This paper presents the development and field testing of multiple autonomous surface vehicles (ASVs) for autonomous cooperative navigation tasks in marine environments. The developed ASVs were designed in a catamaran hull form, and various systems related to autonomous functionalities, including the electrical propulsion, wireless communication, and guidance, navigation, and control (GNC) systems, were integrated with the situational awareness sensor system. Because not all of the navigation and situational awareness sensor data can be shared owing to the large network load, the network communication and data protocol structure was designed to enhance the data management efficiency. Furthermore, for cooperative navigation between multiple ASVs, the network communication system was synchronously shared with motion information through a robot operating system (ROS) multi-master system. In particular, an autonomy framework with cooperative navigation and control was designed to enable autonomous cooperative maneuvering with multiple ASVs in a global navigation satellite system (GNSS)-denied environment. Furthermore, a cooperative navigation and control approach based on relative geometric information between vehicles was implemented in the framework, which incorporated the capabilities of following a predefined path and maintaining a specific formation based on the relative distance between ASVs. Moreover, to demonstrate the essential maneuvering and functional capabilities of the cooperative navigation and control approach of the developed ASVs, preliminary field tests were conducted in an inland water environment, and the test results are discussed in this paper.

A path planning approach is proposed that is in accordance with Collision Regulations (COLREGS) for avoidance of a potential collision and considers the time‐varying trajectory uncertainties of an autonomous surface vehicle (ASV) and an obstacle. Unfortunately, as the motion information for each object includes various uncertainties caused by characteristics of the navigation and perception systems, the trajectory uncertainties increase linearly during the pre‐specified time in a trajectory prediction process. This trajectory uncertainty is preferentially modelled using the error covariance of a tracking filter designed for estimating motion information. Subsequently, collision risk is computed using a probabilistic method, and a collision risk zone (CRZ) is configured on the predicted trajectory. Finally, a COLREGS‐compliant path is planned with consideration of the dynamic characteristics of the ASV to avoid the CRZ. Simulation results that demonstrate the feasibility of the proposed approach with marine traffic examples are presented and discussed.

Acoustic source localization is used in many underwater applications. Acquiring an accurate directional angle for an acoustic source is crucial for source localization. To achieve this purpose, this paper presents a method for directional angle estimation of underwater acoustic sources using a marine vehicle. It is assumed that the vehicle is equipped with two hydrophones and that the acoustic source transmits a specific signal repeatedly. The proposed method provides a probabilistic model for time delay estimation. The probability is recursively updated by prediction and update steps. The prediction step performs a probability transition using the angular displacement of the marine vehicle. The predicted probability is updated using a generalized cross correlation function with a verification process using entropy measurement. The proposed method can provide a reliable and accurate estimation of the directional angles of underwater acoustic sources. Experimental results demonstrate good performance of the proposed probabilistic directional angle estimation method in both an inland water environment and a harbor environment.

Monitoring offshore infrastructure is a challenging task owing to the harsh ocean environment. To reduce human involvement in this task, this study proposes an autonomous surface vehicle (ASV)-based structural monitoring system for inspecting power cable lines under the ocean surface. The proposed ASV was equipped with multimodal sonar sensors, including a multibeam echosounder (MBES) and side-scan sonar (SSS) for mapping the seafloor, combined with a precisely estimated vehicle pose from navigation sensors. In particular, a globally consistent map was developed using the orthometric height as a vertical datum estimated based on the geoid height received from the GPS. Accordingly, the MBES and SSS generate a map of the target objects in the form of point clouds and sonar images, respectively. Dedicated outlier removal methods for MBES sensing were proposed to preserve the sparse inlier point cloud, and we applied the projection of the SSS image pixels to reflect the geometry of the seafloor. A field test was conducted in an ocean environment using real offshore cable lines to verify the efficiency of the proposed monitoring system.