Fido vs. NumPy vs. Python vs. RoboCell Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

RoboCell integrates ScorBase's robotic control software with interactive 3D solid modeling simulation, accurately replicating the dimensions and functions of Intelitek robotic equipment. This integration allows students to teach positions, write programs, and debug robotic applications offline before executing them in an actual work cell. RoboCell enables experimentation with various simulated work cells, even if the physical setups are unavailable in the lab. Advanced users can design 3D objects and import them into RoboCell for use in virtual work cells. The software operates in three modes: Online mode for controlling the robotic cell, Simulation mode for managing the virtual robotic cell in a 3D display, and offline mode for verifying ScorBase programs. Key features include dynamic 3D simulation with tracking of robots and devices, simulation of robot movements and gripper part manipulation, and support for peripheral axes like conveyor belts, XY tables, rotary tables, etc.