Architecture for planning and execution of missions with fleets of unmanned vehicles

Esta tesis presenta contribuciones en el campo de la planificación automática y la programación de tareas, la rama de la inteligencia artificial que se ocupa de la realización de estrategias o secuencias de acciones típicamente para su ejecución por parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún, incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar tareas complejas que requieren interacción física con el mundo real, los vehículos no tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares. En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para alcanzar una serie de objetivos específicos. La planificación con redes jerárquicas de tareas describe dominios utilizando una descripción que descompone conjuntos de tareas en subconjuntos más pequeños de subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar. Los problemas de enrutamiento de vehículos son una generalización del problema del viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador HTN clásico supone un gran reto. Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma de conexión entre un software especializado en la resolución del VRP con el planificador HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos la componente combinatoria del problema de enrutamiento de vehículos mediante un solucionador específico que se comunica con el planificador HTN y le suministra la información necesaria para continuar con la descomposición de tareas. El segundo método consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender de módulos de software externos. Llamamos a este modo el método acoplado. Con este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de búsqueda distinto del que se utiliza normalmente en planificadores de este tipo. Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se aplica una nueva forma de conexión entre dos planificadores independientes basada en un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la Comisión Europea y se presentan extensos resultados de simulación para comprobar la validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling, the branch of artificial intelligence that concerns the realization of strategies or action sequences typically for execution by unmanned vehicles, autonomous robots and/or intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect. The complexity of some tasks requires the cooperation among several agents. Moreover, even if the task is simple enough to be carried out by a single agent, cooperation can be used to decrease the overall cost of the operation. To perform complex tasks that require physical interaction with the real world, unmanned vehicles can be used as agents. In the last years a great variety of unmanned platforms, mainly vehicles that can be driven without a human on board, have been developed and used both in civil and military missions. This thesis deals with the application of Hierarchical Task Network (HTN) planning in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple heterogeneous unmanned vehicles that must cooperate to achieve specific goals. HTN planning describes problem domains using a description that decomposes set of tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be further decomposed and are supposed to be executable. The hierarchy resembles the way the humans reason about problems by decomposing them into sub-problems depending on the context and therefore tend to be easy to understand and design. Vehicle routing problems are a generalization of the travelling salesman problem (TSP). The TSP consists on finding the shortest path that connects all the cities from a list, starting and ending on the same city. The VRP consists on finding the set of minimal routes that cover all cities by using a specific number of vehicles. Both problems have a combinatorial nature, specially the VRP, that makes it very difficult to use a HTN planner in domains where these problems are present. Two approaches to use a HTN planner in domains involving the VRP have been tested. The first approach consists on a score-based optimization system that allows us to apply a new way of connecting a software specialized in the resolution of the VRP with the HTN planner. We call this the decoupled approach, as we tackle the combinatorial nature of the VRP by using a specialized solver that communicates with the HTN planner and provides all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without needing the use of an external software. We call this the coupled approach. For this reason, a new HTN planner that uses a different search algorithm from these commonly used in that type of planners has been developed and is presented in this work. This thesis presents new contributions in the field of hierarchical task network planning for the resolution of vehicle routing problem domains. A new way of connecting two independent planning systems based on a score calculation system that lets them cooperate in the optimization of the solutions is applied, and a new HTN planner that uses a different search algorithm from that usually used in other HTN planners is presented. These two methods are applied in civil missions in the framework of the ARCAS and AEROARMS Projects funded by the European Commission. Extensive simulation results are presented to test the validity of the two approaches

Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.Unión Europea ARCAS FP7-ICT-287617Unión Europea H2020-ICT-644271Unión europea H2020-ICT-73166

Velocity-Based Heuristic Evaluation for Path Planning and Vehicle Routing for Victim Assistance in Disaster Scenarios

