Work

Nonlinear Model Predictive Control With Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment

Year: 2023

Type: article

Abstract: In this article, we describe the development process of autonomous navigation capabilities of a small cruise boat operating in a canal environment and present the results of a field experiment conducted in the Pohang Canal, South Korea. Nonlinear model predictive control (NMPC) was used for the online trajectory planning and tracking control of the cruise boat in a narrow passage in the canal. To consider the nonlinear characteristics of boat dynamics, system identification was performed using experimental data from various test maneuvers, such as acceleration–deceleration and zigzag trials. To efficiently represent the obstacle structures in the canal environment, we parameterized the canal walls as line segments with point cloud data, captured by an onboard LiDAR sensor, and considered them as constraints for obstacle avoidance. The proposed method was implemented in a single NMPC layer, and its real-world performance was verified through experimental runs in the Pohang Canal.

Source: IEEE/ASME Transactions on Mechatronics

DOI: 10.1109/tmech.2023.3312672

Authors: Changyu Lee, Dongha Chung, Jonghwi Kim, Jinwhan Kim

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Ministry of Oceans and Fisheries