Paper Outline

Bolded items represent paper sections and heading, grey items are things I'm not sure of

  1. Background and Introduction
  2. Related Work
    1. Rapidly-exploring Random Tree (RRT):
    2. Workspace Skeleton
      • Workspace skeletons are graphs which captures the topological features of the environment.
      • We use both the Reeb Graph and Mean Curvature Skeleton to generate our workspace skeleton. Mainly because, these skeleton are what's used for our skeleton-biased RRT, and because they both perform differently in workspace with different bodies.
      1. Reeb Graph
      2. Mean Curvature Skeleton
    3. Dynamic Region Rapidly-exploring Random Tree (DR-RRT)
      • DR-RRT is a skeleton-guided RRT which uses the workspace skeleton to bias RRT growth.
      • DR-RRT is faster and returns lower collision detection calls when compared to basic RRT
  3. Our Method
    1. Algorithm Overview
      • Detailed explanation of algorithm.
    2. Calculating Clearance-value
  4. Experiments
    1. Robotics Experiments:
      • MazeTunnel:
      • Obstacles:
    2. Protein Experiments?
  5. Discussion [ Experimental Analysis ]
  6. Conclusions
    1. Future Work
      • Compare our method to current methods in Image-guided Medical Needle Steering by running 3D experiments. The medical robotics application
      • Run our method is animation environments and compare with current methods for planning animation movements
  7. Acknowledgments
  8. References