Application of computational intelligence to engineering

Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. This article shows how CI techniques overpass the strict limits of Artificial Intelligence field and can help solving real problems from distinct engineering areas: Mechanical, Computer Science and Electrical Engineering.

Development of Biomimetic Robots in the EPS Engineering Programme Capstone Project

This paper presents the development of a fish-like robot called Bro-Fish. Bro-Fish aims to be an educational toy dedicated to teaching mechanics, programming and the physics of floating objects to youngsters. The underlying intention is to awaken the interest of children for technology, especially biomimetic (biologically inspired) approaches, in order to promote sustainability and raise the level of ecological awareness. The main focus of this project was to create a robot with carangiform locomotion and controllable swimming, providing the opportunity to customize parts and experiment with the physics of floating objects. Therefore, the locomotion principles of fishes and mechanisms developed in related projects were analysed. Inspired by this background knowledge, a prototype was designed and implemented. The main achievement is the new tail mechanism that propels the robot. The tail resembles the undulation motion of fish bodies and is actuated in an innovative way, triggered by an elegant movement of a rotating helicoidal. First experimental tests revealed the potential of the proposed methodology to effectively generate forward propulsion.

Autonomous guided vehicles applied to industrial engineering and management studies

This article presents a framework to an Industrial Engineering and Management Science course from School of Management and Industrial Studies using Autonomous Ground Vehicles (AGV) to supply materials to a production line as an experimental setup for the students to acquire knowledge in the production robotics area. The students must be capable to understand and put into good use several concepts that will be of utmost importance in their professional life such as critical decisions regarding the study, development and implementation of a production line. The main focus is a production line using AGVs, where the students are required to address several topics such as: sensors actuators, controllers and an high level management and optimization software. The presented framework brings to the robotics teaching community methodologies that allow students from different backgrounds, that normally don’t experiment with the robotics concepts in practice due to the big gap between theory and practice, to go straight to ”making” robotics. Our aim was to suppress the minimum start point level thus allowing any student to fully experience robotics with little background knowledge.