Research on coastal regions traditionally involves methods like manual sampling, monitoring buoys, and remote sensing, but these methods face challenges in spatially and temporally diverse regions of interest. Autonomous surface vehicles (ASVs) with artificial intelligence (AI) are being explored, and recognized by the International Maritime Organization (IMO) as vital for future ecosystem understanding. However, there is not yet a mature technology for autonomous environmental monitoring due to typically complex coastal situations: (1) many static (e.g., buoy, dock) and dynamic (e.g., boats) obstacles not compliant with the rules of the road (COLREGs); (2) uncharted or uncertain information (e.g., non-updated nautical chart); and (3) high-cost ASVs not accessible to the community and citizen science while resulting in technology illiteracy. To address the above challenges, my research involves both system and algorithmic development: (1) a robotic boat system for stable and reliable in-water monitoring, (2) maritime perception to detect and track obstacles (such as buoys, and boats), and (3) navigational decision-making with multiple-obstacle avoidance and multi-objective optimization.

翻译：传统上，海岸带研究依赖于人工采样、监测浮标和遥感等方法，但这些方法在时空异质性的关注区域面临挑战。搭载人工智能（AI）的自主水面艇（ASVs）正被探索用于此类任务，并被国际海事组织（IMO）视为未来理解生态系统的关键工具。然而，由于海岸环境通常复杂，目前尚未形成成熟的自主环境监测技术，其挑战主要在于：（1）存在大量静态（如浮标、码头）和动态（如船只）障碍物，且这些障碍物未必遵守海上避碰规则（COLREGs）；（2）海图信息未标注或不确定（如未更新的航海图）；（3）高成本的ASV难以被科研社区和公民科学项目获取，导致技术普及度低。为应对上述挑战，本研究从系统与算法两个层面展开：（1）开发用于稳定可靠水上监测的机器人艇系统；（2）实现海洋环境感知以检测与跟踪障碍物（如浮标、船只）；（3）开发具备多障碍物规避与多目标优化能力的导航决策方法。