Non-Holonomic Motion Planning & Legged Locomotion.
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Transcript of Non-Holonomic Motion Planning & Legged Locomotion.
Non-Holonomic Motion Planning & Legged Locomotion
Last Time: RRT
Configuration generator f(q,u) Build a tree T of configurations Extend:
Sample a configuration qrand from C at random
Pick the node n in T that is closest to qrand
Pick a control u that brings f(n,u) close to qrand
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 qrand from C at random
Pick the node n in T that is closest to qrand
Pick a control u that brings f(n,u) close to qrand
Add f(n,u) as a child of n in TSampling strategy
Weaknesses of RRT’s strategy
Depends on the domain from which qrand 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 progressTimeline
IU Robotics Open House
Part of National Robotics Week Friday, April 16th
More information forthcoming…
Readings – Legged Locomotion
Bretl, Lall, Latombe, and Rock (2004) *Hauser and Latombe (2009)