fa51c57a39b157eb94e6846654238e35  wevj-15-00565-v2.pdf
c9066b0bb68611ffdc1ed4541bb85d17aa8cecb7  wevj-15-00565-v2.pdf
e42d041552637d19fa4975c31307a1756b70450100003f78ed623db13638d529  wevj-15-00565-v2.pdf

Title:           Travel Time Estimation for Optimal Planning in Internal Transportation
Subject:         Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the ``closer-to-reality'' (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods.
Keywords:        autonomous mobile robot systems; cost parameter estimation; cost efficiency; Kalman filtering; optimal planning
Author:          Pragna Das and Lluís Ribas-Xirgo
Creator:         LaTeX with hyperref
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ModDate:         Thu Dec 12 06:41:11 2024 CET
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 Date: 2025-02-17 16:57:29 CET
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     Item: 
      Title: Introduction
      Destination: section.1
     Item: 
      Title: Related Works
      Destination: section.2
     Item: 
      Title: Internal Transportation System Prototype
      Destination: section.3
     Item: 
      Title: Static Estimation
      Destination: section.4
      Children: 
       Item: 
        Title: Formulation
        Destination: subsection.4.1
       Item: 
        Title: Procedure
        Destination: subsection.4.2
       Item: 
        Title: Path Planning
        Destination: subsection.4.3
       Item: 
        Title: Results
        Destination: subsection.4.4
     Item: 
      Title: Dynamic Estimation
      Destination: section.5
      Children: 
       Item: 
        Title: Formulation
        Destination: subsection.5.1
       Item: 
        Title: Procedure
        Destination: subsection.5.2
       Item: 
        Title: Results
        Destination: subsection.5.3
        Children: 
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          Title: Path Comparison on Plain Surface
          Destination: subsubsection.5.3.1
         Item: 
          Title: Path Comparison with Changed Surface Conditions
          Destination: subsubsection.5.3.2
     Item: 
      Title: Discussion and Conclusions
      Destination: section.6
     Item: 
      Title: References
      Destination: section.7
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    Title: Travel Time Estimation for Optimal Planning in Internal Transportation
    Author: Pragna Das and Lluís Ribas-Xirgo
    Subject: Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the ``closer-to-reality'' (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods.
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  Checksum: 84afec79
   Type: CRC32
  Checksum: fa51c57a39b157eb94e6846654238e35
   Type: MD5
  Checksum: c9066b0bb68611ffdc1ed4541bb85d17aa8cecb7
   Type: SHA-1
  Checksum: e42d041552637d19fa4975c31307a1756b70450100003f78ed623db13638d529
   Type: SHA-256