The all-electric ship (AES) with DC-grid configuration has demonstrated advantages compared to the traditional AC system and has become the state-of-the-art for ships with electric propulsion in the low to medium power range during the past decade. However, the integration with different power sources, such as fuel cells, batteries and diesel gen-sets, increases the system complexity and requires an advanced power management system (PMS) to handle vessel operation and to achieve optimal power control. This paper presents an optimized power management strategy to reduce the total cost of ownership of such vessels, considering not only the fuel cost and emission penalty, but also the power device degradation and equipment replacement cost. In this study, Model Predictive Control (MPC) and Reinforcement Learning (RL)-based PMS control methods are approached respectively. In order to demonstrate the performance of MPC and RL techniques, a typical tugboat load profile is simulated. The testing results are also compared with a traditional rule-based power management control.

As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.

Environmentally driven regulations are significantly affecting shipping in recent years, where the shipbuilding industry is required to comply with upcoming restrictions concerning polluting emissions. The all-electric ship (AES) is one of the most promising technologies for complying with the increasingly strict environmental regulations, improving fuel efficiency, and enhancing system dynamic performance. In this study, the dc-distributed power grid of an AES integrated with fuel cells and batteries has been configured using extensive electrification technology, where the system-level shipboard power plant has been modeled with the average modeling method. The model not only incorporates the hybrid power source integration but also the primary and secondary power management as a whole. In addition, a hardware-in-the-loop (HIL) has been set up to replicate the real-time system behavior, which is essential for the verification of any optimal power management control algorithms to be developed in future work. Finally, both the mathematical and real-time models are validated against the full-scale hybrid shipboard power system.

The electrified hybrid shipboard power system with high-level integration of renewable energy resources and energy storage system has become the new trend for the all-electric ship (AES) configuration. However, the traditional energy management system (EMS) is not able to fulfill the increasingly complex control requirements, and a more advanced EMS control algorithm is required to handle the multiple power sources and even achieve optimal energy management control. This paper proposes supervisory energy management with an improved adaptive model predictive control (AMPC) strategy to optimize the power split of the hybrid power sources and to reduce the total cost of ownership (TCO) of vessel operation, which considers not only the fuel and emission cost but also the power source degradation. In order to achieve real-time implementation, the AMPC-based EMS software has been developed and deployed to a programmable logic controller (PLC) hardware.Ahybrid fuel cell-fed shipboard power system with a DC-grid configuration is modeled and operated on a hardware-in-the-loop (HIL) setup. The prototyping controller verification tests have been performed with this HIL plant in the lab environment. Three typical tugboat load profiles with power fluctuations are implemented as case studies. Lastly, a cost study was carried for a ten-year long-term vessel operational cycle. The proposed AMPC-based EMS can achieve up to 12.19% TCO savings compared to those of a traditional rule-based control strategy.

Robots have been reportedly seen serving food in several restaurants in many parts of the world. New ventures have been deploying mechanical partners which promote the growth in service robotics. However, robots are considerably incompetent when it comes to beverage and soup delivery. The physical challenge behind the clumsy motion of these machines is found to be its jerky motion control. Jerk control solutions are widely studied in a constrained environment but not well introduced in dynamic environments. In this paper, we will begin by examining developed kinematics solutions, open-source packages from Robot Operating System and the constraints of motion planning. The proposed solution in this paper provides a quick system response with jerk limits using spline velocity profiles. The solution will introduce the concepts of a state machine design that enables the robot to behave and move reactively; effectively balancing its desired velocity and position without spilling a drop of customer satisfaction. Experiments have proven that robots can move at higher velocity without any crashing, spilling, or docking issues. The smooth velocity control proposed will improve the capabilities of waiter robot and service operations in restaurants.