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Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integrated with a rapid-exploration random tree to construct a collision-avoidance system. The experimental results show that the proposed collision-avoidance algorithm has a robust performance at high flight speeds in challenging dynamic environments. The experimental results show that the proposed collision-avoidance algorithm can perform faster collision-avoidance maneuvers when compared to the state-of-art algorithms (the average computing time of the collision maneuver is 25.4 ms, while the minimum computing time is 10.4 ms). The average computing time is six times faster than one baseline algorithm. Additionally, fully autonomous flight experiments are also conducted for validating the presented collision-avoidance approach.
Liang Lu; Adrian Carrio; Carlos Sampedro; Pascual Campoy. A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor. Remote Sensing 2021, 13, 1796 .
AMA StyleLiang Lu, Adrian Carrio, Carlos Sampedro, Pascual Campoy. A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor. Remote Sensing. 2021; 13 (9):1796.
Chicago/Turabian StyleLiang Lu; Adrian Carrio; Carlos Sampedro; Pascual Campoy. 2021. "A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor." Remote Sensing 13, no. 9: 1796.
Recent object detection studies have been focused on video sequences, mostly due to the increasing demand of industrial applications. Although single-image architectures achieve remarkable results in terms of accuracy, they do not take advantage of particular properties of the video sequences and usually require high parallel computational resources, such as desktop GPUs. In this work, an inattentional framework is proposed, where the object context in video frames is dynamically reused in order to reduce the computation overhead. The context features corresponding to keyframes are fused into a synthetic feature map, which is further refined using temporal aggregation with ConvLSTMs. Furthermore, an inattentional policy has been learned to adaptively balance the accuracy and the amount of context reused. The inattentional policy has been learned under the reinforcement learning paradigm, and using our novel reward-conditional training scheme, which allows for policy training over a whole distribution of reward functions and enables the selection of a unique reward function at inference time. Our framework shows outstanding results on platforms with reduced parallelization capabilities, such as CPUs, achieving an average latency reduction up to 2.09x, and obtaining FPS rates similar to their equivalent GPU platform, at the cost of a 1.11x mAP reduction.
Alejandro Rodriguez-Ramos; Javier Rodriguez-Vazquez; Carlos Sampedro; Pascual Campoy. Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training. IEEE Access 2020, 8, 124451 -124466.
AMA StyleAlejandro Rodriguez-Ramos, Javier Rodriguez-Vazquez, Carlos Sampedro, Pascual Campoy. Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training. IEEE Access. 2020; 8 (99):124451-124466.
Chicago/Turabian StyleAlejandro Rodriguez-Ramos; Javier Rodriguez-Vazquez; Carlos Sampedro; Pascual Campoy. 2020. "Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training." IEEE Access 8, no. 99: 124451-124466.
This paper presents a complete system for automatic recognition and diagnosis of electrical insulator strings, which efficiently combines different deep learning-based components to build a versatile solution to the automation problem of the power line inspection process. To this aim, the proposed system integrates one component responsible for insulator string segmentation and two components in charge of its diagnosis. The insulator string segmentation component consists of a novel Fully Convolutional Network (FCN) architecture, termed Up-Net, which enhances the capabilities of the state-of-the-art U-Net network by introducing new skip connections at certain levels of the architecture. We further propose a second variant of the Up-Net network by training it within a Generative Adversarial Network (GAN) framework. The capabilities of the proposed Up-Net variants are incremented by the application of data augmentation and transfer learning techniques, achieving accurate segmentation of the insulator string elements (i.e., discs and caps). Regarding the insulator string diagnosis, we design a Convolutional Neural Network (CNN) which takes as input the mask generated by the insulator string segmentation component and is capable of identifying the absence of a variable number of discs. The second diagnosis component consists of a novel strategy which integrates a Siamese Convolutional Neural Network (SCNN) designed for modeling the similarity between adjacent discs, allowing the detection of several types of disc defects using the same model. The proposed system has been extensively evaluated in several video sequences from real aerial inspections of high voltage insulators, showing robust insulator recognition and diagnosis capabilities.
Carlos Sampedro; Javier Rodriguez-Vazquez; Alejandro Rodriguez-Ramos; Adrian Carrio; Pascual Campoy. Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings. IEEE Access 2019, 7, 101283 -101308.
AMA StyleCarlos Sampedro, Javier Rodriguez-Vazquez, Alejandro Rodriguez-Ramos, Adrian Carrio, Pascual Campoy. Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings. IEEE Access. 2019; 7 (99):101283-101308.
Chicago/Turabian StyleCarlos Sampedro; Javier Rodriguez-Vazquez; Alejandro Rodriguez-Ramos; Adrian Carrio; Pascual Campoy. 2019. "Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings." IEEE Access 7, no. 99: 101283-101308.
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.
Hriday Bavle; Jose Luis Sanchez-Lopez; Paloma De La Puente; Alejandro Rodriguez-Ramos; Carlos Sampedro; Pascual Campoy. Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors. Aerospace 2018, 5, 94 .
AMA StyleHriday Bavle, Jose Luis Sanchez-Lopez, Paloma De La Puente, Alejandro Rodriguez-Ramos, Carlos Sampedro, Pascual Campoy. Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors. Aerospace. 2018; 5 (3):94.
Chicago/Turabian StyleHriday Bavle; Jose Luis Sanchez-Lopez; Paloma De La Puente; Alejandro Rodriguez-Ramos; Carlos Sampedro; Pascual Campoy. 2018. "Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors." Aerospace 5, no. 3: 94.
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.
Adrian Carrio; Carlos Sampedro; Jose Luis Sanchez-Lopez; Miguel Pimienta; Pascual Campoy. Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection. Sensors 2015, 15, 29569 -29593.
AMA StyleAdrian Carrio, Carlos Sampedro, Jose Luis Sanchez-Lopez, Miguel Pimienta, Pascual Campoy. Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection. Sensors. 2015; 15 (11):29569-29593.
Chicago/Turabian StyleAdrian Carrio; Carlos Sampedro; Jose Luis Sanchez-Lopez; Miguel Pimienta; Pascual Campoy. 2015. "Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection." Sensors 15, no. 11: 29569-29593.