  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.
