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Value Iteration Networks with Double Estimator for Planetary Rover Path Planning.

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Planetary rover path planning is improved with the novel double value iteration network (dVIN). This algorithm enhances planning in large-scale environments, outperforming existing methods.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Planetary Exploration

Background:

  • Path planning is crucial for planetary rovers in unknown terrains.
  • Value Iteration Network (VIN) is effective but has limitations like sensitivity and poor performance in large domains.

Purpose of the Study:

  • To introduce a novel global path planning algorithm, the double value iteration network (dVIN).
  • To address the limitations of VIN, particularly in large-scale environments and computational cost.

Main Methods:

  • Developed the double value iteration network (dVIN) by decoupling action selection and value estimation.
  • Employed a weighted double estimator method for value approximation.
  • Implemented a two-stage training strategy for VI-based models.

Main Results:

  • The dVIN algorithm demonstrates improved performance over baseline methods.
  • The dVIN shows better generalization capabilities in large-scale planning problems.
  • Evaluated on grid-world and realistic moon landscape datasets.

Conclusions:

  • The dVIN offers a more robust and efficient solution for planetary rover path planning.
  • This approach enhances the autonomy and effectiveness of exploration missions.
  • The proposed training strategy mitigates computational costs and improves performance on large domains.