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An algorithm to create model file for Partially Observable Markov Decision Process for mobile robot path planning.

Shripad V Deshpande1, R Harikrishnan1, Jahariah Sampe2

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

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Summary

This study introduces PCMRPP software to manage Partially Observable Markov Decision Process (POMDP) matrix sizes. It offers control over discretization and observation spread for flexible robot path planning solutions.

Keywords:
Mobile robotObstacle avoidancePCMRPPPOMDPPath planningProbabilistic techniqueUncertainty

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Area of Science:

  • Robotics and Artificial Intelligence
  • Decision Theory and Optimization

Background:

  • Partially Observable Markov Decision Processes (POMDPs) are crucial for decision-making under uncertainty but are computationally challenging due to the curse of dimensionality.
  • Existing methods often focus on approximate solutions for large POMDPs, neglecting direct control over matrix size, which complicates manual model creation.

Purpose of the Study:

  • To develop a method for programmatically generating POMDP matrices with controllable dimensions and sparseness.
  • To address the challenges of creating high-dimensional POMDP models for applications like mobile robot path planning.

Main Methods:

  • Implementation of a novel algorithm within the PCMRPP software package.
  • Control over POMDP matrix size through configurable discretization granularity of state components.
  • Control over matrix sparseness via configurable spread of the observation probability distribution.

Main Results:

  • The PCMRPP software enables programmatic generation of POMDP matrices.
  • Users can adjust matrix size and sparseness, offering flexibility in managing computational complexity.
  • Facilitates a trade-off between the time complexity of POMDP solutions and their robustness.

Conclusions:

  • PCMRPP provides a flexible approach to POMDP matrix generation, mitigating the curse of dimensionality.
  • The software enhances the practical application of POMDPs in domains like mobile robot path planning by enabling size control.
  • Offers a novel solution for balancing computational efficiency and solution robustness in uncertain environments.