In sample-based motion planning, sampling methods would change the cost of path and computation time for the same planning algorithm. I would like to compare different sampling methods.
So, the different stochastic process generates sample points. The task is to identify which stochastic process/distribution is the closest to the target value.
In literature, I am not able to find a comparison method which is standard. If any standard method is available, then help me.
Wasserstein distance between the target value(smooth path cost, time_min = 0.0) and the vector of sample results would be used or just cost should be considered.
Kindly give your suggestions.