A Robotic Cognitive Architecture for Slope and Dam Inspections

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.