Carlos Sampedro

Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection.

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.

Natural user interfaces for human-drone multi-modal interaction

Personal drones are becoming part of every day life. To fully integrate them into society, it is crucial to design safe and intuitive ways to interact with these aerial systems. The recent advances on User-Centered Design (UCD) applied to Natural User Interfaces (NUIs) intend to make use of human innate features, such as speech, gestures and vision to interact with technology in the way humans would with one another. In this paper, a Graphical User Interface (GUI) and several NUI methods are studied and implemented, along with computer vision techniques, in a single software framework for aerial robotics called Aerostack which allows for intuitive and natural human-quadrotor interaction in indoor GPS-denied environments. These strategies include speech, body position, hand gesture and visual marker interactions used to directly command tasks to the drone. The NUIs presented are based on devices like the Leap Motion Controller, microphones and small size monocular on-board cameras which are unnoticeable to the user. Thanks to this UCD perspective, the users can choose the most intuitive and effective type of interaction for their application. Additionally, the strategies proposed allow for multi-modal interaction between multiple users and the drone by being able to integrate several of these interfaces in one single application as is shown in various real flight experiments performed with non-expert users.

Towards autonomous detection and tracking of electric towers for aerial power line inspection

This paper presents an approach towards autonomous aerial power line inspection. In particular, the presented work focuses on real-time autonomous detection, localization and tracking of electric towers. A strategy which combines classic computer vision and machine learning techniques, is proposed. A generalized detection and localization approach is presented, where a two-class multilayer perceptron (MLP) neural network was trained for Tower-Background classification. This MLP is applied over sliding windows for each camera frame until a tower is detected. The detection of a tower triggers the tracker. A hierarchical tracking methodology, especially designed for tracking towers in real-time, is presented. This methodology is based on the Hierarchical Multi-Parametric and Multi-Resolution Inverse Compositional Algorithm [1], and is proposed to be used for tracking and maintaining the tower in the field of view (FOV). The proposed strategy, which is the combination of the tower detector and the tracker, is evaluated on videos from several real manned helicopter inspections. Overall, the results show that the proposed strategy performs very well at detecting and tracking various types of electric towers in diverse environmental settings.

AEROSTACK: An architecture and open-source software framework for aerial robotics

To simplify the usage of the Unmanned Aerial Systems (UAS), extending their use to a great number of applications, fully autonomous operation is needed. There are many open-source architecture frameworks for UAS that claim the autonomous operation of UAS, but they still have two main open issues: (1) level of autonomy, being in most of the cases limited and (2) versatility, being most of them designed specifically for some applications or aerial platforms. As a response to these needs and issues, this paper presents Aerostack, a system architecture and open-source multi-purpose software framework for autonomous multi-UAS operation. To provide higher degrees of autonomy, Aerostacks system architecture integrates state of the art concepts of intelligent, cognitive and social robotics, based on five layers: reactive, executive, deliberative, reflective, and social. To be a highly versatile practical solution, Aerostacks open-source software framework includes the main components to execute the architecture for fully autonomous missions of swarms of UAS; a collection of ready-to-use and flight proven modular components that can be reused by the users and developers; and compatibility with five well known aerial platforms, as well as a high number of sensors. Aerostack has been validated during three years by its successful use on many research projects, international competitions and exhibitions. To corroborate this fact, this paper also presents Aerostack carrying out a fictional fully autonomous indoors search and rescue mission.

A flexible and dynamic mission planning architecture for UAV swarm coordination

In this paper a scalable and flexible Architecture for real-time mission planning and dynamic agent-to-task assignment for a swarm of Unmanned Aerial Vehicles (UAV) is presented. The proposed mission planning architecture consists of a Global Mission Planner (GMP) which is responsible of assigning and monitoring different high-level missions through an Agent Mission Planner (AMP), which is in charge of providing and monitoring each task of the mission to each UAV in the swarm. The objective of the proposed architecture is to carry out high-level missions such as autonomous multi-agent exploration, automatic target detection and recognition, search and rescue, and other different missions with the ability of dynamically re-adapt the mission in real-time. The proposed architecture has been evaluated in simulation and real indoor flights demonstrating its robustness in different scenarios and its flexibility for real-time mission re-planning and dynamic agent-to-task assignment.

A supervised approach to electric tower detection and classification for power line inspection

Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy the different requirements of the inspection: simultaneously detect transmission towers, check for defects, and analyze security distances. Following this direction, this paper proposes a supervised learning approach for solving the tower detection and classification problem, where HOG (Histograms of Oriented Gradients) features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds. In the different evaluations, highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.

A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework

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.

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.

A fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition

In this paper, a fully-autonomous quadrotor aerial robot for solving the different missions proposed in the 2016 International Micro Air Vehicle (IMAV) Indoor Competition is presented. The missions proposed in the IMAV 2016 competition involve the execution of high-level missions such as entering and exiting a building, exploring an unknown indoor environment, recognizing and interacting with objects, landing autonomously on a moving platform, etc. For solving the aforementioned missions, a fully-autonomous quadrotor aerial robot has been designed, based on a complete hardware configuration and a versatile software architecture, which allows the aerial robot to complete all the missions in a fully autonomous and consecutive manner. A thorough evaluation of the proposed system has been carried out in both simulated flights, using the Gazebo simulator in combination with PX4 Software-In-The-Loop, and real flights, demonstrating the appropriate capabilities of the proposed system for performing high-level missions and its flexibility for being adapted to a wide variety of applications.

A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments

A reliable estimation of the flight altitude in dynamic and unstructured indoor environments is an unsolved problem. Standalone available sensors, such as distance sensors, barometers and accelerometers, have multiple limitations in presence of non-flat ground surfaces, or in cluttered areas. To overcome these sensor limitations, maximizing their individual performance, this paper presents a modular EKF- based multi-sensor fusion approach for accurate vertical localization of multirotor UAVs in dynamic and unstructured indoor environments. The state estimator allows to combine the information provided by a variable number and type of sensors, including IMU, barometer and distance sensors, with the capabilities of sensor auto calibration and bias estimation, as well as a flexible configuration of the prediction and update stages. Several autonomous indoors real flights in unstructured environments have been conducted in order to validate our proposed state estimator, enabling the UAV to maintain the desired flight altitude when navigating over wide range of obstacles. Furthermore, it has been successfully used in IMAV 2016 competition. The presented work has been made publicly available to the scientific community as an open source software within the Aerostack1 framework.