Optimization Based Planner

Optimization-based planners are algorithms designed to generate efficient and safe trajectories for robots and autonomous systems navigating complex environments. Current research emphasizes improving computational efficiency, often through hybrid approaches combining optimization with learning-based methods like imitation learning or metaheuristics such as GRASP, and incorporating advanced constraint handling techniques like control barrier functions and B-spline parameterization. These advancements are crucial for enabling real-time decision-making in applications ranging from autonomous driving and UAV search-and-rescue to multi-robot coordination and agile satellite operations, ultimately improving the performance and reliability of these systems.