Swarm constructability: designing through embedded tectonic behaviors in swarm robotic systems

Con questa tesi di laurea si muovono i primi passi di una ricerca applicata finalizzata alla costruzione-deposizione di materiale da parte di sciami di mini-robot dal comportamento indipendente che si coordinano tramite segnali lasciati e rilevati nell’ambiente in cui si muovono. Lo sviluppo di tecniche di progettazione e fabbricazione digitale ha prodotto un aumento nel grado di interconnessione tra tecnologia e design, dunque, di nuove possibilità tettoniche. Le relazioni tettoniche tradizionali stanno infatti subendo una trasformazione radicale, potendo essere esplicitamente informate e dunque mediate attraverso gli strumenti digitali dall’ideazione alla produzione. Questa mediazione informata del contenuto tettonico (che opera costantemente) è distintivo di un approccio material-based alla progettazione che aumenta l’integrazione tra struttura, materia e forma entro le tecnologie di fabbricazione (R.Oxman). Dei numerosi processi di fabbricazione per l’architettura che si servono di tecnologia robotica, pochi sono capaci di superare la logica gerarchica, rigida e lineare-sequenziale che serve di fatto agli obiettivi di automazione ed ottimizzazione. La distribuzione di forme di intelligenza semplificata ad un numero elevato di unità robot è quindi qui proposta come alternativa al modello appena descritto. Incorporando semplici decisioni di carattere architettonico negli agenti-robot che costituiscono il sistema distribuito di entità autonome, la loro interazione e le decisioni prese individualmente producono comportamento collettivo e l’integrazione delle suddette relazioni tettoniche. Nello sviluppo del progetto, si è fatto così riferimento a modelli comportamentali collettivi (di sciame) osservabili in specie comunitarie che organizzano strutture materiali -come termiti e vespe- ed in organismi semplici -come le muffe cellulari della specie Physarum polycephalum. Per queste specie biologiche il processo di costruzione non dipende da un ‘piano generale’ ma è guidato esclusivamente da azioni dei singoli individui che comunicano lasciando tracce chimiche nell’ambiente e modificano il loro comportamento rilevando le tracce lasciate dagli altri individui. A questo scopo, oltre alle simulazioni in digitale, è stato indispensabile sviluppare dei prototipi funzionali di tipo fisico, ovvero la realizzazione di mini-robot dal movimento indipendente, in grado di coordinarsi tra loro tramite segnali lasciati nell’ambiente e capaci di depositare materiale.

Virtual sensing technology applied to a swarm of autonomous robots.

This thesis proposes a novel technology in the field of swarm robotics that allows a swarm of robots to sense a virtual environment through virtual sensors. Virtual sensing is a desirable and helpful technology in swarm robotics research activity, because it allows the researchers to efficiently and quickly perform experiments otherwise more expensive and time consuming, or even impossible. In particular, we envision two useful applications for virtual sensing technology. On the one hand, it is possible to prototype and foresee the effects of a new sensor on a robot swarm, before producing it. On the other hand, thanks to this technology it is possible to study the behaviour of robots operating in environments that are not easily reproducible inside a lab for safety reasons or just because physically infeasible. The use of virtual sensing technology for sensor prototyping aims to foresee the behaviour of the swarm enhanced with new or more powerful sensors, without producing the hardware. Sensor prototyping can be used to tune a new sensor or perform performance comparison tests between alternative types of sensors. This kind of prototyping experiments can be performed through the presented tool, that allows to rapidly develop and test software virtual sensors of different typologies and quality, emulating the behaviour of several hardware real sensors. By investigating on which sensors is better to invest, a researcher can minimize the sensors’ production cost while achieving a given swarm performance. Through augmented reality, it is possible to test the performance of the swarm in a desired virtual environment that cannot be set into the lab for physical, logistic or economical reasons. The virtual environment is sensed by the robots through properly designed virtual sensors. Virtual sensing technology allows a researcher to quickly carry out real robots experiment in challenging scenarios without all the required hardware and environment.

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.

A comprehensive study on robotic manipulation of deformable linear objects

There are many deformable objects such as papers, clothes, ropes in a person’s living space. To have a robot working in automating the daily tasks it is important that the robot works with these deformable objects. Manipulation of deformable objects is a challenging task for robots because these objects have an infinite-dimensional configuration space and are expensive to model, making real-time monitoring, planning and control difficult. It forms a particularly important field of robotics with relevant applications in different sectors such as medicine, food handling, manufacturing, and household chores. In this report, there is a clear review of the approaches used and are currently in use along with future developments to achieve this task. My research is more focused on the last 10 years, where I have systematically reviewed many articles to have a clear understanding of developments in this field. The main contribution is to show the whole landscape of this concept and provide a broad view of how it has evolved. I also explained my research methodology by following my analysis from the past to the present along with my thoughts for the future.

Robotic grasping: State of the art and modular real-world implementation

Robotic Grasping is an important research topic in robotics since for robots to attain more general-purpose utility, grasping is a necessary skill, but very challenging to master. In general the robots may use their perception abilities like an image from a camera to identify grasps for a given object usually unknown. A grasp describes how a robotic end-effector need to be positioned to securely grab an object and successfully lift it without lost it, at the moment state of the arts solutions are still far behind humans. In the last 5–10 years, deep learning methods take the scene to overcome classical problem like the arduous and time-consuming approach to form a task-specific algorithm analytically. In this thesis are present the progress and the approaches in the robotic grasping field and the potential of the deep learning methods in robotic grasping. Based on that, an implementation of a Convolutional Neural Network (CNN) as a starting point for generation of a grasp pose from camera view has been implemented inside a ROS environment. The developed technologies have been integrated into a pick-and-place application for a Panda robot from Franka Emika. The application includes various features related to object detection and selection. Additionally, the features have been kept as generic as possible to allow for easy replacement or removal if needed, without losing time for improvement or new testing.