Robust Perception and Navigation of Autonomous Surface Vehicles in Challenging Environments

TL;DR

This paper presents a comprehensive system for autonomous surface vehicles that enhances perception and navigation in challenging coastal environments, addressing obstacles, uncertain data, and accessibility issues.

Contribution

It introduces a robotic boat system with advanced maritime perception and decision-making algorithms for obstacle avoidance and environment monitoring.

Findings

Effective obstacle detection and tracking in complex environments

Reliable autonomous navigation with multi-objective optimization

Potential for improved coastal environmental monitoring

Abstract

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.