General Rectilinear Macro

General rectilinear macro placement and navigation involve optimizing the arrangement of rectangular blocks (macros) within a constrained space, whether it's a computer chip or a physical environment. Current research focuses on developing efficient algorithms, often leveraging reinforcement learning or sampling-based methods, to achieve optimal placements that minimize power consumption, improve performance, and reduce design time or pedestrian disruption. These advancements aim to automate complex tasks, improving chip design efficiency and enabling more seamless human-robot interaction in crowded spaces. The resulting improvements in both hardware design and robotics have significant implications for various industries.