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Non-Holonomic Motion Planning & Legged Locomotion
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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
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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
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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
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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?
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Motion Planning for Legged Robots
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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
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HRP-2, AIST, Japan
Humanoid Robots
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Lunar Vehicle (ATHLETE, NASA/JPL)
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Climbing Robot
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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
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IU Robotics Open House
Part of National Robotics Week Friday, April 16th
More information forthcoming…
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Readings – Legged Locomotion
Bretl, Lall, Latombe, and Rock (2004) *Hauser and Latombe (2009)