Navigation & SLAM

Path Planning

Path planning is the problem of computing a collision-free geometric path from a start to a goal configuration through a robot's configuration space. Classical approaches include grid-based graph search such as A*, sampling-based planners such as probabilistic roadmaps and rapidly-exploring random trees, and optimization-based methods. It is distinguished from trajectory planning, which additionally assigns timing, velocities, and dynamic feasibility to the path.

Why it matters for physical AI

Even as end-to-end learned policies advance, explicit planners remain the backbone of mobile robot navigation and manipulator motion generation, and hybrid systems increasingly use learned models to guide or warm-start classical planners.