Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

Development of a Towed Underwater Platform That Can Operate in a Marine Environment and Explore the Sea Bottom

Owing to environmental constraints, it is challenging to stably conduct various missions or surveys of the seabed for a prolonged period in the marine environment. To address this challenge, several devices and technologies are being developed. In this study, we aimed to develop an unmanned underwater vehicle (UUV)—specifically, a towed underwater platform—that can be loaded and unloaded via joint operation with an unmanned surface vehicle, which can be connected to a wired cable to obtain a stable power supply and high-speed communication. In addition, various sensors for detection are employed to investigate the marine environment and conduct missions. Furthermore, we operated the developed UUV in actual waters, reviewed the results, and examined its practical operability.

Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)

Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping.

Changes of seagrass area in Beralas Pasir and Beralas Bakau Island observed from Sentinel-2 Satellite and verified by Unmanned Surface Vehicle (USV)

Today, the area of seagrass ecosystems in Indonesia is estimated to have shrunk significantly. Bintan Island has quite a large seagrass ecosystems area. Along with the development of satellite technology, monitoring of conditions and changes to a coastal ecosystem can be carried out effectively through remote sensing technology. One satellite image that is relatively new and has good spatial quality is Sentinel-2 with a spatial resolution value of 10×10 m2 / pixel. Field data retrieval is facilitated by the use of Unmanned Surface Vehicle (USV). This research went through several stages such as image pre-processing, water column correction, masking, unsupervised classification, and detection of changes of seagrass area. The data obtained from the USV becomes the data for the accuracy-test in the supervised classification. Seagrass area was obtained in Beralas Pasir and Beralas Bakau Island is 84.27 ha (2016), 81.3 ha (2019) and 77.4 ha (2021). Detection of seagrass to non-seagrass area changes resulting 31.35 ha (2016-2019) and 30.91 ha (2019-2021). On the other hand non-seagrass to seagrass area is 24.84 ha (2016-2019) and 27.98 ha (2019-2021). The accuracy test of 2019 image classification and Unmanned Surface Vehicle data resulting overall accuracy at 62.20%.