Manned water-powered aerial vehicles have been implemented into specialized missions around water bodies, such as firefighting and rescue. However, the dual requirement of vehicle motion control and performing tasks challenges operators. Moreover, in the presence of a low visibility, dense smoke, and extreme temperature, they always face potential risks. Motivated by these difficulties, this paper proposed an unmanned water-powered aerial vehicle using a nozzle rotation mechanism. This mechanism allows the vehicle to have a wide range of forces and torques in multiple directions under constant mass flowrate condition. A simple controller was designed to investigate the fundamental flight motions and verify dynamic properties of the vehicle in practical testing. To come up with the control law, the following steps were taken. Firstly, a mathematical model was derived to reflect the vehicle’s dynamic characteristics. Secondly, a well-known proportional-derivative-integral controller incorporating gravity compensation was deployed to regulate the 3-degree-of-freedom motion system. Thirdly, experiments were conducted to confirm the flight ability of the proposed vehicle. Results demonstrated that the control system preserved stability and the vehicle could fly following the desired altitude.

The need for automated material handling inside the factory has been steadily increasing, especially due to implementation of intelligent manufacturing for better productivity and product quality. Automated material handling devices include logistics robots, automated guided vehicles, industrial robots, collaborative robots, and pick-and-place devices. This study focuses on the development of a low-cost logistics robot that works effectively within a simulated smart factory environment. A nominal PID controller is implemented to guide the robot to follow the line painted on the factory floor. The tracking error information is generated by four down-facing infrared sensors and is fed into the controller. The line-following performance is significantly improved with augmentation of a model-based friction compensator. Optimization of battery power depending on the remaining charge status enhances the reliability. All hardware/software development is supported by the Arduino platform. The step-by-step movement and performance of the logistics robot is verified inside the simulated smart factory environment that includes a robot arm, three conveyors, and two processing stations.