1 / 25

Planning for Humanoid Robots

Planning for Humanoid Robots. Presented by Irena Pashchenko CS326a, Winter 2004. What is different?. High Dimensionality ( >30 DOF ) Requires careful control to maintain static and dynamic stability. Two problems ( I ).

gittel
Download Presentation

Planning for Humanoid Robots

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Planning forHumanoid Robots Presented by Irena Pashchenko CS326a, Winter 2004

  2. What is different? • High Dimensionality ( >30 DOF ) • Requires careful control to maintain static and dynamic stability.

  3. Two problems ( I ) • Given two statically-stable configurations, qinit and qgoal, generate a dynamically stable, collision-free trajectory from qinit to qgoal. • Kuffner, et. al, Dynamically-stable Motion Planning for Humanoid Robots, 2000.

  4. Two problems ( II ) • In an obstacle-cluttered environment, walk toward a goal position. • Kuffner, et. al, Footstep Planning Among Obstacles for Biped Robots, IROS 2001

  5. q qtarget qnew qnear qinit Dynamically-Stable Motion Planning for Humanoid Robots, Kuffner, et. al, 2000.

  6. center of mass X(q) gravity vector projection of X(q) area of support Restricted Configuration Space • Cvalid = Cstable Cfree • Cstable - set of statically stable configurations • Givenqinit Cvalid andqgoal Cvalid find find trajectory  s.t.  (t) Cvalid • Justification – statically stable trajectory can be transformed intodynamically-stable by arbitrarily slowing down the motion.

  7. Rendering of an RRT by James Kuffner Random-Exploring Trees (LaValle ’98) • Efficient randomized planner for high-dim. spaces with • Algebraic constraints (obstacles) and • Differential constraints (due to nonholonomy & dynamics) • Biases exploration towards unexplored portions of the space

  8. qinit qgoal Modified RRT • Build two trees from the start and goal configurations • To obey dynamic constraints – introduce dynamic balance compensator in Connect/Extend phase

  9. qinit q qgoal Modified RRT - EXTEND( T, q ) • Randomly select a collision-free, statically stable configuration q.

  10. qinit q qnear qgoal Modified RRT - EXTEND( T, q ) • Select nearest vertex to q already in RRT (qnear).

  11. qtarget qinit  q qnear qgoal Modified RRT - EXTEND( T, q ) • Make a fixed-distance motion towards q from qnear (qtarget).

  12. qtarget qinit  q qnew qnear qgoal Modified RRT - EXTEND( T, q ) • Generate qnew by filtering path from qnear to qtarget through dynamic balance compensator and add it to the tree.

  13. RRT_CONNECT_STABLE( qinit, qgoal) 1. Ta.init( qinit); Tb.init( qgoal); 2. For k= 1 to K do 3. qrand RANDOM_STABLE_CONFIG(); 4. if not( EXTEND(Ta, qrand = Trapped ) 5. if( EXTEND(Tb, qnew) = Reached ) 6. return PATH(Ta, Tb); 7. SWAP(Ta, Tb); 8. return Failure;

  14. Distance Metric • To avoid erratic movements from one step to the next. • Encode in the distance metric: (q,r ) =  wi(qi– ri ) • Higher relative weights to links with greater mass and proximity to trunk.

  15. Random Static Postures • It is unlikely that a configuration picked at random will be collision-free and statically-stable. • Solution: generate a set of configurations, and randomly sample this set at run time.

  16. Experimental Results Dynamically-stable planned trajectory for a crouching motion. Performance statistics ( N = 100 trials ). 3-12 min

  17. Footstep Planning Among Obstacles for Biped Robots, Kuffner, et. al, 2001.

  18. Simplifying Assumptions • Stationary obstacles of known position and height • Foot placement only on floor surface (not on obstacles) • Pre-computed set of footstep placement positions (15)

  19. Simplifying Assumptions • Statically-stable transition trajectories, pre-calculated • Statically-stable intermediate postures  fewer trajectories • Thus, problem is reduced to best-first search of feet placements (collision-free)

  20. Initial Configuration Best-First Search • Generate successor nodes from lowest cost node • Prune nodes that result in collisions • Continue until a generated successor node falls in goal region

  21. Cost Heuristic • Cost of path taken so far • Depth of node in the tree • Penalties for orientation changes and backward steps • Cost estimate to reach goal • Straight-line steps from current to goal • Empirically-determined weighting factors

  22. Obstacle Collision Checking • Two-level: • 2D projections of foot and obstacle on walking surface • 3D polyhedral minimum-distance determination (V-clip)

  23. Experimental Results • Path Computed in 12 seconds on an 800 MHz Pentium II

  24. Future Work • Allow stepping on obstacles • Environments with uneven terrain • Incorporate sensor feedback • Different heuristics • Dynamic stepping motions (including hopping or jumping)

  25. Conclusion • General task involves a high-dimensional space • Use random planning [Kuffner 2000] • Restrict motion to a finite set [Kuffner 2001] • Randomized planner is more general, and could solve [Kuffner 2001] problem, but takes longer time • Could integrate the two planners to form a more efficient, comprehensive planner • Use 2001 planner to move to goal location, and 2000 planner to achieve final posture

More Related