Current Topics in Engineering (Discontinued) - Volume 3, Issue 1, 2024

Transformable architecture heavily depends on grasping geometry. Some natural materials possess geometric traits leading to similar structural responses when their size changes, resulting in diverse spatial shapes. Auxetic materials demonstrate a negative Poisson's ratio, enabling them to shift from lines to surfaces, and from surfaces to volumes or spatial frameworks. The background of this research focuses on evaluating and establishing honeycomb reentrant auxetic geometries to craft innovative architectural spaces capable of transformation. By methodically examining the geometric attributes of these structures at both molecular and macroscopic levels, they could play a crucial role in architectural development.

The method of this investigation entails a comparative Computer-Aided Design (CAD) study of 2D honeycomb reentrant auxetic patterns. The objective is to employ CAD to gain deeper insights into these patterns for architectural applications.

As a result, the study evaluates the geometric performance of various 2D honeycomb reentrant auxetic patterns, systematically comparing them based on criteria such as the maximum achievable area reductions relative to the total length of the structure's bars.

The conclusion is to ascertain a growth factor for these structures.

In some instances, when tendering for supplying goods to a purchaser, the bidders know that bid evaluation is done in a multicriteria way but ignore the precise metric used. This is the case, for example, of acquisitions by small and medium-sized businesses carried out by inviting a handful of potential suppliers to advance a proposal for fulfilling some need and informing them of criteria hurdle levels and worded statements of criteria preferred directions.

The uncertainty about bid assessment is compounded with that about competing bids, making bid design increasingly difficult. This work presents a Decision Analysis-based methodology for developing a model for bid preparation useful under said circumstances, aiming to take advantage of the available knowledge of competitors’ capabilities and client’s preferences through subjective probabilities.

A key feature of the methodology is the idea that the model of the bidder’s knowledge of the client’s preferences for different criterion levels should separately consider the criterion type and the likely size of its variation among bids. This allows basing the model on the assumption that, to the client’s decision, a criterion becomes more important if the size of its variation among bids grows, while it is rendered unimportant if it does not vary a lot among bids. After outlining the general steps of the methodology, a simple, hypothetical case study is numerically worked out, illustrating how the methodology is operationalized for a problem with two criteria and two levels.

Finally, closing remarks on the potential practical usefulness of the presented framework are provided.

Compared to the single unmanned boat, multi-unmanned boats have more flexible mobility and efficient task completion capabilities, which can effectively expand the types of tasks. However, the traditional independent path planning and obstacle avoidance methods of unmanned boats make it difficult to meet the requirements of collaborative operation among multiple unmanned boats due to the lack of information exchange.

According to the actual demand of multi unmanned boats' cooperative operation, a method of multi unmanned boats cooperative obstacle avoidance based on 5G edge computing is proposed to realize the unified planning and scheduling of multi unmanned boats.

Firstly, 5G technology and Kubeedge edge computing tools are used to build a multi unmanned boat collaborative obstacle avoidance system based on cloud, edge and end collaboration, and the Kubeedge edge computing platform was optimized by optimizing communication strategies, building a highly available Kubeedge cluster, building a Harbor image center, and using Web management interfaces further to improve the reliability and stability of the system. Secondly, the YOLOR-Deepport multi-target recognition and tracking algorithm based on cloud, edge and end collaborative network is used to complete the recognition and tracking tasks of obstacle targets, and a set of EECBS path planning methods based on the Kubedge centralized control platform is designed to plan collision-free and efficient paths for each unmanned boat in real-time. Finally, the effectiveness of the system was verified through simulation experiments.

The experimental results show that compared to the traditional autonomous planning obstacle avoidance method for unmanned boats, the collaborative planning obstacle avoidance method proposed in this paper can exhibit excellent performance in dense and narrow scenarios, with a more reasonable navigation path, a range reduction of 20% - 50%, and higher safety.

The results show that the cooperative obstacle avoidance system based on 5G edge computing designed in the paper is feasible, and it can effectively realize the cooperative path planning and obstacle avoidance of multi unmanned boats.