...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace science and technology >A hybrid genetic approach for airborne sensor vehicle routing in real-time reconnaissance missions
【24h】

A hybrid genetic approach for airborne sensor vehicle routing in real-time reconnaissance missions

机译：实时侦察任务中机载传感器车辆路由的混合遗传方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Past initiatives to address surveillance and reconnaissance mission planning mainly focused on low-level control aspects such as real-time path planning and collision avoidance algorithms in limited environment. However, few efforts have been spent on high-level real-time task allocation. It is believed that automated decision capabilities supporting real-time resource allocation for sensor control and interactions might significantly reduce user workload, focusing attention on alternate tasks and objectives while assigning hard computational tasks to artificial agents. In this paper, we propose a new hybrid genetic algorithm to solve the dynamic vehicle routing problem with time windows, in which a group of airborne sensors are engaged in a reconnaissance mission evolving in a dynamic uncertain environment involving known and unknown targets/threats. In that context, visiting a target may consist in carrying out a collection of subtasks such as search, detect, recognize and confirm suspected targets, discover and confirm new ones. The approach consists in concurrently evolving two populations of solutions to minimize total travel time and temporal constraint violation using genetic operators combining variations of key concepts inspired from routing techniques and search strategies. A least commitment principle in servicing scheduled customers is also exploited to potentially improve solution quality.
机译：过去解决监视和侦察任务计划的计划主要集中在低级控制方面，例如在有限环境中的实时路径计划和避免碰撞算法。但是，很少有精力用于高级实时任务分配。据信，支持用于传感器控制和交互的实时资源分配的自动决策功能可能会大大减少用户的工作量，将注意力集中在替代任务和目标上，同时将硬计算任务分配给人工代理。在本文中，我们提出了一种新的混合遗传算法来解决带有时间窗的动态车辆路径问题，其中一组机载传感器参与侦察任务，该侦察任务在涉及已知和未知目标/威胁的动态不确定环境中发展。在这种情况下，访问目标可能包括执行子任务的集合，例如搜索，检测，识别和确认可疑目标，发现并确认新目标。该方法包括同时发展两个解决方案群，以利用遗传算子结合路由技术和搜索策略启发而来的关键概念的变化，以最大程度地减少总旅行时间和违反时间约束的情况。还利用服务承诺客户的最小承诺原则来潜在地提高解决方案质量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号