Active robotic search for victims using ensemble deep learning techniques

Author:

García-Samartín Jorge FORCID,Cruz Ulloa ChristyanORCID,del Cerro JaimeORCID,Barrientos AntonioORCID

Abstract

Abstract In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with search and rescue operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot A1 Rescue Tasks UPM Robot (ARTU-R) by Unitree, equipped with an RGB-D camera and a lidar, to perform victim searches in post-disaster scenarios. Exploration is done not by following a pre-planned path (as common methods) but by prioritising the areas most likely to harbour victims. To accomplish that task, both indirect search and next best view techniques have been used. When ARTU-R gets inside an unstructured and unknown environment, it selects the next exploration point from a series of candidates. This operation is performed by comparing, for each candidate, the distance to reach it, the unexplored space around it and the probability of a victim being in its vicinity. This probability value is obtained using a Random Forest, which processes the information provided by a convolutional neural network. Unlike other AI techniques, random forests are not black box models; humans can understand their decision-making processes. The system, once integrated, achieves speeds comparable to other state-of-the-art algorithms in terms of exploration, but concerning victim detection, the tests show that the resulting smart exploration generates logical paths—from a human point of view—and that ARTU-R tends to move first to the regions where victims are present.

Funder

Proyectos de I+D+i del Ministerio de Ciencia, Innovacion y Universidades

Universidad Politécnica de Madrid

Programas de Actividades I+D en la Comunidad Madrid

Publisher

IOP Publishing

Reference65 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3