The recent advancements in computational geometry and robotics have seen a significant shift towards more efficient and versatile tools for visibility computation and trajectory planning. Innovations in visibility algorithms, such as those leveraging triangular expansion, have led to the development of high-performance libraries that outperform existing solutions in both speed and reliability. These tools are crucial for applications ranging from robotics to video games, where real-time visibility queries are essential. Additionally, hybrid heuristics for sensor placement have been introduced, significantly reducing the number of sensors required for coverage in complex environments while also improving runtime efficiency. This approach is particularly beneficial for large-scale, task-oriented route planning scenarios. Furthermore, advancements in graph search methods for planning on graphs of convex sets have enabled faster and more robust trajectory optimization, with notable improvements in handling complex robotics problems. These methods not only enhance computational efficiency but also open avenues for future enhancements through parallelization and replanning techniques. Lastly, the introduction of Python packages for convex set manipulation has streamlined the analysis and control of dynamical systems, providing a user-friendly interface for set operations and visualization.