AntBot-EX: Enhancing robot search efficiency in complex post-disaster environments

Abstract

In post-disaster scenarios, effective rescue operations hinge on deploying robots equipped with sophisticated path planning algorithms capable of navigating through complex and unknown environments, facilitating an exhaustive search for survivors. The inherent limitations of traditional Coverage Path Planning (CPP) algorithms, particularly their struggle to adapt to the highly dynamic and unpredictable nature of post-disaster environments characterized by collapsed structures, shifting debris fields, and unforeseen obstacles, hinder their effectiveness in time-sensitive rescue operations. To address the challenges, this paper introduces an innovative three-stage online CPP method, termed Ant Colony Optimization based Robot Exploration with Escape Mechanism (AntBot-EX). Our three-stage approach leverages the strengths of different algorithms. Firstly, we utilize a modified Ant Colony Optimization algorithm to explore the unknown environment efficiently, prioritizing uncharted territories and avoiding potential dead ends using an escape mechanism. Secondly, the remaining unexplored areas are segmented, enabling targeted path planning with the A* algorithm to maximize coverage. Thirdly, to address computational limitations in large and complex environments, a configurable boundary-aware and a score-based threshold are introduced to simplify paths by strategically disregarding irrelevant regions, optimizing search efficiency. Simulation results show that our method can basically achieve complete coverage in complex and unknown environments.