Control

Motion Planning

Motion planning is the computation of a collision-free, kinematically feasible path for a robot from a start to a goal configuration through its configuration space. Sampling-based algorithms such as PRM, RRT, and their asymptotically optimal variants dominate high-dimensional manipulator planning, as implemented in OMPL, while optimization-based methods like CHOMP and TrajOpt refine trajectories directly. Planned paths are subsequently time-parameterized for execution.

Why it matters for physical AI

Explicit planners provide the collision-safety guarantees learned policies lack, and hybrid systems increasingly pair them with learned components, using policies for contact-rich segments and planners for free-space transit.