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Thursday, 13 December 2018

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/




at December 13, 2018
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Labels: Blog, K-d Tree

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