Non-Holonomic Motion Planning

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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 T

Sampling strategy
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Weaknesses of RRTs 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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Humanoid Robots
HRP-2, AIST, Japan
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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 progress
• Timeline

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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)