulator outcomes and enable environment optimization under more realistic conditions.

Together, these contributions demonstrate that optimizing physical layouts, task assignments, and virtual guidance can substantially improve coordination in large-scale multi-robot systems, offering a unified perspective on environment design for scalable robotic operation.