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

Shripad V Deshpande, R Harikrishnan, Jahariah Sampe, Abhimanyu Patwa

Abstract read

In one paragraph

Article in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Design a path - planning strategy for mobile robot in multi-structured environment based on distributional reinforcement learning.MethodsX · 2025
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5 · Who and what money

Authors and funding

4 authors.

Shripad V DeshpandeSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

R HarikrishnanSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

Jahariah SampeInstitute of Microengineering and Nanoelectronics, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

Abhimanyu PatwaSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality. There are various methods that can handle huge sizes of POMDP matrices to create approximate solutions, but no serious effort has been reported to effectively control the size of the POMDP matrices. Manually creating the high-dimension matrices of a POMDP model is a cumbersome and sometimes even impossible task. The PCMRPP (POMDP file Creator for Mobile Robot Path Planning) software package implements a novel algorithm to programmatically generate these matrices such that: •The sizes of the matrices can be controlled by configuring the granularity of discretization of the components of the state and•The sparseness of the matrices can be controlled by configuring the spread of the observation probability distribution. This kind of flexibility allows one to achieve a trade-off between time complexity and the level of robustness of the POMDP solution.

Indexed as

Mobile robotObstacle avoidancePath planningPCMRPPPOMDPProbabilistic techniqueUncertainty

Identifiers

PMID38299041

DOI10.1016/j.mex.2024.102552

PMCPMC10828799

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.