Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling

Zamuda, Aleš; Sosa, José Daniel Hernández; Adler, Leonhard

doi:10.1016/j.asoc.2016.01.038

Cited by 69 publications

(36 citation statements)

References 81 publications

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“…These studies include testing different multiobjective optimization algorithms, as well as studying the impact of key parameters. Such systematic studies have recently illuminated the performance of evolutionary algorithms in underwater path planning [45,46], and we consider them an important step in further studies of inspection path planning.…”

Section: Resultsmentioning

confidence: 99%

Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

Ellefsen

Lepikson

Albiez

2017

Applied Soft Computing

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An important open problem in robotic planning is the autonomous generation of 3D inspection paths -that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously -and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together ensures a good balance between the two -both when 100% coverage is feasible, and when large parts of the object are hidden.

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Section: Resultsmentioning

confidence: 99%

Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

Ellefsen

Lepikson

Albiez

2017

Applied Soft Computing

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“…They also concluded that vehicles that can change their displacement speeds can reach the programmable locations by using favourable currents, saving energy for crossing areas with unfavourable conditions. UGPP challenge has also been tackled using evolutionary techniques, like in Zamuda et al works [13,14], including eddy sampling applications [15].…”

Section: Underwater Glider Path Planningmentioning

confidence: 99%

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Lucas

Hernández-Sosa

Greiner

et al. 2019

Sensors

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Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.

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“…Also, in the case of EU project RIVR (Upgrading National Research Structures in Slovenia) supported by European Regional Development Fund (ERDF), an important sideeffect of cHiPSet COST action was leveraging it's experts' inclusiveness to gain capacity recognition at a national ministry for co-financing HPC equipment 1 . In the view of future possibilities for modelling and simulation in CI context, gain from HPC is clearly seen in improving upon techniques with DE like in energy applications [148], constrained trajectory planning [149], artificial life of full ecosystems [150] including HPC-enabled evolutionary computer vision in 2D [151,152] and 3D [151], many other well recognized real-world optimization challenges [153], or even insight to deep inner dynamics of DE over full benchmarks, requiring large HPC capacities [154].…”

Section: Hpc-enabled Modelling and Simulation For Socio-economical Anmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling

Cited by 69 publications

References 81 publications

Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Why High-Performance Modelling and Simulation for Big Data Applications Matters

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