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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.

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  • $\begingroup$ Welcome to Robotics Kandarp Gandhi. It sounds like you already have a method that meets your needs. Is there something about the Wasserstein distance that doesn't work for you? If so, could you please clarify what the issue is and what alternatives you've considered? As it stands, this looks like an opinion poll, which is discouraged on stack exchange. Please edit your question to clarify what other methods you've considered, why those may or may not be suitable, and how they apply to your problem. $\endgroup$ – Chuck May 8 '19 at 14:34
  • $\begingroup$ We prefer practical, answerable questions based on actual problems that you face, so questions which ask for a list of different options are off-topic. Please take a look at How to Ask and tour for more information on how stack exchange works. $\endgroup$ – Chuck May 8 '19 at 14:35

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