I have not used real robotic simulations, but I have used RViz and ROS for testing my own path-finding algorithms. Performance on my Late 2015 MacBook Pro was stuttery (about 15-20 fps), but still good enough to be somewhat usable.

Overall, I had more issues with finding good resources on the rospy module than anything else. It seems almost all robotics development with ROS is done using C++ instead of Python (in my little experience), so there is little I could find in terms of code samples that would allow to get past a project like this (https://github.com/gandalf15/CS3027--CS5059--Robotics-Univer...).

I felt the same way during my lectures. I wanted to use Python because I had already implemented the fuzzy functions I needed for obstacle avoidance and it is overall simpler, but most people on class used C++ anyway.

FWIW, after realizing I never directly answered your question, I figured I should give you a full response.

Rospy aside, writing and implementing your own SLAM/path-planning tools in ROS is quite simple once you get familiar with the architecture of ROS. All the sensor data can be collected by subscribing to the right channel, and then you can return driving instructions back to a controller by publishing them on another channel. Since all this channel business operates on the network layer, you can even offload the computational workload of the algorithms to a remote system and have the robot get its moving instructions over Wi-Fi.

It highly depends on what exactly you want to integrate on which robot. Stuff like control algorithms can be dependent on the type of robot used. Also, a lot of this stuff is already present in ROS as a downloadable package.

I confess I only worked on small projects and all the movement/trajectory/obstacle avoidance functions were implemented manually, so this didn't cross my mind.

It really makes sense to rely on the ROS packages, I will look what is available, thank you.