Motion planning

as search

MIT 6.832: Underactuated Robotics

Spring 2022, Lecture 19

Follow live at https://slides.com/d/OjU1A6A/live
(or later at https://slides.com/russtedrake/spring22-lec19)

Image credit: Boston Dynamics

http://www.kuffner.org/james/plan

from Choset, Howie M., et al. Principles of robot motion: theory, algorithms, and implementation. MIT press, 2005.

Probabilistic Roadmap (PRM)

Amato, Nancy M., and Yan Wu. "A randomized roadmap method for path and manipulation planning." Proceedings of IEEE international conference on robotics and automation. Vol. 1. IEEE, 1996.

from Choset, Howie M., et al. Principles of robot motion: theory, algorithms, and implementation. MIT press, 2005.

Rapidly-exploring random trees (RRTs)

BUILD_RRT (qinit) {
  T.init(qinit);
  for k = 1 to K do
    qrand = RANDOM_CONFIG();
    EXTEND(T, qrand)
}

Naive Sampling

RRTs have a "Voronoi-bias"

Cost-to-go for the obstacle-free case

Basic RRT

Reachability-Guided RRT

Open Motion Planning Library (OMPL)

Google "drake+ompl" to find some examples (on stackoverflow) of drake integration in C++.  Using the python bindings should work, too.

Lecture 19: Motion planning as search

By russtedrake

Lecture 19: Motion planning as search

MIT Underactuated Robotics Spring 2021 http://underactuated.csail.mit.edu

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