UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimizer

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
DongSeop Lee ◽  
Jacques Periaux ◽  
Luis Felipe Gonzalez
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Planning System ◽  
Design Quality ◽  
Start Position ◽  
Multi Objective ◽  
Spline Curves

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimiser

2009 ◽  
Author(s):  
DongSeop Lee ◽  
Jacques Periaux ◽  
Luis Felipe Gonzalez
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Planning System ◽  
Unmanned Aerial Systems ◽  
Three Dimension ◽  
Design Quality ◽  
Nsga Ii ◽  
Start Position ◽  
Multi Objective

This paper presents the application of advanced optimization techniques to Unmanned Aerial Systems (UAS) Mission Path Planning System (MPPS) using Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and a Hybrid Game strategy are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The resulting trajectories on a three-dimension terrain are collision-free and are represented by using Be´zier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a Hybrid-Game strategy to a MOEA and for a MPPS.

Three Dimensional Robot Path Planning With Workspace Considerations

1992 ◽  
Author(s):  
C. Y. Liu ◽  
R. W. Mayne
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Recursive Quadratic Programming ◽  
Six Degree Of Freedom ◽  
Nonlinear Programming Method ◽  
Path Planning Problem ◽  
Robot Path

Abstract This paper considers the problem of robot path planning by optimization methods. It focuses on the use of recursive quadratic programming (RQP) for the optimization process and presents a formulation of the three dimensional path planning problem developed for compatibility with the RQP selling. An approach 10 distance-to-contact and interference calculations appropriate for RQP is described as well as a strategy for gradient computations which are critical to applying any efficient nonlinear programming method. Symbolic computation has been used for general six degree-of-freedom transformations of the robot links and to provide analytical derivative expressions. Example problems in path planning are presented for a simple 3-D robot. One example includes adjustments in geometry and introduces the concept of integrating 3-D path planning with geometric design.

Single and Multi Objective CFD Optimization of Horizontal Axis Wind Turbines

2011 ◽  
Author(s):  
Fabio De Bellis ◽  
Luciano A. Catalano
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Blades ◽  
Computational Cost ◽  
Three Dimensional ◽  
Steam Turbines ◽  
Wind Turbine Blades ◽  
Multi Objective ◽  
Horizontal Axis Wind Turbines ◽  
Cfd Optimization

In spite of the enlarging interest in wind turbines development, the design optimization of wind turbine blades has not been studied in the past as gas or steam turbines optimization. Due to its reduced computational cost, Blade Element Momentum (BEM) method has been employed up to now to estimate the power output of the turbine. However, BEM method is not able to predict complex three dimensional flow fields or the performance of profiles for which drag and lift coefficients are not available. Theoretically, Computational Fluid Dynamics (CFD) can be more useful in these cases, but at the price of a much higher overall computational cost. In a past work, the authors developed and validated a simplified CFD process (including meshing) capable to assess the aerodynamic loads acting on a wind turbine with acceptable computational resources. Starting from that, in this work a full 3D CFD optimization of a small wind turbine is presented, both with constrained single- and multi-objective. Twist and chord distributions of a single blade have been varied keeping fixed the aerodynamic profile, and the obtained optimums have been compared with a benchmark case. The results demonstrate that CFD optimization can be effectively employed in a wind turbine optimization. As expected, stalled conditions of the blade are more likely to be improved than those characterized by attached flow. Future works will focus on multi-disciplinary optimization and will include also aerodynamic profile variation.

