Multiobjective path planner for UAVs based on genetic algorithms

This paper presents a path planner for Unmanned Air Vehicles (UAVs) based on Genetic Algorithms (GA) that obtains a feasible and optimal 3-D path for the UAV. It uses 9 different objective values which are calculated with a realistic model of the UAV and the environment and which are structured with 3 levels of priorities. Our planner works globally offline as well as locally online, which means that the algorithm can recalculate parts of the generated path in order to avoid unexpected risks. Finally, the effectiveness of the solutions given by this planner has been successfully tested against a simulator that contains the complete model of the UAV and the environment

A multiobjective optimization issue: genetic control planning or trajectories.

Part of a research project on cooperative marine robotics is the scenario of a submarine rendez-vous. This paper considers this case, where a high-manoeuvrability AUV (autonomous underwater vehicle) should meet a submarine platform for energy, samples and data service. Since the AUV is equipped with a set of thrusters, the problem of an adequate command of the thrusters appears. Given initial and final points for the AUV underwater trajectory, the question is to determine the set of forces and times to be exerted by the thrusters to get an adequate trajectory. Several constraints and simultaneous objectives to be optimized must be considered. Given the complexity of the multi-objective optimisation problem, it seems opportune to use Genetic Algorithms. The paper describes the problem to be solved, then explains how the GA were applied, and presents results for a set of cases considered, including obstacle avoidance

Global search metaheuristics for planning transportation of multiple petroleum products in a multi-pipeline system

The objective of this work is to develop several metaheuristic algorithms to improve the efficiency of the MILP algorithm used for planning transportation of multiple petroleum products in a multi-pipeline system. The problem involves planning the optimal sequence of products assigned to each new package pumped through each polyduct of the network in order to meet product demands at each destination node before the end of the planning horizon. All the proposed metaheuristics are combinations of improvement methods applied to solutions resulting from different construction heuristics. These improvements are performed by searching the neighborhoods generated around the current solution by different Global Search Metaheuristics: Multi-Start Search, Variable Neighborhood Search, Taboo Search and Simulated Annealing. Numerical examples are solved in order to show the performance of these metaheuristics against a standard commercial solver using MILP. Results demonstrate how these metaheuristics are able to reach better solutions in much lower computational time. (C) 2011 Elsevier Ltd. All rights reserved

A mathematical model for planning transportation of multiple petroleum products in a multi-pipeline system.

A multiproduct Pipeline provides an economic way to transport large Volumes of refined petroleum products over long distances. In such a pipeline, different products are pumped back-to-back without any separation device between them. Sometimes, multiproduct pipelines can be connected together, resulting in a more complex system commonly named multi-pipeline system. This paper proposes a new discrete mathematical approach to solve short-term operational planning Of multi-pipeline systems for refined products. This model is based on a discrete approach that divides both the planning horizon into time intervals of equal duration and the individual polyducts into packages of equal volume each containing a single product. Numerical examples are solved in order to show the performance of the proposed model. All the instances are implemented with the OPL modeling language running CPLEX as solver

Evolutionary trajectory planner for multiple UAVs in realistic scenarios

This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions

Entorno gráfico de modelado para problemas de optimización de sistemas a gran escala

En este trabajo se definen los formalismos necesarios para construir un entorno gráfico de modelado de problemas de optimización de sistemas a gran escala. Dado un problema modelado bajo esta orientación se generan automáticamente las bases de datos que albergarán la información del problema, datos ficticios para la realización de pruebas iniciales y los modelos de resolución, así como modelos de depuración en caso de que el resolutor no encuentre solución factible. Además se generan automáticamente editores gráficos que permitan visualizar gráficamente los datos del problema representado y sus soluciones, permitiendo modificar estos de forma cómoda. Finalmente estos formalismos se han utilizado para implementar un editor de problemas de optimización, que contemple las características mencionadas

Entorno Gráfico de Modelado para Problemas de Optimización de Sistemas a Gran Escala

[ES] En este trabajo se definen los formalismos necesarios para construir un entorno gráfico de modelado de problemas de optimización de sistemas a gran escala. Dado un problema modelado bajo esta orientación se generan automáticamente las bases de datos que albergarán la información del problema, datos ficticios para la realización de pruebas iniciales y los modelos de resolución, así como modelos de depuración en caso de que el resolutor no encuentre solución factible. Además se generan automáticamente editores gráficos que permitan visualizar gráficamente los datos del problema representado y sus soluciones, permitiendo modificar estos de forma cómoda. Finalmente estos formalismos se han utilizado para implementar un editor de problemas de optimización, que contemple las características mencionadas.Los autores quieren agradecer al Ministerio de Ciencia y Tecnología su financiación al proyecto en el que se basa EDIPO (CICYT PI2002-02924).Risco Martín, JL.; De La Cruz García, JM.; De Andrés Y Toro, B.; Herrán González, A. (2010). Entorno Gráfico de Modelado para Problemas de Optimización de Sistemas a Gran Escala. Revista Iberoamericana de Automática e Informática industrial. 2(4):89-100. http://hdl.handle.net/10251/146457OJS891002

Development of a control-oriented model of the vertical motions of a fast ferry

As a main part of a research study on the control of active flaps and a T-foil of a high-speed ferry, a control-oriented model of vertical motions of the ship has been developed. The objective of the control is to improve comfort, decreasing the impact of heave and pitch motions. We have experimental data from a towing tank institution and simulations with PRECAL. The model is based on a decomposition of the physic phenomena into two main aspects: the coupling of the ship with distance between waves and the dynamics of a semisubmerged mass. The model can be handled with MATLAB-SIMULINK, which is useful for studying control strategies. The model shows good agreement (model validation) with the experimental and simulated data for regular and irregular waves. The article shows a methodology, based on MATLAB tools, for obtaining control-oriented models from computer-aided design (CAD)-based programs. That means that the control-oriented model can be derived from the ship design, even before the ship is built