Advancing Marine Systems: Ship Deck Equipment

这项研究计划的任务是通过发展机器人甲板机械来提高海上船只的安全性。为了在海上货物转移过程中提高操作可用性和人员福利，该研究计划侧重于启用可用于补偿所有轴上的相对运动的技术。由于物体之间的相对运动，在海上货物转运以及发射和回收作业期间出现了危险的挑战。该计划采取系统级的方法来解决这个问题，以在以下方面取得进展：控制算法；系统建模；机器视觉；机器人学；以及机器人海洋甲板机械的机械设计。拟议的NSERC-DG将专注于船载起重机和绞车的运动补偿系统。该计划的愿景是自主可靠地将任何载荷或物体从空中、海面或其他船只发射、回收或转移到主船，而不受天气条件或相对运动的影响。为了推进这一愿景，拟议的NSERC DG将专注于开发一种新的传感系统，以识别和跟踪海洋中移动的船只上的任意物体。由于环境的极端动态，建议的系统将是一套传感器，并将利用我们之前的传感器融合和信号预测算法。这项工作的结果将产生一种新的传感器套件和传感器融合算法，该算法可用于海上发射和回收以及其他自动驾驶车辆的使用。此外，拟议的研究将使用新型传感器系统的输出为基于起重机的运动补偿系统(MCS)提供输入。拟议的研究将通过删除几个常见的假设，如刚性连杆、线性摩擦和可以忽略的吊船相互作用，来扩展我们以前开发的MCS。这项工作还将开发一种适合于集成到实时控制回路中的起重机MCS的新型实时数字双胞胎。这对数字双胞胎还将用于研究和减轻来自起重机-船舶相互作用的干扰和可能的正反馈环路的有害影响。该计划将培养12名高素质人才(HQP)：3名博士、4名硕士和5名理科硕士。我们的研究将有助于提高价值50亿美元的加拿大海运业的安全性，满足加拿大政府改善海洋安全的承诺，并为加拿大价值350亿美元的国家造船战略提供技术。HQP还将拥有核心技能，为加拿大海洋市场做出贡献，并在未来20年为海上电力领域估计新增9.1万个工作岗位。

The mission of this research program is to increase safety on maritime vessels by advancing robotic deck-machinery. To increase operational availability and personnel welfare during maritime load transfers, the research program focuses on enabling technologies that can be used to compensate for the relative motion on all axes. Due to the relative motion between the bodies, dangerous challenges arise during cargo transfers while at sea and during launch and recovery operations. The program takes a systems level approach to the problem to make advances in: control algorithms; system modelling; machine vision; robotics; and mechanical design of robotic marine deck machinery. The proposed NSERC-DG will focus on motion compensation systems for ship mounted cranes and winches. The vision of the program is to autonomously & reliably launch, recover, or transfer any load or object from the air, sea surface, or another vessel to the host ship irrespective of weather conditions or relative motions. To progress the vision the proposed NSERC DG will focus on developing a novel sensory system to identify and track an arbitrary body in the ocean from a moving vessel. Due to the extreme dynamics of the environment, the proposed system will be a suite of sensors and will leverage our previous sensor fusion and signal prediction algorithms. The outcome of the work will result in a novel sensor suite and sensor fusion algorithm which has applications for maritime launch and recovery along with other autonomous vehicle uses. Additionally, the proposed research will use the outputs of the novel sensor system to provide inputs to crane-based Motion Compensation Systems (MCS). The proposed research will extend our previously developed MCS by removing several common assumptions such as rigid links, linear friction, and negligible crane-ship interaction. The work will also develop a novel real-time digital twin for a crane MCS which is suitable to be integrated into a real-time control loop. This digital twin will also be used to study and mitigate unwanted effects from disturbances and possible positive feedback loops from the crane-ship interactions. The proposed program will train 12 Highly Qualified Personnel (HQP): 3 PhD, 4 MSc and 5 BSc. Our research will help to improve the safety of the 5-billion-dollar Canadian marine industry, address the Canadian Government's commitment to improve marine safety, and provide technologies for the 35-billion-dollar national shipbuilding strategy for Canada. The HQP will also possess core skills to contribute to the Canadian marine market and the estimated 91,000 new jobs in the offshore power field over the next 20 years.