Author ORCID Identifier
0000-0002-9097-6343
Date of Award
Fall 10-1-2025
Document Type
Thesis (Ph.D.)
Department or Program
Computer Science
First Advisor
Alberto Quattrini Li
Abstract
The primary objective of this thesis is to develop and validate innovative and robust navigation methods for autonomous surface vehicles (ASVs) in challenging scenarios. These efforts aim to establish a complete autonomy pipeline for robotic decision-making systems, enabling high-level tasks such as environmental monitoring and autonomous transportation with broader impacts. The ocean economy contributes over 1.5 trillion USD annually, supporting diverse cultures and economies through tourism, fisheries, shipping, and renewable energy. The global marine industry handles over 90% of the world’s cargo transportation, underscoring its critical importance. Despite this significance, current maritime navigation relies heavily on human decision-making, which is prone to error under uncertain conditions. Recent advancements in autonomous mobility, including efforts by the International Maritime Organization (IMO) and technology companies, highlight the importance of developing autonomous systems for the marine domain. These systems promise reduced costs, improved safety, and enhanced efficiency. However, navigation safety and robustness remain major barriers to the widespread adoption of ASVs due to unstructured waterway conditions, vehicle dynamics, uncertain sensor information and intention, and ambiguous traffic regulations. Such challenges make common techniques from self-driving cars not directly applicable, motivating the need for aquatic-specific autonomy. This thesis addresses these challenges through contributions in two key areas: planning and perception. For planning, contributions include: (1) adaptive and proactive collision avoidance using risk-vector-based near-miss strategies; (2) multiple obstacle avoidance via holistic motion attribute-based clustering, theoretical analysis, and multi-objective optimization; (3) learning-augmented active collision avoidance with topological modeling of passing and intent-awareness, validated in real robot deployments; and (4) risk- and energy-aware global path planning across topologically distinct options under dynamic disturbances. For perception, contributions include: (5) an efficient LiDAR-based in-water obstacle detection framework for unknown aquatic environments that operates in real time onboard ASVs; and (6) the first multi-modal maritime dataset, publicly released to advance sensor fusion frameworks. The proposed holistic framework advances autonomy “in the wild” and serves as proof of concept using a custom ASV in real-world deployments.
Recommended Citation
Jeong, Mingi, "A Comprehensive Pipeline for Autonomous Surface Vehicle Navigation in Challenging Aquatic Environments" (2025). Dartmouth College Ph.D Dissertations. 430.
https://digitalcommons.dartmouth.edu/dissertations/430
