papers-we-love_papers-we-love/robotics
Alvaro Garcia 9dacb5e553
Robotics/README (#795)
Update link to "The Dynamic Window Approach to Collision Avoidance"
2024-06-29 12:31:38 -04:00
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README.md Robotics/README (#795) 2024-06-29 12:31:38 -04:00

Robotics

Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow

DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks

The Dynamic Window Approach to Collision Avoidance

Online Trajectory Generation: Basic Concepts for Instantaneous Reactions to Unforeseen Events

Probablistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces

Rapidly-Exploring Random Trees: A New Tool for Path Planning

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

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 strategy to deal with motion planning and created a practical method 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.