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Alejandro Rodriguez-Ramos
Computer Vision and Aerial Robotics group, Centre for Automation and Robotics, Universidad Politécnica de Madrid (UPM-CSIC), Calle Jose Gutierrez Abascal 2, 28006 Madrid, Spain. ()

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Journal article
Published: 01 July 2020 in IEEE Access
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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.

ACS Style

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 Style

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 (99):124451-124466.

Chicago/Turabian Style

Alejandro 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.

Journal article
Published: 04 November 2019 in Sensors
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Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).

ACS Style

Alejandro Rodriguez-Ramos; Adrian Alvarez-Fernandez; Hriday Bavle; Pascual Campoy; Jonathan P. How. Vision-Based Multirotor Following Using Synthetic Learning Techniques. Sensors 2019, 19, 4794 .

AMA Style

Alejandro Rodriguez-Ramos, Adrian Alvarez-Fernandez, Hriday Bavle, Pascual Campoy, Jonathan P. How. Vision-Based Multirotor Following Using Synthetic Learning Techniques. Sensors. 2019; 19 (21):4794.

Chicago/Turabian Style

Alejandro Rodriguez-Ramos; Adrian Alvarez-Fernandez; Hriday Bavle; Pascual Campoy; Jonathan P. How. 2019. "Vision-Based Multirotor Following Using Synthetic Learning Techniques." Sensors 19, no. 21: 4794.

Journal article
Published: 25 July 2019 in IEEE Access
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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.

ACS Style

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 Style

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 (99):101283-101308.

Chicago/Turabian Style

Carlos 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.

Conference paper
Published: 01 October 2018 in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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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.

ACS Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Ignacio Gil Moreno; Pascual Campoy. A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018, 1010 -1017.

AMA Style

Alejandro Rodriguez-Ramos, Carlos Sampedro, Hriday Bavle, Ignacio Gil Moreno, Pascual Campoy. A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018; ():1010-1017.

Chicago/Turabian Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Ignacio Gil Moreno; Pascual Campoy. 2018. "A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform." 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , no. : 1010-1017.

Journal article
Published: 06 September 2018 in Aerospace
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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.

ACS Style

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 Style

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 (3):94.

Chicago/Turabian Style

Hriday 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.

Article
Published: 03 July 2018 in Journal of Intelligent & Robotic Systems
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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.

ACS Style

Carlos Sampedro; Alejandro Rodriguez-Ramos; Hriday Bavle; Adrian Carrio; Paloma De La Puente; Pascual Campoy. A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. Journal of Intelligent & Robotic Systems 2018, 95, 601 -627.

AMA Style

Carlos Sampedro, Alejandro Rodriguez-Ramos, Hriday Bavle, Adrian Carrio, Paloma De La Puente, Pascual Campoy. A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. Journal of Intelligent & Robotic Systems. 2018; 95 (2):601-627.

Chicago/Turabian Style

Carlos Sampedro; Alejandro Rodriguez-Ramos; Hriday Bavle; Adrian Carrio; Paloma De La Puente; Pascual Campoy. 2018. "A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques." Journal of Intelligent & Robotic Systems 95, no. 2: 601-627.

Article
Published: 03 July 2018 in Journal of Intelligent & Robotic Systems
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The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, 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 outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

ACS Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Paloma de la Puente; Pascual Campoy. A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform. Journal of Intelligent & Robotic Systems 2018, 93, 351 -366.

AMA Style

Alejandro Rodriguez-Ramos, Carlos Sampedro, Hriday Bavle, Paloma de la Puente, Pascual Campoy. A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform. Journal of Intelligent & Robotic Systems. 2018; 93 (1-2):351-366.

Chicago/Turabian Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Paloma de la Puente; Pascual Campoy. 2018. "A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform." Journal of Intelligent & Robotic Systems 93, no. 1-2: 351-366.

Review
Published: 14 August 2017 in Journal of Sensors
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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.

ACS Style

Adrian Carrio; Carlos Sampedro; Alejandro Rodriguez-Ramos; Pascual Campoy. A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles. Journal of Sensors 2017, 2017, 1 -13.

AMA Style

Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez-Ramos, Pascual Campoy. A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles. Journal of Sensors. 2017; 2017 ():1-13.

Chicago/Turabian Style

Adrian Carrio; Carlos Sampedro; Alejandro Rodriguez-Ramos; Pascual Campoy. 2017. "A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles." Journal of Sensors 2017, no. : 1-13.

Conference paper
Published: 01 June 2017 in 2017 International Conference on Unmanned Aircraft Systems (ICUAS)
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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 Aerostack 1 framework.

