Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System

Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.Comment: Conditionally accpeted by TR

Online Informative Path Planning for Active Classification on UAVs

We propose an informative path planning (IPP) algorithm for active classification using an unmanned aerial vehicle (UAV), focusing on weed detection in precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We use a combination of global viewpoint selection and evolutionary optimization to refine the UAV's trajectory in continuous space while satisfying dynamic constraints. We validate our approach in simulation by comparing against standard "lawnmower" coverage, and study the effects of varying objectives and optimization strategies. We plan to evaluate our algorithm on a real platform in the immediate future.Comment: 7 pages, 4 figures, submission to International Symposium on Experimental Robotics 201

Unmanned Aerial Vehicle (UAV) mission planning based on Fast Marching Square (FM²) planner and Differential Evolution (DE)

Nowadays, mission planning for Unmanned Aerial Vehicles (UAVs) is a very attractive research field. UAVs have been a research focus for many purposes. In military and civil fields, the UAVs are very used for different missions. Many of these studies require a path planning to perform autonomous flights. Several problems related to the physical limitations of the UAV arise when the planning is carried out, as well as the maintenance of a fixed flight level with respect to the ground to capture videos or overlying images. This work presents an approach to plan missions for UAVs keeping a fixed flight level constraint. An approach is proposed to solve these problems and to generate effective paths in terms of smoothness and safety distance in two different types of environments: 1) 3D urban environments and 2) open field with non-uniform terrain environments. Many proposed activities to be carried out by UAVs in whatever the environment require a control over the altitude for different purposes: energy saving and minimization of costs are some of these objectives. In general terms, the planning is required to avoid all obstacles encountered in the environment and to maintain a fixed flight level during the path execution. For this reason, a mission planning requires robust planning methods. The method used in this work as planner is the Fast Marching Square (FM2) method, which generates a path free of obstacles. As a novelty, the method proposed includes two adjustment parameters. Depending on the values of these parameters, the restriction of flight level can be modified, as well as the smoothness and safety margins from the obstacles of the generated paths. The Dubins airplane model is used to check if the path resulting from the FM2 is feasible according to the constraints of the UAV: its turning rate, climb rate and cruise speed. Besides, this research also presents a novel approach for missions of Coverage Path Planning (CPP) carried out by UAVs in 3D environments. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search or rescue tasks. The methodology followed uses an optimization process based on the Differential Evolution (DE) algorithm in combination with the FM2 planner. Finally, the UAVs formation problem is introduced and addressed in a first stage using the planner proposed in this thesis. A wide variety of simulated experiments have been carried out to illustrate the efficiency and robustness of the approaches presented, obtaining successful results in different urban and open field 3D environments.Hoy en día la planificación de misiones para vehículos aéreos no tripulados (UAV) es un campo de investigación muy atractivo. Los UAV son foco de investigación en numerosas aplicaciones, tanto en el campo civil como militar. Muchas de estas aplicaciones requieren de un sistema de planificación de ruta que permita realizar vuelos autónomos y afrontar problemas relacionados con las limitaciones físicas del UAV y con requerimientos como el nivel de vuelo sobre el suelo para, entre otras funciones, poder capturar videos o imágenes. Este trabajo presenta una propuesta de planificador para vehículos aéreos no tripulados que permite resolver los problemas citados previamente, incluyendo en la planificación las consideraciones cinemáticas del UAV y las restricciones de nivel de vuelo, generando rutas suaves, realizables y suficientemente seguras para dos tipos diferentes de entornos 3D: 1) entornos urbanos y 2) campos abiertos con terrenos no uniformes. El método utilizado en esta tesis como base para la planificación es el método Fast Marching Square (FM2), que genera un camino libre de obstáculos. Como novedad, el método propuesto incluye dos parámetros de ajuste. Dependiendo de los valores de estos parámetros, se puede modificar la restricción de nivel de vuelo, así como la suavidad y los márgenes de seguridad respecto a los obstáculos de las rutas generadas. El modelo cinemático de Dubins se utiliza para verificar si la ruta resultante de nuestro planificador es realizable de acuerdo con las restricciones del UAV: su velocidad de giro, velocidad de ascenso y velocidad de crucero. Además, esta tesis también presenta una propuesta novedosa para la planificación de misiones de Coverage Path Planning (CPP) en entornos 3D. Estas misiones se centran en la planificación de rutas para cubrir un área determinada de un entorno con el fin de llevar a cabo tareas de rastreo, búsqueda o rescate. La metodología seguida utiliza un proceso de optimización basado en el algoritmo Differential Evolution (DE) en combinación con nuestro planificador FM2. Como parte final de la tesis, el problema de formación de UAVs se introduce y aborda en una primera etapa utilizando el planificador FM2 propuesto.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Antonio Giménez Fernández.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Raúl Suárez Feijó