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Article overview
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Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots | | Yang Xu
; Ronghao Zheng
; Senlin Zhang
; Meiqin Liu
; | | Date: |
2 Jan 2023 | | Abstract: | This paper concerns realizing highly efficient information-theoretic robot
exploration with desired performance in complex scenes. We build a continuous
lightweight inference model to predict the mutual information (MI) and the
associated prediction confidence of the robot’s candidate actions which have
not been evaluated explicitly. This allows the decision-making stage in robot
exploration to run with a logarithmic complexity approximately, this will also
benefit online exploration in large unstructured, and cluttered places that
need more spatial samples to assess and decide. We also develop an objective
function to balance the local optimal action with the highest MI value and the
global choice with high prediction variance. Extensive numerical and dataset
simulations show the desired efficiency of our proposed method without losing
exploration performance in different environments. We also provide our
open-source implementation codes released on GitHub for the robot community. | | Source: | arXiv, 2301.00523 | | Services: | Forum | Review | PDF | Favorites | |
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