Efficient Navigation for Quadruped Robots in Post-Disaster Scenarios
Christyan Cruz Ulloa, Jorge Tolón, Jaime del Cerro, Antonio Barrientos
Abstract
Search and rescue (SAR) operations require optimal solutions to maximize efficiency and minimize risks for human responders. Quadruped robots have emerged as viable agents due to their locomotion and agility capabilities. This work presents a navigation framework designed to enhance stability and adaptability of legged robots in complex environments. A high-fidelity simulation environment was developed using NVIDIA IsaacSim, incorporating realistic disaster conditions such as unstructured terrain, fire, and smoke. Multiple stochastic routes were generated and analyzed in simulation in terms of energy consumption, stability, traversal time, and fall occurrences, to categorize them and determine the safest and most efficient path before real-world deployment, reducing the likelihood of failures. A fuzzy logic controller was proposed to regulate speed and improve locomotion adaptability. The proposed approach was validated in both simulation and real-world experiments. The results demonstrate the effectiveness of the proposed strategy in enabling safe and efficient navigation for quadruped robots in SAR missions. The code is publicly available at Robcib-GIT/IsaacSim_LeggedRobotSupplementary Video: https://youtu.be/cxEP5YVy8Qo
BibTeX
@inproceedings{iros2025_efficientnavigat,
title = {Efficient Navigation for Quadruped Robots in Post-Disaster Scenarios},
author = {Christyan Cruz Ulloa and Jorge Tolón and Jaime del Cerro and Antonio Barrientos},
booktitle = {IROS 2025},
year = {2025}
}