diff --git a/README.md b/README.md old mode 100755 new mode 100644 diff --git a/robotics/README.md b/robotics/README.md new file mode 100644 index 0000000..f6298b7 --- /dev/null +++ b/robotics/README.md @@ -0,0 +1,44 @@ +Robotics +==== + +[Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow](http://www.roboticsproceedings.org/rss01/p36.pdf) + +[DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks](http://people.ee.duke.edu/~lcarin/Lihan4.21.06a.pdf) + +[The Dynamic Window Approach to Collision Avoidance](http://www.cs.washington.edu/node/4749) + +[Online Trajectory Generation: Basic Concepts for Instantaneous Reactions to Unforeseen Events](http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5350749) + +[Probablistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces](http://www.kavrakilab.org/sites/default/files/kavraki1996prm-high-dim-conf.pdf) + +[Rapidly-Exploring Random Trees: A New Tool for Path Planning](http://msl.cs.uiuc.edu/~lavalle/papers/Lav98c.pdf) + +[RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments](http://www.cs.washington.edu/robotics/postscripts/3d-mapping-iser-10-final.pdf) + + + +Reasoning for the new papers: + +The dynamic window approach to collision avoidance is an influential +paper for mobile robots. The method is based on a robot's dynamics +rather than higher-level representations of a robot and/or obstacles in +an environment. + +The PRM and RRT algorithms are two seminal papers in robot motion +planning. The problem of motion planning scales exponentially with the +degrees of freedom a robot has and the degrees of freedom the obstacles +in an environment have. Thus, planning with high degrees of freedom leads to many problems +such as incompleteness and extremely slow speed. The PRM method was the first to +propose a sampling-based stratey to deal with motion planning and +created a practical methed for offline planning of robot manipulators. +The RRT method modified PRM by using a tree structure rather than a +graph so that non-holonomic and other constraints could be considered +when planning. + +The Instantaneous Trajectory Generation method is relatively new, but +very important. It allows for extremely fast trajectory generation for +robots of high degrees of freedom (motion states generated within 1 +millisecond). It has been used to implement robot sword fighting and +other activities that require fast reaction-based planning. The author +started a business based simply on the work and has shown the +algorithm's success in many robot applications. diff --git a/robotics/adaptive-road-following-using-self-supervised-learning-and-reverse-optical-flow.pdf b/robotics/adaptive-road-following-using-self-supervised-learning-and-reverse-optical-flow.pdf deleted file mode 100644 index 29d33cc..0000000 Binary files a/robotics/adaptive-road-following-using-self-supervised-learning-and-reverse-optical-flow.pdf and /dev/null differ diff --git a/robotics/dp-slam-fast-robust-simultaneous-localization-and-mapping-without-predetermined-landmarks.pdf b/robotics/dp-slam-fast-robust-simultaneous-localization-and-mapping-without-predetermined-landmarks.pdf deleted file mode 100644 index de48907..0000000 Binary files a/robotics/dp-slam-fast-robust-simultaneous-localization-and-mapping-without-predetermined-landmarks.pdf and /dev/null differ diff --git a/robotics/rgb-d-mapping-using-depth-cameras-for-dense-3d-modeling-of-indoor-environments.pdf b/robotics/rgb-d-mapping-using-depth-cameras-for-dense-3d-modeling-of-indoor-environments.pdf deleted file mode 100644 index d97f6a2..0000000 Binary files a/robotics/rgb-d-mapping-using-depth-cameras-for-dense-3d-modeling-of-indoor-environments.pdf and /dev/null differ