DART is a very useful toolkit for implementing sophisticated controllers and easily running physics simulations, but a familiarity with control theory and numerical methods is still necessary in order to get sane results. Since the robotics community consists of people from a broad variety of backgrounds, it would be useful for DART to provide some introductory educational tutorials to familiarize

Sample code

Please checkout how the sample test cases in:

• https://github.com/mithi/hexapod-robot-simulator/tree/master/tests

References

Get started with testing python

• https://realpython.com/python-testing/

Write Professional Unit Tests in Python

• https://code.tutsplus.com/tutorials/write-professional-unit-tests-in-python--cms-25835

Web Automation Tests with Selenium

Open dev tools and generate report with lighthouse

• Use video instead of gif
• Use passive scroll listeners

<img width="727" alt="Screen Shot 2020-07-26 at 10 39 36 PM" src="https://user-images.githubusercontent.com/167042

Generated cpp code appears to assign results of multiple solutions to the same "argument" variable, and uses undeclared th_1, th_2, th_3 (etc) variables in solution calculations. The code won't compile, and is unusable.

The generated python code doesn't appear to have these issues.

My robot definition:

    if(name == 'Flexo'):
       # standardize on the order "alpha N-1, a N-1, d N, 

It's really impressive how much pyomeca has progressed! Have you ever considered including some form of simple 3D marker visualization?

Segment mass properties and degrees of freedom of the joint models should be easy be customised, maybe with more options e.g. de Leva, This should be trivial to implement.