Search results
Results from the WOW.Com Content Network
The Robotics Toolbox for Python is a reimplementation of the Robotics Toolbox for MATLAB for Python 3. [7] [8] Its functionality is a superset of the Robotics Toolbox for MATLAB, the programming model is similar, and it supports additional methods to define a serial link manipulator including URDF and elementary transform sequences.
Peter Corke FAA (born 24 August 1959) is an Australian roboticist known for his work on Visual Servoing, field robotics, online education, the online Robot Academy and the Robotics Toolbox and Machine Vision Toolbox for MATLAB (matrix laboratory).
rviz [69] (Robot Visualization tool) is a three-dimensional visualizer used to visualize robots, the environments they work in, and sensor data. It is a highly configurable tool, with many different types of visualizations and plugins. Unified Robot Description Format is an XML file format for robot model description.
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
In the context of AI, it is particularly used for embedded systems and robotics. Libraries such as TensorFlow C++, Caffe or Shogun can be used. [1] JavaScript is widely used for web applications and can notably be executed with web browsers. Libraries for AI include TensorFlow.js, Synaptic and Brain.js. [6]
The MATLAB/DIDO toolbox does not require a "guess" to run the algorithm. This and other distinguishing features have made DIDO a popular tool to solve optimal control problems. [4] [7] [15] The MATLAB optimal control toolbox has been used to solve problems in aerospace, [11] robotics [1] and search theory. [2]
Processes in industries like robotics and the aerospace industry typically have strong nonlinear dynamics. In control theory it is sometimes possible to linearize such classes of systems and apply linear techniques, but in many cases it can be necessary to devise from scratch theories permitting control of nonlinear systems.
Visual servoing, also known as vision-based robot control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor (visual feedback [1]) to control the motion of a robot. One of the earliest papers that talks about visual servoing was from the SRI International Labs in 1979.