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The purpose of this study is the use of 3D printing technology to perform maintenance on damaged parts on site. To maintain damaged parts, the user needs experience in the parts design and 3D printing technology. To help users who have little or no experience on 3D printing, a part library-based information retrieval and inspection framework was proposed to support the process of manufacturing replaceable parts using a 3D printer.

To establish the framework, 3D printing-based maintenance procedure was first defined, comprising retrieval, manufacturing and inspection steps, while identifying the technical components required to perform the procedure. Once the technical components are identified, part library-based information retrieval and inspection framework was defined based on the technical components and the relationships between the components. For validation of the concept of the framework, prototype system is developed according to the proposed framework.

The feasibility of the proposed framework is proved through maintenance experiments on gaskets and O-rings.

The main contribution of this study is the proposal of the framework, which aims to support the maintenance of damaged parts for the user who has little or no experience in part design or does not know how to operate a 3D printer.

Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.

The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.

The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.

The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.