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Decomposition-based interval multi-objective evolutionary algorithm for patrol robot path planning in chemical

Enze Zhang1, Guilan Xu1, Yang Yi2

  • 1College of Information and Artificial Intelligence, Yangzhou University, Yangzhou, 225127, China.

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|April 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for patrol robot path planning in chemical plants, optimizing for both efficiency and safety under uncertain conditions. The new method enhances convergence and diversity for better results in hazardous environments.

Keywords:
Decomposition-based multi-objective optimizationEvolutionary algorithmInterval uncertaintyPath planningPatrol robot

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Chemical Engineering

Background:

  • Patrol robot path planning in chemical plants is complex, requiring balancing efficiency and safety.
  • Existing deterministic risk models are inadequate for uncertain chemical plant hazards.
  • Uncertainty in hazard risk necessitates advanced path planning approaches.

Purpose of the Study:

  • To develop an interval multi-objective optimization model for robot path planning.
  • To simultaneously optimize path length and safety risk under uncertainty.
  • To improve path planning in hazardous chemical environments.

Main Methods:

  • Formulated the problem as an interval multi-objective optimization model.
  • Developed a decomposition-based interval multi-objective evolutionary algorithm with dynamic neighborhood and adaptive weight adjustment (IMOEA/D-DAW).
  • Introduced an interval sparsity function for adaptive weight and neighborhood adjustment.

Main Results:

  • The IMOEA/D-DAW algorithm demonstrated improved convergence and solution diversity.
  • Experimental comparisons showed superior path planning results compared to existing algorithms.
  • The proposed method effectively handles uncertain hazard conditions in chemical plants.

Conclusions:

  • The interval multi-objective optimization approach is effective for uncertain environments.
  • IMOEA/D-DAW offers enhanced performance in patrol robot path planning.
  • This research contributes to safer and more efficient robot operations in chemical plants.