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This study proposes an advanced waypoint navigation framework utilizing an interval type-2 fuzzy logic controller (IT2FLC). By integrating a dynamic model that explicitly considers wheel slip effects, the proposed system enhances the dependability of mobile robot navigation. The primary application focuses on search and rescue (SAR) operations, demanding robust and accurate navigation in complex and hazardous terrains. A differential drive wheeled mobile robot (DDWMR) model is utilized to implement the IT2FLC, with performance comparisons drawn against type-1 fuzzy logic controllers (T1FLC) and sliding mode controllers (SMC). Comprehensive robustness evaluations, including considerations of model inaccuracies, localization errors, motor performance degradation, and challenging surface conditions, reveal that IT2FLC delivers superior outcomes. Its effectiveness is particularly notable under severe wheel slip and parameter variations commonly encountered in SAR contexts.

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Adaptive Control of Mobile Robots Using Neuro-Fuzzy Based Reinforcement Learning Systems with Nonlinear Wheel-Slip Dynamics and Uncertainty Estimation

  • 会议论文

  • 发表日期：

    2026年

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  • Yasmine Saidi,
  • Aimen Abdelhak Messaoui,
  • Fethi Demim,
  • Ali Zakaria Messaoui,
  • Sofiane Bououden,
  • Abdelkrim Nemra,
  • Souhila Benmansour,
  • Momamed Tadjine

摘要

This study proposes an advanced waypoint navigation framework utilizing an interval type-2 fuzzy logic controller (IT2FLC). By integrating a dynamic model that explicitly considers wheel slip effects, the proposed system enhances the dependability of mobile robot navigation. The primary application focuses on search and rescue (SAR) operations, demanding robust and accurate navigation in complex and hazardous terrains. A differential drive wheeled mobile robot (DDWMR) model is utilized to implement the IT2FLC, with performance comparisons drawn against type-1 fuzzy logic controllers (T1FLC) and sliding mode controllers (SMC). Comprehensive robustness evaluations, including considerations of model inaccuracies, localization errors, motor performance degradation, and challenging surface conditions, reveal that IT2FLC delivers superior outcomes. Its effectiveness is particularly notable under severe wheel slip and parameter variations commonly encountered in SAR contexts.