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