Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

Bayesian Optimization is a sequential method for obtaining the maximum of an unknown function that has gained much popularity in recent years. Bayesian Optimization is commonly used to monitor the surface of large-scale aquatic environments using an Autonomous Surface Vehicle. We propose to model water quality parameters using Gaussian Processes, and propose three different adaptations of classical Acquisition Functions in order to explore an unknown space, considering surface vehicle restrictions. The proposed Sequential Bayesian Optimization system uses the aforementioned information in order to monitor the Lake and also to obtain a water quality model, which has an associated uncertainty map. For evaluation, the Mean Squared Error of the resulting approximated models are compared. Afterwards, they are compared with other monitoring algorithms, like the Traveling Salesman Problem, using Genetic Algorithms and Lawnmower. Concluding remarks indicate that the proposed method not only performs better while minimizing the Mean Squared Error (via active monitoring), but also manages to quickly identify an approximate of the black-box function, which is very useful for monitoring lakes like Ypacarai Lake ( $60\: km^{2}$ ) in Paraguay. Additionally, the proposed method reduces the MSE by 25% when compared with Traveling Salesman Problem-based monitoring algorithms and also provides a more robust solution, i.e., 30% more independent of initial conditions, when compared with known robust coverage methods like the lawnmower method.

The number of computers installed in urban and transport networks has grown tremendously in recent years, also the local processing capabilities and digital networking currently available. However, the heterogeneity of existing equipment in the field of ITS (Intelligent Transportation Systems) and the large volume of information they handle, greatly hinder the interoperability of the equipment and the design of cooperative applications between devices currently installed in urban networks. While the dynamic discovery of information, composition and invocation of services through intelligent agents are a potential solution to these problems, all these technologies require intelligent management of information flows. In particular, it is necessary to wean these information flows of the technologies used, enabling universal interoperability between computers, regardless of the context in which they are located. The main objective of this paper is to propose a systematic methodology to create ontologies, using methods such as a semantic clustering algorithms for retrieval and representation of information. Using the proposed methodology, an ontology will be developed in the ITS domain. This ontology will serve as the basis of semantic information to a SS (Semantic Service) that allows the connection of new equipment to an urban network. The SS uses the CORBA standard as distributed communication architecture. We propose a methodology to build ontology's in the domain of ITS (Intelligent Transportation Systems).We have proposed: DPK (Discrimination of Paragraphs with Keywords) and IRWDP (Retrieval with Weighted Data in Paragraphs).The two new methods proposed, allow reducing the sample size of the study.Much information irrelevant has been discarded, achieving greater performance in the ontology construction.The methodologies proposed can be used to build ontologies in any domains.