References of "Sampedro, Carlos"
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See detailDeep learning based semantic situation awareness system for multirotor aerial robots using LIDAR
Sanchez Lopez, Jose Luis UL; Sampedro, Carlos; Cazzato, Dario UL et al

in 2019 International Conference on Unmanned Aircraft Systems (ICUAS) (2019, June)

In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise ... [more ▼]

In this work, we present a semantic situation awareness system for multirotor aerial robots, based on 2D LIDAR measurements, targeting the understanding of the environment and assuming to have a precise robot localization as an input of our algorithm. Our proposed situation awareness system calculates a semantic map of the objects of the environment as a list of circles represented by their radius, and the position and the velocity of their center in world coordinates. Our proposed algorithm includes three main parts. First, the LIDAR measurements are preprocessed and an object segmentation clusters the candidate objects present in the environment. Secondly, a Convolutional Neural Network (CNN) that has been designed and trained using an artificially generated dataset, computes the radius and the position of the center of individual circles in sensor coordinates. Finally, an indirect-EKF provides the estimate of the semantic map in world coordinates, including the velocity of the center of the circles in world coordinates.We have quantitative and qualitative evaluated the performance of our proposed situation awareness system by means of Software-In-The-Loop simulations using VRep with one and multiple static and moving cylindrical objects in the scene, obtaining results that support our proposed algorithm. In addition, we have demonstrated that our proposed algorithm is capable of handling real environments thanks to real laboratory experiments with non-cylindrical static (i.e. a barrel) and moving (i.e. a person) objects. [less ▲]

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See detailA Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Sampedro, Carlos; Rodriguez-Ramos, Alejandro; Bavle, Hriday UL et al

in Journal of Intelligent and Robotic Systems (2019), 95(2), 601--627

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 ... [more ▼]

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. [less ▲]

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See detailFast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors
Bavle, Hriday UL; Sanchez Lopez, Jose Luis UL; de la Puente, Paloma et al

in Aerospace (2018), 5(3),

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor ... [more ▼]

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack. [less ▲]

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See detailStereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments
Bavle, Hriday UL; Manthe, Stephan; De La Puente, Paloma et al

Scientific Conference (2018)

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of ... [more ▼]

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot. We validate our approach in several real flight experiments where we compare it against ground truth and a state of the art visual SLAM approach. [less ▲]

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See detailThe Power Line Inspection Software (PoLIS): A versatile system for automating power line inspection
Martinez Luna, Carol UL; Sampedro, Carlos; Chauhan, Aneesh et al

in Engineering applications of artificial intelligence (2018), 71

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See detailA Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform
Rodriguez-Ramos, Alejandro; Sampedro, Carlos; Bavle, Hriday UL et al

in IEEE International Conference on Intelligent Robots and Systems (2018)

Deep learning techniques for motion control have recently been qualitatively improved, since the successful application of Deep Q- Learning to the continuous action domain in Atari-like games. Based on ... [more ▼]

Deep learning techniques for motion control have recently been qualitatively improved, since the successful application of Deep Q- Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide impressive results in continuous state and action domains, which are closely linked to most of the robotics-related tasks. In this paper, a vision-based autonomous multirotor landing maneuver on top of a moving platform is presented. The behaviour has been completely learned in simulation without prior human knowledge and by means of deep reinforcement learning techniques. Since the multirotor is controlled in attitude, no high level state estimation is required. The complete behaviour has been trained with continuous action and state spaces, and has provided proper results (landing at a maximum velocity of 2 m/s), Furthermore, it has been validated in a wide variety of conditions, for both simulated and real-flight scenarios, using a low-cost, lightweight and out-of-the-box consumer multirotor. [less ▲]

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See detailLaser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning
Sampedro, Carlos; Bavle, Hriday UL; Rodriguez-Ramos, Alejandro et al

Scientific Conference (2018)

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re ... [more ▼]

Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles. [less ▲]

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See detailA Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework
Sanchez Lopez, Jose Luis UL; Molina, Martin; Bavle, Hriday UL et al

in Journal of Intelligent and Robotic Systems (2017), 88(2), 638-709

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods ... [more ▼]

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission. [less ▲]