An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 15-37 ◽  
Author(s):  
Muneendra Ojha ◽  
Krishna Pratap Singh ◽  
Pavan Chakraborty ◽  
Shekhar Verma
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Front ◽  
Optimization Problem ◽  
Computational Cost ◽  
Optimization Methods ◽  
Objective Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Multi Objective

Multi-objective optimization algorithms using evolutionary optimization methods have shown strength in solving various problems using several techniques for producing uniformly distributed set of solutions. In this article, a framework is presented to solve the multi-objective optimization problem which implements a novel normalized dominance operator (ND) with the Pareto dominance concept. The proposed method has a lesser computational cost as compared to crowding-distance-based algorithms and better convergence. A parallel external elitist archive is used which enhances spread of solutions across the Pareto front. The proposed algorithm is applied to a number of benchmark multi-objective test problems with up to 10 objectives and compared with widely accepted aggregation-based techniques. Experiments produce a consistently good performance when applied to different recombination operators. Results have further been compared with other established methods to prove effective convergence and scalability.

On the Coupling of Inverse Design and Optimization Techniques for the Multiobjective, Multipoint Design of Turbomachinery Blades

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Duccio Bonaiuti ◽  
Mehrdad Zangeneh
Keyword(s):  
Design Method ◽  
Computational Cost ◽  
Three Dimensional ◽  
Design Strategy ◽  
Design Guidelines ◽  
Optimization Techniques ◽  
Inverse Design ◽  
Design Parameters ◽  
Turbomachinery Blades ◽  
The Impact

Automatic optimization techniques have been used in recent years for the aerodynamic and mechanical design of turbomachine components. Despite the many advantages, their use is usually limited to simple applications in industrial practice, because of their high computational cost. In this paper, an optimization strategy is presented, which enables the three-dimensional multipoint, multiobjective aerodynamic optimization of turbomachinery blades in a time frame compatible with industrial standards. The design strategy is based on the coupling of three-dimensional inverse design, response surface method, multiobjective evolutionary algorithms, and computational fluid dynamics analyses. The blade parametrization is performed by means of a three-dimensional inverse design method, where aerodynamic parameters, such as the blade loading, are used to describe the blade shape. Such a parametrization allows for a direct control of the aerodynamic flow field and performance, leading to a major advantage in the optimization process. The design method was applied to the redesign of a centrifugal and of an axial compressor stage. The two examples confirmed the validity of the design strategy to perform the three-dimensional optimization of turbomachine components, accounting for both design and off-design performance, in a time-efficient manner. The coupling of response functions and inverse design parametrization also allowed for an easy sensitivity analysis of the impact of the design parameters on the performance ones, contributing to the development of design guidelines that can be exploited for similar design applications.

scholarly journals Metamodel-based multidisciplinary design optimization methods for aerospace system

Astrodynamics ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 185-215
Author(s):  
Renhe Shi ◽  
Teng Long ◽  
Nianhui Ye ◽  
Yufei Wu ◽  
Zhao Wei ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Computational Cost ◽  
Optimization Methods ◽  
Problem Formulation ◽  
Black Box ◽  
Optimization Techniques ◽  
Multidisciplinary Optimization ◽  
Multidisciplinary Design ◽  
Aerospace Systems

AbstractThe design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization

2006 ◽  
Author(s):  
Mian Li ◽  
Genzi Li ◽  
Shapour Azarm
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Pareto Frontier ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Main Challenge ◽  
Engineering Design Problems

The high computational cost of population based optimization methods, such as multi-objective genetic algorithms, has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of computationally intensive simulation (objective/constraint functions) calls. We present a new multi-objective design optimization approach in that kriging-based metamodeling is embedded within a multi-objective genetic algorithm. The approach is called Kriging assisted Multi-Objective Genetic Algorithm, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points or individuals are evaluated by kriging metamodels, which are computationally inexpensive, instead of the simulation. The decision as to whether the simulation or their kriging metamodels to be used for evaluating an individual is based on checking a simple condition. That is, it is determined whether by using the kriging metamodels for an individual the non-dominated set in the current generation is changed. If this set is changed, then the simulation is used for evaluating the individual; otherwise, the corresponding kriging metamodels are used. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average, K-MOGA converges to the Pareto frontier with about 50% fewer number of simulation calls compared to a conventional MOGA.

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