Motion Planning

Motion planning determines how a robot or vehicle can move from a starting state to a goal while avoiding obstacles and respecting limits on dynamics, sensing, and interaction with others. Classical approaches include sampling-based planners such as RRT and PRM, graph search and motion primitives, and trajectory optimization methods such as model predictive control and convex optimization. Learning-based methods, including imitation learning, diffusion models, and learned world models, increasingly propose or refine plans, often alongside geometric checks, reachability analysis, and control barrier functions for safety. A central challenge is balancing speed, solution quality, and formal reliability in uncertain, dynamic, high-dimensional settings, especially when plans must transfer from simulation to physical execution.