K-d Tree

Findings:

• Free-configuration Biased Sampling for Motion Planning
• Interactive Design Space Exploration and Optimization for CAD Models
• Optimizations for Sampling-Based Motion Planning Algorithms
• Lecture 9 - Augmentation - MIT opencourseware
• https://en.wikipedia.org/wiki/K-d_tree
• https://www.ri.cmu.edu/pub_files/pub1/moore_andrew_1991_1/moore_andrew_1991_1.pdf
• http://www.montefiore.ulg.ac.be/~poirrier/download/particle/poirrier-kdtree-pp1.pdf
• https://naeimdesigntechnologies.wordpress.com/2016/10/21/old-research-generative-algorithms-in-architectural-space-layout-planning/ (Rhinopythonscripts)

Codes from Matlab:

• https://www.mathworks.com/matlabcentral/fileexchange/26649-kdtree-implementation-in-matlab?s_tid=FX_rc1_behav

Recommended course by MIT:

• https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012/

• https://github.com/youngpong/kdtree-in-python 
• https://tsoding.github.io/