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See detailTML: a language to specify aerial robotic missions for the framework Aerostack
Molina, Martin; Suarez-Fernandez, Ramon; Sampedro, Carlos et al

in International Journal of Intelligent Computing and Cybernetics (2017), 10(4), 491-512

Purpose - The main purpose of this paper is to describe the specification language TML for adaptive mission plans that we designed and implemented for the open source framework Aerostack for aerial ... [more ▼]

Purpose - The main purpose of this paper is to describe the specification language TML for adaptive mission plans that we designed and implemented for the open source framework Aerostack for aerial robotics. Approach – The TML language combines a task-based hierarchical approach together with a more flexible representation, rule-based reactive planning, to facilitate adaptability. This approach includes additional notions that abstract programming details. We built an interpreter integrated in the software framework Aerostack. The interpreter was validated with flight experiments for multi-robot missions in dynamic environments. Findings – The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments. The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms. The TML interpreter is able to verify the mission plan before its execution, which increases robustness and safety, avoiding the execution of certain plans that are not feasible. Originality – One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans, integrated in an active open-source project with periodic releases. To the best knowledge of the authors, there are not solutions similar to this in other active open-source projects. As additional contributions, TML uses an original combination of representations for adaptive mission plans (i.e., task trees with original abstract notions and rule-based reactive planning) together with the demonstration of its adequacy for aerial robotics. [less ▲]

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See detailA flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments
Bavle, Hriday UL; Sanchez Lopez, Jose Luis UL; Rodriguez-Ramos, Alejandro et al

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, June)

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See detailA fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition
Sampedro, Carlos; Bavle, Hriday UL; Rodríguez-Ramos, Alejandro et al

in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) (2017, June)

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See detailTowards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles
Rodriguez-Ramos, Alejandro; Sampedro, Carlos; Bavle, Hriday UL et al

Scientific Conference (2017)

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means ... [more ▼]

Fully autonomous landing on moving platforms poses a problem of importance for Unmanned Aerial Vehicles (UAVs). Current approaches are usually based on tracking and following the moving platform by means of several techniques, which frequently lack performance in real applications. The aim of this paper is to prove a simple landing strategy is able to provide practical results. The presented approach is based on three stages: estimation, prediction and fast landing. As a preliminary phase, the problem is solved for a particular case of the IMAV 2016 competition. Subsequently, it is extended to a more generic and versatile approach. A thorough evaluation has been conducted with simulated and real flight experiments. Simulations have been performed utilizing Gazebo 6 and PX4 Software-In-The-Loop (SITL) and real flight experiments have been conducted with a custom quadrotor and a moving platform in an indoor environment. [less ▲]

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See detailSpecifying complex missions for aerial robotics in dynamic environments
Molina, Martin; Diaz-Moreno, Adrian; Palacios, David et al

Scientific Conference (2016, October)

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See detailA flexible and dynamic mission planning architecture for UAV swarm coordination
Sampedro, Carlos; Bavle, Hriday; Sanchez Lopez, Jose Luis UL et al

in 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (2016, June)

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See detailNatural user interfaces for human-drone multi-modal interaction
Suárez Fernández, Ramon; Sanchez Lopez, Jose Luis UL; Sampedro, Carlos et al

in 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (2016, June)

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See detailAEROSTACK: An architecture and open-source software framework for aerial robotics
Sanchez Lopez, Jose Luis UL; Suárez Fernández, Ramon; Bavle, Hriday et al

in 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (2016, June)

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See detailAutomated low-cost smartphone-based lateral flow saliva test reader for drugs-of-abuse detection
Carrio, Adrian; Sampedro, Carlos; Sanchez Lopez, Jose Luis UL et al

in Sensors (2015), 15(11), 29569--29593

Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator ... [more ▼]

Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results. [less ▲]

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See detailTowards autonomous detection and tracking of electric towers for aerial power line inspection
Martinez Luna, Carol UL; Sampedro, Carlos; Chauhan, Aneesh et al

in 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (2014)

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See detailA supervised approach to electric tower detection and classification for power line inspection
Sampedro, Carlos; Martinez Luna, Carol UL; Chauhan, Aneesh et al

in 2014 International Joint Conference on Neural Networks (IJCNN) (2014)

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