Non-Holonomic Motion Planning & Legged Locomotion

Last Time: RRT Configuration generator f(q,u) Build a tree T of configurations Extend:  Sample a configuration q rand from C at random  Pick the node n in T that is closest to q rand  Pick a control u that brings f(n,u) close to q rand  Add f(n,u) as a child of n in T

Last Time: RRT Configuration generator f(q,u) Build a tree T of configurations Extend:  Sample a configuration q rand from C at random  Pick the node n in T that is closest to q rand  Pick a control u that brings f(n,u) close to q rand  Add f(n,u) as a child of n in T Sampling strategy

Weaknesses of RRT’s strategy Depends on the domain from which q rand is sampled Depends on the notion of “closest” A tree that is grown “badly” by accident can greatly slow convergence

Unanswered Questions Probabilistically complete is a weak notion How fast does such a planner converge, and what characteristics of the space does it depend on?

Motion Planning for Legged Robots

Walking/Hiking/Climbing is a problem-solving activity  Each step is unique  Where to make contact?  Which body posture to take?  Which forces to exert?  Decisions at one step may affect the ability to perform future steps

HRP-2, AIST, Japan Humanoid Robots

Lunar Vehicle (ATHLETE, NASA/JPL)

Climbing Robot

Project Midterm Presentations 3/9 and 3/11 10 minute presentation  Describe project goals (be specific)  What milestones have you achieved so far?  Pictures, videos of work in progress  Timeline

IU Robotics Open House Part of National Robotics Week Friday, April 16 th More information forthcoming…

Readings – Legged Locomotion Bretl, Lall, Latombe, and Rock (2004) *Hauser and Latombe (2009)