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A Robotic Cognitive Architecture for Slope and Dam Inspections.

Milena F Pinto1, Leonardo M Honorio2, Aurélio Melo2

  • 1Electronics Department, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro CEP 20271, Brazil.

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Summary

This study introduces a cognitive architecture for unmanned aerial vehicles (UAVs) to improve visual inspections of large construction sites. The system optimizes data gathering and decision-making for enhanced performance and safety.

Keywords:
3D reconstructiondam inspectiondecentralized architecturedecision-makingintelligent sensingrule-based expert systemunmanned aerial vehicle

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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Large-scale construction projects like dams and mining slopes require frequent, detailed visual inspections.
  • Current inspection methods using human operators or basic drones struggle with complex multi-objective goals (performance, quality, safety), often necessitating mission repetition.
  • The sheer volume of data and the need for real-time analysis pose significant challenges for autonomous systems.

Purpose of the Study:

  • To develop a novel cognitive architecture for unmanned aerial vehicles (UAVs) to enhance data gathering, information processing, and decision-making during infrastructure inspections.
  • To create a collaborative environment among UAVs and other agents for optimizing complex inspection missions.
  • To enable real-time, intelligent decision-making for efficient and safe completion of construction site inspections.

Main Methods:

  • Development of a cognitive architecture that modularizes tasks from sensor input to high-level intelligence.
  • Implementation of a collaborative agent-based system where each agent manages specific behaviors.
  • Introduction of a supervisory agent to analyze and resolve conflicting agent requests, ensuring optimized mission planning.
  • Utilizing slope inspection scenarios to demonstrate the proposed methodology.

Main Results:

  • The proposed architecture facilitates optimized data gathering and processing for complex inspection tasks.
  • Real-time decision-making capabilities are achieved through intelligent social behavior among collaborating agents.
  • The system effectively manages conflicting objectives, leading to more efficient and successful missions.
  • Demonstrated feasibility through successful application in slope inspection scenarios.

Conclusions:

  • The developed cognitive architecture significantly improves the efficiency and effectiveness of UAV-based inspections for large construction enterprises.
  • The collaborative, agent-based approach enables intelligent management of multi-objective goals in dynamic environments.
  • This methodology offers a pathway towards more autonomous and reliable infrastructure monitoring systems.