TwinPort Integrates Digital Twin and Drones for Precise Ship Maneuvering

By Dr. Sampath LonkaAug 1 2023

In a paper published in the journal Scientific Reports, researchers proposed a cutting-edge architectural concept called TwinPort, which seamlessly integrates digital twin technology and drone-assisted data collection. This approach aims to achieve precise ship maneuvering in congested ports while ensuring optimal navigation during the docking process by incorporating a recommendation engine.

Background

The world's seaports play a fundamental role in the global economy, and the trend toward automation is steadily gaining momentum. This has led to the emergence of "smart ports," a rapidly growing industry with a projected market value of 5.7 billion USD by 2027. Efficient ship maneuvering within busy port zones is critical to avoiding costly delays, accidents, and environmental damage.

Two promising technologies, digital twin (DT) and drone-assisted data collection, can potentially revolutionize this field. DT enables real-time monitoring and predictive maintenance through digital replicas, while drones offer cost-effective and flexible data collection, especially in remote areas, thereby improving wireless communication capabilities. By mitigating collisions, reducing carbon emissions, and minimizing environmental impact, these technologies are deemed essential for the future success of smart port operations.

Related work

Numerous studies explore drones for data collection. Caruso et al. focus on drone-sensor proximity in precision agriculture; Wei et al. investigate UAVs for IoT data collection; Yuan et al. optimize UAV trajectories; and Liu et al. plan UAV trajectories for environmental monitoring. In the maritime sector, Liang et al. study multi-UAV marine IoT systems; Chapapria et al. develop coastal monitoring with UAVs and computer vision; and another study introduces a multi-UAV maritime communication paradigm. DT applications also gain attention, including security analysis and DT-assisted honeypots for smart seaports, estimating speed loss, tracking ship life cycles, and enabling automated ship maneuvering.

Proposed model

The present research begins with a comprehensive examination of how DT technology impacts a ship's path. Comparing sensor-only trajectories to those combined with DT, the latter enables the ship to finish the track one minute earlier, covering a shorter distance and achieving significant fuel savings. The proposed three-layered DT architecture for smart seaports aligns with the Gemini Principle, prioritizing quality, evolution, and insight values. It consists of the Data Layer with real-time data from IoT devices, the Twin Layer with digital replicas for modeling, and the Service Layer offering smart seaport applications for insights and predictions, optimizing port performance, and reducing carbon emissions.

Drone-assisted data collection in smart seaports presents numerous advantages, including improved safety, cost savings, efficiency, and real-time data acquisition. It holds potential for transforming ship maneuvering processes, particularly during container ship entry and departure. Integrating drone-assisted data collection into the TwinPort framework enhances precision and efficiency during ship maneuvering.

The drone-assisted data collection network includes sensors, a drone, a base station, a control station, and a server. Sensors detect the environment and transmit data to the drone, which collects and sends it to the server. The control station optimizes the drone's flight path, while the base station receives sensing data. The server stores and forwards the data to the connectivity layer.

To optimize data collection, drone altitude adheres to regulations, with a maximum limit of 400 feet near seaports based on sensor types. The drone's flight path follows the Hilbert curve path planning algorithm to ensure precise ship maneuvering and effective data gathering.

A fundamental data collection protocol using IEEE 802.11p facilitates seamless communication between ships and drones, progressing through various states to guarantee efficient and accurate data gathering for precise ship navigation.

Communication between drones is enabled through the Automatic Neighbor Relation (ANR) mechanism, inspired by the Self-Organizing Network (SON) architecture. ANR ensures smooth data exchange, eliminates redundancy, and enhances overall network performance. Drone registration includes ship details, and ANR proactively verifies neighboring drones' registration lists to prevent duplicate data collection and manage new routes effectively. The communication process involves neighbor drone discovery, intersect-free data collection zones, communication link establishment, data collection, updating registry lists, registry list exchange, neighbor registry checks, and periodic list management. Using SON architecture and ANR mechanisms optimizes data collection, enhancing efficiency and reliability in ship monitoring and maneuvering.

Model evaluation

In the present study, researchers propose a recommended engine for ship maneuvering in ports. The engine utilizes twin-layer data to construct a modular mathematical ship model, thereby optimizing paths considering port slots and ship features. The engine calculates desired and actual paths while specifying precise maneuvering parameters like heel and drift angles. These recommendations are then presented to a master mariner, who can either accept or reject them.

To execute the maneuvering model, the researchers employ the MMG model, incorporating surge, sway forces, and yaw moment equations, and adopt PID control for waypoint navigation. This data-driven approach optimizes port resource utilization, prevents route duplications, and enhances ship maneuvering efficiency. Experimental results demonstrate improved trajectory performance, reduced financial costs, and minimized fuel consumption, further enhancing the benefits of smart seaports and maritime operations.

Conclusion

In summary, precise ship navigation is crucial in congested seaport areas to avoid delays, collisions, and potential hazards. The proposed TwinPort architecture, integrating DT technology and drone-assisted data collection, offers a solution for precise ship maneuvering in smart seaports. The recommendation engine facilitates accurate ship navigation during docking. Experimental results demonstrate improved trajectory performance, reduced financial expenditures, minimized fuel consumption, and lowered carbon emissions, promoting environmental protection.