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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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S3PM: Entropy-Regularized Path Planning for Autonomous Mobile Robots in Dense 3D Point Clouds of Unstructured

Artem Sazonov1, Oleksii Kuchkin1, Irina Cherepanska2

  • 1Automation Hardware and Software Department, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 37, Prospect Beresteiskyi, 03056 Kyiv, Ukraine.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

This study introduces S3PM, a novel framework for mobile robot navigation in complex industrial settings. S3PM enhances safety and reliability by integrating dynamic object information into its mapping and path planning, significantly reducing collisions.

Keywords:
computer visionentropymobile robotspath planningpoint cloudrobot controlunstructured environment

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Autonomous Systems

Background:

  • Autonomous navigation in cluttered industrial environments is challenging due to sensor limitations and dynamic elements.
  • Existing methods struggle with partial observability, sensor noise, and moving agents, impacting reliability for Industry 4.0.
  • This necessitates advanced solutions for robust mapping and path planning in unstructured settings.

Purpose of the Study:

  • To introduce S3PM, a lightweight entropy-regularized framework for simultaneous mapping and path planning using 3D point clouds.
  • To develop a dynamics-aware entropy field that fuses occupancy probabilities with motion cues for risk assessment.
  • To enable robust navigation, proactive collision avoidance, and real-time trajectory replanning in dynamic industrial environments.

Main Methods:

  • S3PM utilizes a dynamics-aware entropy field on 3D point clouds, fusing occupancy probabilities with residual optical flow.
  • Each voxel receives a risk-weighted entropy score considering geometric uncertainty and predicted object dynamics.
  • A multi-objective cost function balances path length, smoothness, safety, and information gain, optimized via voxel hashing and incremental distance transforms.

Main Results:

  • S3PM demonstrated 18-27% higher IoU in static/dynamic segmentation and 0.94-0.97 AUC in motion detection.
  • The framework achieved 30-45% fewer collisions compared to baseline methods like OctoMap + RRT*.
  • The system operates at 12-15 Hz on a Raspberry Pi 5, reaching 25-30 Hz with NPU acceleration.

Conclusions:

  • S3PM provides a computationally efficient and robust solution for autonomous navigation in challenging industrial environments.
  • The framework's ability to integrate dynamic information enhances safety and reliability for Industry 4.0 applications.
  • S3PM's performance on resource-constrained platforms makes it suitable for real-world deployment.