A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

International audienceThis paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives

An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping

Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixel-wise labelled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pre-trained on publicly available datasets. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model re-training. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labelled for model re-training. Experimental results on real-world data and in a photorealistic simulation show that our framework maximises model performance and drastically reduces labelling efforts. Our map-based planners outperform state-of-the-art local planning.Comment: 18 pages, 24 figure

Field coverage and weed mapping by UAV swarms

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. © 2017 IEEE

Multi UAV coverage path planning in urban environments

This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems.Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696

PATH PLANNING STRATEGIES FOR VISIBILITY ENHANCEMENT WITH UNMANNED AERIAL VEHICLES IN CLUTTERED ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

3-D Coverage Path Planning for Underwater Terrain Mapping

This thesis presents an autonomous approach of 3-D coverage of underwater terrain using multi-level coverage trees. An autonomous underwater vehicle (AUV) equipped with multi-beam sonar sensors, Doppler velocity log (DVL) and inertial measurement unit (IMU) sensors is used to achieve this goal. The underwater 3-D search space is represented by a multi-level coverage tree which is generated online based on the obstacle information collected by the AUV. The nodes of the tree correspond to safe sub-areas for AUV navigation which are identified based on obstacle density in neighborhood of free cells. Standard tree traversal strategies like depth-first-search (DFS) and breath-first-search (BFS) are then used for visiting all the nodes of the tree thus securing complete coverage of the 3-D space. The terrain data collected by the AUV during tree coverage is used offline for the 3-D reconstruction of seabed using alpha shapes algorithm. The performance of this method is validated using a high-fidelity underwater simulator UWSim based on Robot Operating System (ROS). The simulations show that the proposed methodology achieves complete coverage and accurate reconstruction of 3-D underwater terrain

Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon