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

TL;DR

This paper presents a nonlinear model predictive control approach for autonomous boat navigation in narrow canal environments, integrating obstacle avoidance with real-time trajectory planning validated through field experiments.

Contribution

It introduces a novel NMPC framework that incorporates obstacle constraints based on LiDAR data for autonomous boat navigation in complex canal settings.

Findings

Successful real-world navigation in Pohang Canal

Effective obstacle avoidance using LiDAR data

Validated control approach through field experiments

Abstract

In this paper, 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.