On-line Learning with Evolutionary Algorithms towards Adaptation of Underwater Vehicle Missions to Dynamic Ocean Environments
On-line Learning with Evolutionary Algorithms towards Adaptation of Underwater Vehicle Missions to Dynamic Ocean Environments
复制标题
使用进化算法在线学习使水下航行器任务适应动态海洋环境
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
M. Seto
中科院分区：
文献类型：
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作者：
M. Seto
自主水下航行器（AUV）的任务是更长时间的部署，因此能源管理问题是及时和相关的。能源短缺可能是由于动态海洋条件在时间和空间上以不可预测的方式变化而发生的。水下通信挑战加剧了这一问题。建议，是一个正在进行的能源评估，评估AUV的能力，以完成使命通过代理，认为AUV在线状态，非线性动力学，最近学到的历史，和过去的历史项目的能源短缺。当发生短缺时，基于知识的机载代理重新计划的AUV调查使命使用在线学习与遗传算法给定的能量预算，使命持续时间，和剩余的调查区域尺寸。经验证的代理人是特别有效的情况下，研究的能源短缺所造成的调查面积增加了2倍，2倍的能源下降。一个代理，有效地监测和重新规划最佳的任务与能源的考虑，特别是侧扫声纳，是相当新颖的，并增加了长期部署的AUV的操作选项。
Autonomous underwater vehicles (AUV) are tasked to ever longer deployments so energy management issues are timely and relevant. Energy shortages can occur due to dynamic ocean conditions that vary temporally and spatially in unpredictable ways. This is compounded by underwater communication challenges. Proposed, is an on-going energy evaluation that assesses the AUV ability to complete the mission through an agent that considers the AUV on-line states, non-linear dynamics, recent learned history, and past history to project an energy shortage. When a shortage occurs an onboard knowledge-based agent re-plans the AUV survey mission using on-line learning with a genetic algorithm given the energy budget, mission duration, and the remaining survey area dimensions. The validated agent is especially effective in the case studied for an energy shortfall resulting from increasing the surveyed area by a factor of 2, for a factor of 2 drop in energy. An agent that effectively monitors and re-plans optimal missions with energy considerations, especially for side scan sonars, is quite novel and increases the operational options of AUVs on long deployments.