A simple CSP-based model for unmanned air vehicle mission planning

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. C. Ramírez-Atencia, G. Bello-Orgaz, M. D. R.-Moreno, and D. Camacho, "A simple CSP-based model for Unmanned Air Vehicle Mission Planning", in 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014, pp. 146 - 153The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.This work is supported by the Spanish Ministry of Science and Education under Project Code TIN2010-19872 and Savier Project (Airbus Defence & Space, FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: Jose Insenser, C ´ esar Castro and ´ Gemma Blasco

Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms

Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.Comment: This is a preprint version of the article submitted and published in Soft Computin

Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems

This Master Thesis provides a first analysis of mission planning for Unmanned Air Vehicles (UAVs), dealing with multiple UAVs that must perform one or more tasks in a set of waypoints and specific time windows. The solution plans obtained should fulfill all the constraints given by the different components and capabilities of the UAVs involved over the time periods given. Therefore a Temporal Constraint Satisfaction Problem (TCSP) representation is needed. In a first approach, a temporal constraint model is implemented and tested by performing Backtracking (BT) search in several missions. In this model, a set of resources and temporal constraints are designed to represent the main characteristics (task time, fuel consumption, ...) of this kind of aircrafts. On the other hand, BT algorithm is used to look through the whole solutions space to measure the scalability of the problem. In a second approach, we consider a Constraint Satisfaction Optimization Problem (CSOP) with an optimization function to minimize the fuel cost, the flight time and the number of UAVs needed; and Branch & Bound (B&B) search is employed for solving this CSOP model. Finally, some experiments will be carried out to validate both the quality of the solutions found and the runtime spent to found them.El presente proyecto final de máster muestra un primer análisis sobre planificación de misiones para Vehículos Aéreos no tripulados (UAVs), donde se trata con múltiples UAVs que deben realizar una o más tareas en un conjunto de puntos o waypoints y en una ventana temporal específica. Los planes obtenidos como solución deben cumplir todas las restricciones dadas por los diferentes componentes y capacidades de los UAVs involucrados en un periodo de tiempo dado. Por tanto, se precisa de una representación del problema como un Problema de Satisfacción de Restricciones Temporales (TCSP). En una primera aproximación, se implementa un modelo de restricciones temporales y se testea ejecutando una búsqueda Backtracking (BT) cronológico en varias misiones. En este modelo, se diseñan un conjunto de restricciones temporales y de recursos para representar las principales características (tiempo de la tarea, consumo de combustible, ...) de este tipo de aviones. Por otro lado, el algoritmo BT es usado para examinar todo el espacio de soluciones para medir la escalabilidad del problema. En una segunda aproximación, consideramos un Problema de Optimización de Satisfacción de Restricciones (CSOP) con una función de optimización que minimice el coste de combustible, el tiempo de vuelo y el número de UAVs necesarios; y se utiliza Branch & Bound (B&B) para resolver este modelo de CSOP. Finalmente, se realizarán algunos experimentos para validar tanto la calidad de las soluciones encontradas como el tiempo de ejecución gastado en su búsqueda