Conceptualization of an Electric Compact SUV for the American and Italian Market using the IDeS Method

Electric cars are increasingly popular due to a transition of mobility towards more sustainable forms. From an increasingly green and pollution reduction perspective, there are more and more incentives that encourage customers to invest in electric cars. Using the Industrial Design and Structure (IDeS) research method, this project has the aim to design a new electric compact SUV suitable for all people who live in the city, and for people who move outside urban areas. In order to achieve the goal of developing a new car in the industrial automotive environment, the compact SUV segment was chosen because it is a vehicle very requested by the costumers and it is successful in the market due to its versatility. IDeS is a combination of innovative and advanced systematic approaches used to set up a new industrial project. The IDeS methodology is sequentially composed of Quality Function Deployment (QFD), Benchmarking (BM), Top-Flop analysis (TFA), Stylistic Design Engineering (SDE), Design for X, Prototyping, Testing, Budgeting, and Planning. The work is based on a series of steps and the sequence of these must be meticulously scheduled, imposing deadlines along the work. Starting from an analysis of the market and competitors, the study of the best and worst existing parameters in the competitor’s market is done, arriving at the idea of a better product in terms of numbers and innovation. After identifying the characteristics that the new car should have, the other step is the styling part, with the definition of the style and the design of the machine on a 3D CAD. Finally, it switches to the prototyping and testing phase to see if the product is able to work. Ultimately, intending to place the car on the market, it is essential to estimate the necessary budget for a possible investment in this project.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.