Research on diving control of underactuated UUV based on model predictive control with artificial bee colony algorithm

Zhang, Wei; Yi, Guo; Meng, Dawei; Liang, Zhicheng; Chen, Tao

doi:10.1109/chicc.2015.7260266

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…This is an important problem in the undersea domain, where unmanned systems are increasingly being used to manage potentially dangerous tasks and where group autonomy is of great interest to help facilitate large-scale tasking. There is a lot of focus on group autonomy using nature-inspired techniques, especially in the undersea domain [1], [2], [3], [4], [5]. These techniques are attractive because they have the benefit of being evaluated by the organisms using them in environments of interest.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Swarm Optimization: A teamwork-based approach to reduction of uncertainty

DeMedeiros

Wettergren

2016

OCEANS 2016 MTS/IEEE Monterey

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Abstract-We describe a new metaheuristic, called cooperative swarm optimization (CSO), which manages the efficient use of a group of unmanned agents performing cooperative operations. In this work we are concerned with the problem of group exploration of an uncertain environment. The CSO algorithm was developed to perform this exploration with limited interagent coordination and no central oversight. CSO is a swarm search algorithm that uses the mechanics of velocity to move each agent about the information space. In this way, CSO is similar to particle swarm optimization (PSO) however, CSO differs from PSO in how information is used from other agents and how the information is combined. In particular, the individual agent velocity is affected by the current agent information, the personal history of the agent, and the group's information and is weighted based on the entropy rate of change of the information space, allowing each agent to take into account the dynamic nature of the information space. New information is weighted highest, followed by changes to currently known information. Information that does not change is rated lowest. We illustrate the derivation of the algorithm and show numerical examples to demonstrate its effectiveness for use in cooperative unmanned underwater vehicle missions.

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