A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques

Sampedro, Carlos and Rodríguez Ramos, Alejandro and Bavle, Hriday and Carrio Fernández, Adrián and Puente Yusty, Paloma de la and Campoy Cervera, Pascual (2019). A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. "Journal of Intelligent & Robotic Systems", v. 95 (n. 2); pp. 601-627. ISSN 0921-0296. https://doi.org/10.1007/s10846-018-0898-1.

Description

Title: A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Author/s:
  • Sampedro, Carlos
  • Rodríguez Ramos, Alejandro
  • Bavle, Hriday
  • Carrio Fernández, Adrián
  • Puente Yusty, Paloma de la
  • Campoy Cervera, Pascual
Item Type: Article
Título de Revista/Publicación: Journal of Intelligent & Robotic Systems
Date: 15 August 2019
ISSN: 0921-0296
Volume: 95
Subjects:
Freetext Keywords: Autonomous robots; Search and rescue; Supervised learning; Reinforcement learning; Deep learning; Image-based visual servoing
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2014-60139-RUnspecifiedUnspecifiedAutonomía visual para vehículos aéreos no tripulados en entornos dinámicos

More information

Item ID: 64148
DC Identifier: http://oa.upm.es/64148/
OAI Identifier: oai:oa.upm.es:64148
DOI: 10.1007/s10846-018-0898-1
Official URL: https://link.springer.com/article/10.1007/s10846-018-0898-1
Deposited by: Memoria Investigacion
Deposited on: 23 Oct 2020 10:28
Last Modified: 23 Oct 2020 10:28
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