Distributed Small-Step Path Planning and Detection Method for Post-earthquake Robot to Inspect and Evaluate Building...

Area of Science:

• Robotics
• Artificial Intelligence
• Structural Engineering

Background:

• Post-earthquake environments pose significant risks for building safety assessments.
• Autonomous robots are essential for inspecting damaged structures in hazardous conditions.
• Effective path planning is critical for robots to navigate complex and unstable terrains.

Purpose of the Study:

• To develop an advanced path planning method for post-earthquake robots.
• To enhance the accuracy and efficiency of building damage inspection.
• To enable robots to navigate safely and effectively in unpredictable environments.

Main Methods:

• Proposed a distributed small-step path planning method utilizing modified reinforcement learning (MRL).
• Implemented a grid-based movement system with limited distance and 12 directions.
• Integrated an improved Harris algorithm for enhanced building corner point detection.
• Utilized an experimental simulation platform for validation.

Main Results:

• Achieved high accuracy in post-earthquake building damage inspection, reaching up to 98%.
• Demonstrated a 20% improvement in detection accuracy compared to traditional methods.
• Successfully verified the robot's design, path planning algorithm, and overall detection performance.
• Showcased the robot's capability to explore unknown and hazardous environments.

Conclusions:

• The MRL-based path planning method enables optimal navigation in complex post-earthquake scenarios.
• The developed system significantly enhances the accuracy of building damage assessment.
• The proposed robot is suitable for exploring hazardous conditions unsafe for humans.