Published in "Robot 2019: Fourth Iberian Robotics Conference. Advances in Intelligent Systems and Computing, Vol 1093. Silva M., Luís Lima J., Reis L., Sanfeliu A., Tardioli D. (eds)" published by Springer, Cham. Avalaible online at: https://doi.org/10.1007.987-3-030-36150-1_10Natural and human-made disasters require effective victim assistance and last-mile relief supply operations with teams of ground vehicles. In these applications, digital elevation models (DEM) can provide accurate knowledge for safe vehicle motion planning but grid representation results in very large search graphs. Furthermore, travel time, which becomes a crucial cost optimization criterion, may be affected by inclination and other challenging terrain characteristics. In this paper, our goal is to evaluate a search heuristic function based on anisotropic vehicle velocity restrictions for building the cost matrix required for multi-vehicle routing on natural terrain and disaster sites. The heuristic is applied to compute the fastest travel times between every pair of matrix elements by means of a path planning algorithm. The analysis is based on a case study on the ortophotographic-based DEM of natural terrain with different target points, where theUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work has received funding from the national project RTI2018-093421-B-I00 (Spanish Government), the University of Malaga (Andalucía Tech) and the grant BES-2016-077022 of the European Social Fund

Newer generations of multi-target CAR and STAb-T immunotherapeutics: NEXT CART Consortium as a cooperative effort to overcome current limitations

Adoptive T cellular immunotherapies have emerged as relevant approaches for treating cancer patients who have relapsed or become refractory (R/R) to traditional cancer treatments. Chimeric antigen receptor (CAR) T-cell therapy has improved survival in various hematological malignancies. However, significant limitations still impede the widespread adoption of these therapies in most cancers. To advance in this field, six research groups have created the “NEXT Generation CART MAD Consortium” (NEXT CART) in Madrid’s Community, which aims to develop novel cell-based immunotherapies for R/R and poor prognosis cancers. At NEXT CART, various basic and translational research groups and hospitals in Madrid concur to share and synergize their basic expertise in immunotherapy, gene therapy, and immunological synapse, and clinical expertise in pediatric and adult oncology. NEXT CART goal is to develop new cell engineering approaches and treatments for R/R adult and pediatric neoplasms to evaluate in multicenter clinical trials. Here, we discuss the current limitations of T cell-based therapies and introduce our perspective on future developments. Advancement opportunities include developing allogeneic products, optimizing CAR signaling domains, combining cellular immunotherapies, multi-targeting strategies, and improving tumor-infiltrating lymphocytes (TILs)/T cell receptor (TCR) therapy. Furthermore, basic studies aim to identify novel tumor targets, tumor molecules in the tumor microenvironment that impact CAR efficacy, and strategies to enhance the efficiency of the immunological synapse between immune and tumor cells. Our perspective of current cellular immunotherapy underscores the potential of these treatments while acknowledging the existing hurdles that demand innovative solutions to develop their potential for cancer treatment fully

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Description of the tree abundance data file (.xlsx

Data from: Beta diversity and oligarchic dominance in the tropical Forests of Southern Costa Rica

Recent studies have reported a consistent pattern of strong dominance of a small subset of tree species in Neotropical forests. These species have been called ‘hyperdominant’ at large geographical scales and ‘oligarchs’ at regional-landscape scales when being abundant and frequent. Forest community assembly is shaped by environmental factors and stochastic processes, but so far the contribution of oligarchic species to the variation of community composition (i.e. beta diversity) remains poorly known. To that end, we established 20 1-ha plots, i.e. 5 sites with 4 forest types (ridge, slope and ravine primary forest, and secondary forest) per site, in humid lowland tropical forests of southwestern Costa Rica to (1) investigate how community composition responds to differences in topography, successional stage and distance among plots for different groups of species (all, oligarch, common and rare/very rare species) and (2) identify oligarch species characterizing changes in community composition among forest types. From a total of 485 species of trees, lianas and palms recorded in this study only 27 species (i.e. 6%) were nominated as oligarch species. Oligarch species accounted for 37% of all recorded individuals and were present in at least half of the plots. Plant community composition significantly differed among forest types, thus contributing to beta diversity at the landscape-scale. Oligarch species was the component best explained by geographical and topographic variables, allowing a confident characterization of the beta diversity among tropical lowland forest stands

tree_abundance_data_GolfoDulce_BITR-18-091R2

Abundance (individuals per hectare) of tree and palm species in the Golfo Dulce region, Costa Ric