Enhancing Reinforcement Learning in Sensor Fusion: A Comparative Analysis of Cubature and Sampling-based Integration Methods for Rover Search Planning

Abstract

This study investigates the computational speed and accuracy of two numerical integration methods, cubature and sampling-based, for integrating an integrand over a 2D polygon. Using a group of rovers searching the Martian surface with a limited sensor footprint as a test bed, the relative error and computational time are compared as the area was subdivided to improve accuracy in the sampling-based approach. The results show that the sampling-based approach exhibits a \14.75}% deviation in relative error compared to cubature when it matches the computational performance at \100}%\. Furthermore, achieving a relative error below \1}% necessitates a \10000}% increase in relative time to calculate due to the {}mathcal{O}(N^2) complexity of the sampling-based method. It is concluded that for enhancing reinforcement learning capabilities and other high iteration algorithms, the cubature method is preferred over the sampling-based method.

BibTex

 @misc{ewers_enhancing_2024,
  title = {Enhancing {{Reinforcement Learning}} in {{Sensor Fusion}}: {{A Comparative Analysis}} of {{Cubature}} and {{Sampling-based Integration Methods}} for {{Rover Search Planning}}},
  author = {Ewers, Jan-Hendrik and Sarah, Swinton and Anderson, David and Euan, McGookin and Thomson, Douglas},
  year = {2024},
  month = may,
  number = {arXiv:2405.08691},
  eprint = {2405.08691},
  publisher = {arXiv},
  urldate = {2024-05-15},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  oa = {true}
}