Hello robotics stack exchange community, hope my message finds you well during these challenging times. I need an opinion regarding path planning algorithms.
I am looking for a path planning algorithm that is able to produce smooth paths that are shorter and more predictable than RRT and RRT*. It shouldn't get stuck in local minima like Artificial Potential Field.
What I tried
I have set up a Linux 16 system with ROS Kinetic Kame and Python. I implemented Artificial Potential Field Path Planning, RRT and RRT* and ran those on a robot in a small arena. The arena was a square and I placed different objects inside of it that needed to be avoided. See the picture below for an example of how my arena configuration looks like.
Artificial Field Path Planning creates nice, smooth curves and a robot executes smooth movements to follow it. My problem with them is local minima where my robot gets stuck.
RRT produced jagged paths, but the nice thing about it was that it always found a path (albeit it was always a different one). I implemented RRT* to get smoother paths, but they're quite "branchy" looking (still better than RRT though).
I have also looked into other algorithm such as A* and MEA, but rather than trying to implement them straight away, I wanted to ask for your opinion.