Advancing Marine Systems: Ship Deck Equipment

这项研究计划的使命是通过推进机器人甲板机械来提高海上船舶的安全性。为了提高海上负载转移期间的运营可用性和人员福利，该研究计划重点关注可用于补偿所有轴上相对运动的技术。由于主体之间的相对运动，在海上货物转移期间以及在发射和回收操作期间会出现危险的挑战。该计划采用系统级方法解决问题，以在以下方面取得进展：控制算法;系统建模;机器视觉;机器人技术;以及机器人海洋甲板机械的机械设计。拟议的NSERC-DG将专注于船用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发射，回收或将任何负载或物体从空中，海面或另一艘船上转移到主机上，而不管天气条件或相对运动。为了推进这一愿景，拟议中的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船只上的任意物体。由于环境的极端动态性，所提出的系统将是一套传感器，并将利用我们以前的传感器融合和信号预测算法。这项工作的成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沿着其他自动驾驶车辆。此外，拟议的研究将使用新的传感器系统的输出，以提供输入起重机为基础的运动补偿系统（MCS）。拟议中的研究将扩展我们以前开发的MCS删除几个常见的假设，如刚性链接，线性摩擦，可以忽略不计的起重机船的相互作用。这项工作还将开发一种新的实时数字孪生的起重机MCS，这是适合于集成到一个实时控制回路。这个数字孪生模型还将用于研究和减轻干扰带来的不必要的影响，以及起重机与船舶相互作用可能产生的正反馈回路。 该计划将培养12名高素质人才（HQP）：3名博士，4名硕士和5名学士。我们的研究将有助于提高价值50亿美元的加拿大海洋产业的安全性，实现加拿大政府改善海洋安全的承诺，并为加拿大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年内为海上电力领域提供约91，000个新工作岗位。

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.