Periodic Reporting for period 1 - AUTOBarge (European training and research network on Autonomous Barges for Smart Inland Shipping)
Période du rapport: 2021-10-01 au 2023-09-30
According to the European Commission, passenger transport is projected to increase 42% by 2050, and freight transport up to 60%. Needless to say, this puts an enormous burden on transport networks and our environment. Compared to other modes of transport – which often face congestion and capacity problems – inland waterway transport is characterised by reliability, energy efficiency and a capacity for increased use. More than 37,000 km of waterways connect hundreds of cities and industrial regions in Europe. In the EU, 13 countries share an interconnected waterway network, highlighting the potential for increasing the modal share of inland waterway transport. This will not happen unless we can make inland waterways economically competitive. However, with crew costs accounting for 60% of the total cost, autonomous inland vessels represent an exciting disruptive technology.
AUTOBarge is about seizing an opportunity. Europe’s waterways are a vital resource that we have underused for most of the last century. Now, with the possibility for mass autonomous shipping, these canals and rivers offer a network that we can exploit without damaging the environment to the extent of new roads and aircraft runways. But to be able to do this we need new people with new skills. These innovators must be experts in remote control, monitoring, smart logistics, regulatory aspects, and many more areas associated with the complexity of inland shipping. The 15 early-stage researchers recruited to AUTObarge will begin this transport revolution.
AUTOBarge offers the first-ever training programme for the application of unmanned or autonomous vessels for smart inland shipping and their role in the overall multi-modal transport activity in Europe and has three scientific/technical objectives:
Objective 1 (WP1): Maximize the situational awareness (Sense and Understand) of an unmanned or autonomous inland vessel by covering the state and manoeuvrability of the vessel itself, the location and motion of other vessels, other relevant static and moving objects in the vicinity, features like buoys or traffic signs, as well as the wireless communication of information between the different waterway actors.
Objective 2 (WP2): Exploit the above situational awareness (Decide and Act) obtained for a safe, robust and energy-efficient path planning and motion control of the autonomous inland vessel with a focus on model predictive control, control methods supported by real-time machine learning, energy-efficiency, and fault identification and isolation schemes such that those do not affect the operation of autonomous vessels in a negative way.
Objective 3 (WP3): In-depth analysis of the socio-technical, economic and legal aspects that are needed to make autonomous inland shipping a success in the near future, including safety assurance, collaborative decision making for maximized performance, logistics, economic benefits, and the required changes to the regulatory framework.
AUTOBarge is about seizing an opportunity. Europe’s waterways are a vital resource that we have underused for most of the last century. Now, with the possibility for mass autonomous shipping, these canals and rivers offer a network that we can exploit without damaging the environment to the extent of new roads and aircraft runways. But to be able to do this we need new people with new skills. These innovators must be experts in remote control, monitoring, smart logistics, regulatory aspects, and many more areas associated with the complexity of inland shipping. The 15 early-stage researchers recruited to AUTObarge will begin this transport revolution.
AUTOBarge offers the first-ever training programme for the application of unmanned or autonomous vessels for smart inland shipping and their role in the overall multi-modal transport activity in Europe and has three scientific/technical objectives:
Objective 1 (WP1): Maximize the situational awareness (Sense and Understand) of an unmanned or autonomous inland vessel by covering the state and manoeuvrability of the vessel itself, the location and motion of other vessels, other relevant static and moving objects in the vicinity, features like buoys or traffic signs, as well as the wireless communication of information between the different waterway actors.
Objective 2 (WP2): Exploit the above situational awareness (Decide and Act) obtained for a safe, robust and energy-efficient path planning and motion control of the autonomous inland vessel with a focus on model predictive control, control methods supported by real-time machine learning, energy-efficiency, and fault identification and isolation schemes such that those do not affect the operation of autonomous vessels in a negative way.
Objective 3 (WP3): In-depth analysis of the socio-technical, economic and legal aspects that are needed to make autonomous inland shipping a success in the near future, including safety assurance, collaborative decision making for maximized performance, logistics, economic benefits, and the required changes to the regulatory framework.
ESR01 (Yan-Yun Zhang, KU Leuven) is developing a comprehensive online identification framework for ship maneuvering models, addressing various vessels and maneuvers, with progress in framework design and validation using virtual and real-world data.
ESR02 (Martin Baerveldt – NTNU) is developing algorithms combining Multiple Extended Object Tracking (MEOT) and Simultaneous Localization and Mapping (SLAM) using LiDAR data, focusing on enhancing MEOT for LiDAR and incorporating map data for localization.
ESR03 (Zhongbi Luo – KU Leuven) is developing an algorithm to integrate IENC data with multi-sensor fusion, optimizing for online localization without GNSS, and synchronizing real-time point cloud information to verify geographic data reliability for bridges.
ESR04 (Hoang Anh Tran – NTNU) is developing a distributed collision avoidance (COLAV) protocol for inland waterway ships, with a literature review, COLAV algorithm design, and ongoing theoretical development.
ESR05 (AmirReza H. Mojaveri – Periskal) is improving the Track Keeping Pilot in the AUTOBarge framework, focusing on Model Predictive Control (MPC) for obstacle avoidance and path following using geofencing constraints, with ongoing experiments and performance refinement.
ESR06 (Dhanika Mahipala – Kongsberg) is adapting the Scenario Based Model Predictive Control (SB-MPC) algorithm for autonomous collision avoidance in inland waterway vessels, with a literature review and plans to propose a novel algorithm for inland waterways.