ACS Style

Hriday Bavle; Jose Luis Sanchez-Lopez; Alejandro Rodriguez-Ramos; Carlos Sampedro; Pascual Campoy. A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments. 2017 International Conference on Unmanned Aircraft Systems (ICUAS) 2017, 1044 -1051.

AMA Style

Hriday Bavle, Jose Luis Sanchez-Lopez, Alejandro Rodriguez-Ramos, Carlos Sampedro, Pascual Campoy. A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments. 2017 International Conference on Unmanned Aircraft Systems (ICUAS). 2017; ():1044-1051.

Chicago/Turabian Style

Hriday Bavle; Jose Luis Sanchez-Lopez; Alejandro Rodriguez-Ramos; Carlos Sampedro; Pascual Campoy. 2017. "A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments." 2017 International Conference on Unmanned Aircraft Systems (ICUAS) , no. : 1044-1051.

Conference paper
Published: 01 June 2017 in 2017 International Conference on Unmanned Aircraft Systems (ICUAS)
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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.

ACS Style

Carlos Sampedro; Hriday Bavle; Alejandro Rodriguez-Ramos; Adrian Carrio; Ramón A. Suárez Fernández; Jose Luis Sanchez-Lopez; Pascual Campoy. A fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition. 2017 International Conference on Unmanned Aircraft Systems (ICUAS) 2017, 989 -998.

AMA Style

Carlos Sampedro, Hriday Bavle, Alejandro Rodriguez-Ramos, Adrian Carrio, Ramón A. Suárez Fernández, Jose Luis Sanchez-Lopez, Pascual Campoy. A fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition. 2017 International Conference on Unmanned Aircraft Systems (ICUAS). 2017; ():989-998.

Chicago/Turabian Style

Carlos Sampedro; Hriday Bavle; Alejandro Rodriguez-Ramos; Adrian Carrio; Ramón A. Suárez Fernández; Jose Luis Sanchez-Lopez; Pascual Campoy. 2017. "A fully-autonomous aerial robotic solution for the 2016 International Micro Air Vehicle competition." 2017 International Conference on Unmanned Aircraft Systems (ICUAS) , no. : 989-998.

Conference paper
Published: 01 June 2017 in 2017 International Conference on Unmanned Aircraft Systems (ICUAS)
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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.

ACS Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Zorana Milosevic; Alejandro Garcia-Vaquero; Pascual Campoy. Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles. 2017 International Conference on Unmanned Aircraft Systems (ICUAS) 2017, 170 -178.

AMA Style

Alejandro Rodriguez-Ramos, Carlos Sampedro, Hriday Bavle, Zorana Milosevic, Alejandro Garcia-Vaquero, Pascual Campoy. Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles. 2017 International Conference on Unmanned Aircraft Systems (ICUAS). 2017; ():170-178.

Chicago/Turabian Style

Alejandro Rodriguez-Ramos; Carlos Sampedro; Hriday Bavle; Zorana Milosevic; Alejandro Garcia-Vaquero; Pascual Campoy. 2017. "Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles." 2017 International Conference on Unmanned Aircraft Systems (ICUAS) , no. : 170-178.

Conference paper
Published: 01 June 2016 in 2016 International Conference on Unmanned Aircraft Systems (ICUAS)
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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.

ACS Style

Carlos Sampedro; Hriday Bavle; Jose Luis Sanchez-Lopez; Ramón A. Suárez Fernández; Alejandro Rodriguez-Ramos; Martin Molina; Pascual Campoy. A flexible and dynamic mission planning architecture for UAV swarm coordination. 2016 International Conference on Unmanned Aircraft Systems (ICUAS) 2016, 355 -363.

AMA Style

Carlos Sampedro, Hriday Bavle, Jose Luis Sanchez-Lopez, Ramón A. Suárez Fernández, Alejandro Rodriguez-Ramos, Martin Molina, Pascual Campoy. A flexible and dynamic mission planning architecture for UAV swarm coordination. 2016 International Conference on Unmanned Aircraft Systems (ICUAS). 2016; ():355-363.

Chicago/Turabian Style

Carlos Sampedro; Hriday Bavle; Jose Luis Sanchez-Lopez; Ramón A. Suárez Fernández; Alejandro Rodriguez-Ramos; Martin Molina; Pascual Campoy. 2016. "A flexible and dynamic mission planning architecture for UAV swarm coordination." 2016 International Conference on Unmanned Aircraft Systems (ICUAS) , no. : 355-363.