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

Branching to find feasible solutions in unmanned air vehicle mission planning

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10840-7_35Proceedings 15th International Conference, Salamanca, Spain, September 10-12, 2014.Mission Planning is a classical problem that has been traditionally studied in several cases from Robotics to Space missions. This kind of problems can be extremely difficult in real and dynamic scenarios. This paper provides a first analysis for mission planning to Unmanned Air Vehicles (UAVs), where sensors and other equipment of UAVs to perform a task are modelled based on Temporal Constraint Satisfaction Problems (TCSPs). 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. Using this simplified TCSP model, and a Branch and Bound (B&B) search algorithm, a set of feasible solutions will be found trying to minimize the fuel cost, flight time spent and the number of UAVs used in the mission. Finally, some experiments will be carried out to validate both the quality of the solutions found and the spent runtime to found them.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 In-Transit Vigilant Covering Tour Problem of Routing Unmanned Ground Vehicles

The routing of unmanned ground vehicles for the surveillance and protection of key installations is modeled as a new variant of the Covering Tour Problem (CTP). The CTP structure provides both the routing and target sensing components of the installation protection problem. Our variant is called the in-transit Vigilant Covering Tour Problem (VCTP) and considers not only the vertex cover but also the additional edge coverage capability of the unmanned ground vehicle while sensing in-transit between vertices. The VCTP is formulated as a Traveling Salesman Problem (TSP) with a dual set covering structure involving vertices and edges. An empirical study compares the performance of the VCTP against the CTP on test problems modified from standard benchmark TSP problems to apply to the VCTP. The VCTP performed generally better with shorter tour lengths but at higher computational cost

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

Constrained multi-objective optimization for multi-UAV planning

Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.Comment: Preprint of the article submitted and published in Journal of Ambient Intelligence and Humanized Computin

GAMPP: Genetic algorithm for UAV mission planning problems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25017-5_16Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (way-points) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.This work is supported by Comunidad Autónoma de Madrid under project CIBERDINE S2013/ICE-3095, Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P 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: José Insenser, César Castro and Gemma Blasco

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

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists