Navigable Area Detection and Perception-Guided Model Predictive Control for Autonomous Navigation in Narrow Waterways
Jonghwi Kim, Chan‐Gyu Lee, Dongha Chung, Jinwhan Kim
Abstract
This letter presents an integrated navigation and control strategy for an autonomous surface vehicle (ASV) to operate in narrow waterways without relying on GPS. The proposed method uses a camera and a light detection and ranging (LiDAR) sensor to detect navigable regions in the waterway. A deep learning-based semantic segmentation algorithm is applied to detect the navigable region in camera images, and the segmented region is projected onto the water surface using planar homography. A line-detection algorithm is also introduced to improve the reliability of detecting navigable regions from LiDAR measurements. A safe collision-free path for the ASV is generated within the navigable regions using model predictive control-based local path planning and control algorithms. The performance and practical utility of the proposed method were demonstrated through field experiments using a small cruise boat, modified as an autonomous surface vehicle.