New approaches to intelligent navigation of autonomous surface and underwater robotic systems

自主水面和水下机器人系统智能导航的新方法

• 批准号: RGPIN-2015-05959
• 负责人: Yang, Simon
• 金额: $ 2.7万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590747
• 关键词: New; approaches; intelligent; navigation; autonomous

Unmanned surface robots (USR) and unmanned underwater robots (UUR) have many important applications such as security surveillance, environmental monitoring and exploration in rivers, lakes and seas, particularly in unstructured and hazardous environments. Real-time sensing, localization, and navigation are fundamentally important issues for autonomous surface robots (ASR) and autonomous underwater robots (AUR). The key challenge is to infuse these robots with “human-like” intelligence that will enable safe autonomous navigation and successful completion of designated tasks.

无人水面机器人（USR）和无人水下机器人（UUR）具有许多重要的应用，例如河流、湖泊和海洋的安全监控、环境监测和勘探，特别是在非结构化和危险环境中的实时传感、定位和导航。自主水面机器人（ASR）和自主水下机器人（AUR）面临的根本性重要挑战是为这些机器人注入“类人”智能，以实现安全自主导航并成功完成指定任务。