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.