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Summary

This study introduces a novel approach for autonomous mobile robot navigation in dynamic environments. By using semantic segmentation for drivable area estimation, robots can plan safer local motion, even in unexperienced real-world settings.

Keywords:
obstacle avoidancesemantic segmentationsidewalk autonomous delivery robotszero-shot transfer

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous mobile robots require accurate environmental perception for safe navigation in dynamic settings.
  • Traditional methods using local maps for dynamic scene representation are limited by map accuracy and obstacle density.
  • Predicting scene evolution is crucial for feasible local motion planning.

Purpose of the Study:

  • To propose an alternative representation for local motion planning using semantic segmentation-based drivable area estimation.
  • To develop and validate a system capable of zero-shot transfer from simulation to real-world environments.
  • To enhance the safety and efficiency of autonomous robot navigation in dynamic sidewalk environments.

Main Methods:

  • A realistic 3D simulator (Unreal Engine) was used to generate synthetic datasets under diverse weather conditions.
  • An encoder-decoder model trained with transfer learning performed semantic segmentation to identify drivable areas.
  • A nonlinear model predictive control (NMPC) scheme was employed for local motion planning, integrating estimated drivable space, robot state, and global plan.

Main Results:

  • The proposed method demonstrated effective zero-shot transfer capabilities, performing well in previously unseen real-world scenarios.
  • Experiments showed the system's effectiveness in dynamic sidewalk environments, outperforming the dynamic window approach (DWA).
  • The NMPC planner successfully generated safe velocity commands, minimizing tracking costs and actuator effort while avoiding obstacles.

Conclusions:

  • Semantic segmentation-based drivable area estimation provides a robust alternative for local motion planning in dynamic environments.
  • The developed system offers a reliable solution for autonomous robot navigation, achieving successful simulation-to-real-world transfer.
  • The approach enhances robot safety and efficiency by effectively handling dynamic and static obstacles.