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

By Carlos Sampedro, Alejandro Rodriguez-Ramos, Hriday Bavle, Adrian Carrio, Paloma de la Puente, Pascual Campoy. 2018

163 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Reinforcement Learning

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Citations in the corpus

Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (2018, background): “For real-world technologies, such as self-driving cars [10], autonomous drones [14], and search-and-rescue robots [37], the test distribution may be non-stationary, and new observations will often be out-of-distribution (OoD), i.”
Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments (2019, methodology): “Traditional machine learning and deep learning techniques have been applied to image classification [13]–[16], target object detection [17], [18], robot-team work division [18], and robot visual odometry [19], [20] problems.”
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, methodology): “In the ﬁeld of SAR robotics, a relevant example of the utilization of both DL and DRL techniques was presented by Sampedro et al. [245].”