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Legged Locomotion Planning

Legged Locomotion Planning. Kang Zhao B659 Intelligent Robotics Spring 2013. Planning Biped Navigation Strategies in Complex Environments Joel Chestnutt , James Kuffner , Koichi Nishiwaki , Satoshi Kagami. Global terrain map M Goal Primitive set {Trans} Search algorithm.

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Legged Locomotion Planning

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  1. Legged Locomotion Planning Kang Zhao B659 Intelligent Robotics Spring 2013

  2. Planning Biped Navigation Strategies in Complex Environments • Joel Chestnutt, James Kuffner, Koichi Nishiwaki, Satoshi Kagami

  3. Global terrain map M • Goal • Primitive set {Trans} • Search algorithm

  4. Algorithm - Biped Robot Model • State: • θ: position and orientation relative to {U} • One-step motion destination

  5. Algorithm- State transitions • Footstep transition 1 … 4 2 3 0 5 6 Branching factor 7 A 16-transitions set

  6. Algorithm- Environment • Terrain map

  7. Algorithm- State Evaluation Location metric to evaluate a location’s cost Slope angle Roughness Stability Largest bump Safety

  8. The slope angle of the surface at the candidate location. Perfectly horizontal surfaces are desired. The slope angle is computed by fitting a plane h(x, y) to the cells in the location. Slope angle Roughness Stability Largest bump It’s purpose is to take into account the possible inaccuracy of foot positioning. This can be computed using the roughness and largest bump metrics, using the cells around the foot location Safety

  9. Algorithm- State Evaluation Step metric to evaluate cost of taking a step • Penalty for height change • Collision check Cost of transition

  10. Algorithm- State Evaluation The heuristic functionestimates the cost to go from to a goal state Heuristic metric to evaluate remaining cost Euclidean distance Relative angle Height difference Its value is independent of the current search tree; it depends only on and the goal

  11. Best First Search • It exploits state description to estimate how “good” each search node is • An evaluation function maps each node of the search tree to a real number • Greedy BFS

  12. A* Search

  13. Searching the State Space A schematic view Search tree

  14. Searching the State Space A schematic view Search tree

  15. Searching the State Space A schematic view Search tree

  16. Searching the State Space A schematic view Search tree

  17. Searching the State Space A schematic view Search tree

  18. Searching the State Space A schematic view Search tree

  19. Results • Cluttered terrain

  20. Results • Multi-level terrain

  21. Results • Uneven ground with obstacles

  22. Comparisons • Distance to goal • Transitions and obstacle effects • Metric weights

  23. BFS A 26-transitions set A 40-transitions set

  24. Performance comparison of A* and BFS for increasing numbers of stairs along the path

  25. Local-minimum problem

  26. Online Experiments Stereo vision system Walking area map Planner Footstep sequence Trajectory generator

  27. Following work • A tired planning Strategy for biped navigation, 2004 • Biped navigation in rough environments using on-board sensing, 2009

  28. Multi-Step Motion Planning for Free-climbing Robots • Tim Bretl, Sanjay Lall, Jean-Claude Latombe, Stephen Rock

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