Active Robotic Search for Victims using Ensemble Deep Learning Techniques - Scenario 2
This video complements the article: García-Samartín, J.F. et al "Active Robotic Search for Victims Using Ensemble Deep Learning Techniques"
It shows the validation experiment carried out in the second of the orange scenarios presented in the work. As described in the published paper, ARTU-R, the quadruped robot, can intelligently search for victims in an unstructured environment after a disaster.
Abstract: In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with Search and Rescue (SAR) operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) 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 (IS) and Next Best View (NBV) 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 (CNN). 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.
Cite: García-Samartín, J.F.; Cruz Ulloa, C.; del Cerro, J.; Barrientos, A. "Active Robotic Search for Victims Using Ensemble Deep Learning Techniques", Machine Learning: Science and Technology, 2024.