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Performance Characterization of a Point‐Cloud‐Based Path Planner in Off‐Road Terrain.

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Title: Performance Characterization of a Point‐Cloud‐Based Path Planner in Off‐Road Terrain.
Authors: Majhor, Casey D.1 (AUTHOR) cmajhor@mtu.edu, Bos, Jeremy P.1 (AUTHOR)
Source: Journal of Field Robotics. Mar2026, Vol. 43 Issue 2, p695-716. 22p.
Subjects: Robotic path planning, Digital computer simulation, Computer performance, Relief models, Mathematical optimization
Abstract: We present a comprehensive evaluation of a point‐cloud‐based navigation stack, MUONS, for autonomous off‐road navigation. Performance is characterized by analyzing the results of 30,000 planning and navigation trials in simulation and validated through field testing. Our simulation campaign considers three kinematically challenging terrain maps and twenty combinations of seven path‐planning parameters. In simulation, our MUONS‐equipped AGV achieved a 0.98 success rate and experienced no failures in the field. By statistical and correlation analysis, we determined that the Bi‐RRT expansion radius used in the initial planning stages is most correlated with performance in terms of planning time and traversed path length. Finally, we observed that the proportional variation due to changes in the tuning parameters is remarkably well correlated to performance in field testing. This finding supports the use of Monte‐Carlo simulation campaigns for performance assessment and parameter tuning. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Performance Characterization of a Point‐Cloud‐Based Path Planner in Off‐Road Terrain.
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  Data: <searchLink fieldCode="AR" term="%22Majhor%2C+Casey+D%2E%22">Majhor, Casey D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cmajhor@mtu.edu</i><br /><searchLink fieldCode="AR" term="%22Bos%2C+Jeremy+P%2E%22">Bos, Jeremy P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Field+Robotics%22">Journal of Field Robotics</searchLink>. Mar2026, Vol. 43 Issue 2, p695-716. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+computer+simulation%22">Digital computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Relief+models%22">Relief models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: We present a comprehensive evaluation of a point‐cloud‐based navigation stack, MUONS, for autonomous off‐road navigation. Performance is characterized by analyzing the results of 30,000 planning and navigation trials in simulation and validated through field testing. Our simulation campaign considers three kinematically challenging terrain maps and twenty combinations of seven path‐planning parameters. In simulation, our MUONS‐equipped AGV achieved a 0.98 success rate and experienced no failures in the field. By statistical and correlation analysis, we determined that the Bi‐RRT expansion radius used in the initial planning stages is most correlated with performance in terms of planning time and traversed path length. Finally, we observed that the proportional variation due to changes in the tuning parameters is remarkably well correlated to performance in field testing. This finding supports the use of Monte‐Carlo simulation campaigns for performance assessment and parameter tuning. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/rob.70059
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 695
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      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Digital computer simulation
        Type: general
      – SubjectFull: Computer performance
        Type: general
      – SubjectFull: Relief models
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
    Titles:
      – TitleFull: Performance Characterization of a Point‐Cloud‐Based Path Planner in Off‐Road Terrain.
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            NameFull: Majhor, Casey D.
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              Text: Mar2026
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              Y: 2026
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