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Footstep Planning Among Obstacles for Biped Robots

Footstep Planning Among Obstacles for Biped Robots. James Kuffner et al. presented by Jinsung Kwon. Objective. Planning safe navigation strategies for biped robots moving in obstacle-cluttered environments. Biped Navigation Model Assumptions. The environment floor is flat

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Footstep Planning Among Obstacles for Biped Robots

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  1. Footstep Planning Among Obstacles for Biped Robots James Kuffner et al. presented by Jinsung Kwon

  2. Objective Planning safe navigation strategies for biped robots moving in obstacle-cluttered environments.

  3. Biped Navigation ModelAssumptions The environment floor is flat Obstacles are not moving and their positions and heights are known Footstep positions and motions are pre-computed Only the floor surface is allowed for foot placement

  4. Biped Navigation ModelStatically-stable Footstep

  5. Biped Navigation ModelStatically-stable Footstep Select placements along the edge of the reachable region at different relative foot angles Select a few interior placements to move in tight areas A few backward foot placements  15 placements for each foot

  6. Biped Navigation ModelFootstep Transition Trajectory Set of statically-stable motion trajectories for transitioning between footsteps are pre-calculated. 15x14 = 210 trajectories needed for each foot?

  7. Biped Navigation ModelFootstep Transition Trajectory Statically-stable intermediate postures, Qright and Qleft, are introduced to reduce the number of transition trajectories.  15 for each foot Q1 Qright  Q2

  8. Footstep Planning AlgorithmDynamic Programming Forward dynamic programming Greedy heuristic search instead of exhaustive search Priority queue of Search nodes (footprint placement + heuristic cost)

  9. Footstep Planning AlgorithmDynamic Programming • Fail • if No more valid successor nodes • if number of nodes in search tree exceeds pre-defined maximum limit Obstacle Collision

  10. Footstep Planning AlgorithmCost Heuristic Function D(NQ) = depth of NQ in the tree ρ(NQ) = penalty to orientation change or backward step Х(NQ) = min steps to traverse the straight- line distance to the center of the goal region w = weighting values  The heuristics favors straight path with less steps to the goal.

  11. Footstep Planning AlgorithmObstacle Collision-checking Two-level collision checking 1. 2D polygon-polygon intersection test Outline of obstacle projection  Outline of footstep 2. 3D polyhedral minimum distance Check for footstep and trajectories

  12. Footstep Planning AlgorithmObstacle Collision-checking Lazy-evaluation : Insert all successors and perform the minimum distance calculation after a node is removed from the priority queue  Reduce the num of collision check which is very expensive in calculation

  13. Footstep Planning AlgorithmOverview of Planner

  14. Experiments 15 footsteps 20 floor obstacles 6,700 nodes in the search tree  Computed in 12 sec on 800MHz Pentium II wd = 1.0 wp = 0.2 wg = 1.0 determined experimentally

  15. Experiments

  16. Future works 1. Step upon the surface of obstacles 2. Handle environments with uneven terrain 3. Incorporate visual or sensor feedback during planning 4. Investigate different heuristics 5. Running on a real humanoid 6. Include dynamic stepping motions

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