ESR07 (Yuhan Chen – Chalmers) is creating a real-time autonomous ship navigation platform with multi-objective voyage optimization algorithms, emphasizing real-time local route planning and collision risk avoidance.
ESR08 (Chengqian Zhang– Chalmers) is developing a voyage planning system for energy optimization in autonomous barges, analyzing operational energy consumption and behaviors.
ESR09 (Abhishek Dhyani – TU Delft) is designing fault diagnosis algorithms for safe navigation of autonomous inland vessels, with ongoing collaboration on an online hazard/risk mitigation framework.
ESR10 (Yunjia Wang – KU Leuven) is evaluating vulnerabilities in real-time vision-based object detection for autonomous vessels, proposing insights for improved robustness, and introducing a methodology for dynamic safety assurance in machine learning models.
ESR11 (Rana Saha – Chalmers) is developing a theoretical framework for data collection and processing in smart autonomous inland shipping, conducting field studies and investigating barriers to automation in European inland waterways.
ESR12 (Lingyu Zhang – UHamburg) is assessing the impact of autonomous vessels on transportation choices, predicting inland waterway shipping demand, conducting industry surveys, and aiming to forecast the potential demand for autonomous vessels.
ESR13 (Dhaneswara Al Amien– Nord University) is developing an economic implication model for the adoption of autonomous inland shipping, progressing through literature review, primary data survey, and decision-making model development.
ESR14 (Sophie Orzechowski – IDIT) is addressing regulatory challenges in autonomous inland shipping, conducting a regulatory framework analysis and exploring solutions through a comparative analysis.
ESR15 (Camilla Domenighini – University of Antwerp) is assessing risk distribution in the autonomous inland shipping ecosystem, finding technology providers often limit liability while shipowners face mandatory liabilities, with challenges in proving negligence and compensating damages under different national legislations.
ESR02 (Martin Baerveldt – NTNU) is developing algorithms combining Multiple Extended Object Tracking (MEOT) and Simultaneous Localization and Mapping (SLAM) using LiDAR data, focusing on enhancing MEOT for LiDAR and incorporating map data for localization.
ESR03 (Zhongbi Luo – KU Leuven) is developing an algorithm to integrate IENC data with multi-sensor fusion, optimizing for online localization without GNSS, and synchronizing real-time point cloud information to verify geographic data reliability for bridges.
ESR04 (Hoang Anh Tran – NTNU) is developing a distributed collision avoidance (COLAV) protocol for inland waterway ships, with a literature review, COLAV algorithm design, and ongoing theoretical development.
ESR05 (AmirReza H. Mojaveri – Periskal) is improving the Track Keeping Pilot in the AUTOBarge framework, focusing on Model Predictive Control (MPC) for obstacle avoidance and path following using geofencing constraints, with ongoing experiments and performance refinement.
ESR06 (Dhanika Mahipala – Kongsberg) is adapting the Scenario Based Model Predictive Control (SB-MPC) algorithm for autonomous collision avoidance in inland waterway vessels, with a literature review and plans to propose a novel algorithm for inland waterways.
ESR07 (Yuhan Chen – Chalmers) is creating a real-time autonomous ship navigation platform with multi-objective voyage optimization algorithms, emphasizing real-time local route planning and collision risk avoidance.
ESR08 (Chengqian Zhang– Chalmers) is developing a voyage planning system for energy optimization in autonomous barges, analyzing operational energy consumption and behaviors.
ESR09 (Abhishek Dhyani – TU Delft) is designing fault diagnosis algorithms for safe navigation of autonomous inland vessels, with ongoing collaboration on an online hazard/risk mitigation framework.
ESR10 (Yunjia Wang – KU Leuven) is evaluating vulnerabilities in real-time vision-based object detection for autonomous vessels, proposing insights for improved robustness, and introducing a methodology for dynamic safety assurance in machine learning models.
ESR11 (Rana Saha – Chalmers) is developing a theoretical framework for data collection and processing in smart autonomous inland shipping, conducting field studies and investigating barriers to automation in European inland waterways.
ESR12 (Lingyu Zhang – UHamburg) is assessing the impact of autonomous vessels on transportation choices, predicting inland waterway shipping demand, conducting industry surveys, and aiming to forecast the potential demand for autonomous vessels.
ESR13 (Dhaneswara Al Amien– Nord University) is developing an economic implication model for the adoption of autonomous inland shipping, progressing through literature review, primary data survey, and decision-making model development.
ESR14 (Sophie Orzechowski – IDIT) is addressing regulatory challenges in autonomous inland shipping, conducting a regulatory framework analysis and exploring solutions through a comparative analysis.
ESR15 (Camilla Domenighini – University of Antwerp) is assessing risk distribution in the autonomous inland shipping ecosystem, finding technology providers often limit liability while shipowners face mandatory liabilities, with challenges in proving negligence and compensating damages under different national legislations.
At present, no commercial vessels operate at full autonomy although most of them are equipped with basic technologies for increased autonomy. Ongoing research projects (e.g. Watertruck+, Novimar, AUTOSHIP, ShippingLab, etc.) mainly focus on prototypes or the application of new technologies or processes to facilitate autonomy (5G, GNSS, radar, risk analysis…). Most effort is focused on autonomous shipping at sea. AUTOBarge does not aim at prototypes but rather wants to develop and integrate more advanced methodologies (MPC, lidar, SLAM, IENC, etc.) within the specific context of autonomous shipping on the European inland waterways. AUTOBarge deliberately focuses a large part of its research to the socio-technical, economic, and legal impact of autonomous shipping which are key skills for future experts in this field.