Constrained Motion Planning

Constrained motion planning focuses on finding collision-free paths for robots or other systems while adhering to various constraints, such as maintaining a specific orientation, avoiding obstacles, or respecting physical limitations. Current research emphasizes efficient algorithms, including those based on hierarchical quadratic programming, physics-informed neural networks, and transformers, to overcome the computational challenges posed by high-dimensional constraint manifolds. These advancements are crucial for enabling autonomous operation in complex environments, with applications ranging from minimally invasive surgery and agile flight control to automated laboratory procedures and collaborative robotics.