Reference : A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environm...
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Sampedro, Carlos [> >]
Rodriguez-Ramos, Alejandro [> >]
Bavle, Hriday mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation]
Carrio, Adrian [> >]
de la Puente, Paloma [> >]
Campoy, Pascual [> >]
Journal of Intelligent and Robotic Systems
Journal of Intelligent & Robotic Systems
Yes (verified by ORBilu)
[en] Autonomous robots ; Deep learning ; Image-based visual servoing ; Reinforcement learning ; Search and rescue ; Supervised learning
[en] 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.

File(s) associated to this reference

Fulltext file(s):

Open access
root.pdfAuthor preprint3.47 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.