Addressing Tasks Through Robot Adaptation

Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

A modular robot architecture capable of learning to move and be automatically reconfigured

Tackling the problem of making a modular robot automatically learn the movements necessary to locomote in different environments is not an easy task. The ability of modular robots to have an arbitrary morphology provides an advantage over usual monolithic robots when moving in different environments. However, being able to reconfigure also has its problems. Movement control for reconfigurable robots is difficult to design and implement. Morphology can also influence the sensing capabilities of a modular robot. Only a few studies include sensor information when adjusting or optimizing controllers for modular robots. The main contribution of this work is the development of an architecture that includes a locomotion training framework that enables a modular robot to move in different environments taking into account sensor information. The framework is composed of four main parts: a control strategy, a configurable environment approach, an adaptation mechanism and a new modular robot platform: the EMERGE modular robot. The EMERGE modular robot platform is designed to be easy to be assembled and can be quickly reconfigured thanks to the magnetic connectors present in its modules. This in turn enables an external agent, like a robot manipulator to reconfigure the robot. Results show that well coordinated movements turn out to be very important for controllers using sensors to improve when being adapted. The mechanisms inside the controller, for example, decision structures, also play a major part in allowing a robot to adapt to move in different environments and be improved. Evaluating robots in reality is a very expensive task and differences between simulation and reality also make robots behave very differently. The magnetic connector makes the assembly of an EMERGE morphology easier but hinders the disassembly process.Resumen: Resolver el problema de hacer, de forma automática, que un robot modular se mueva en diferentes ambientes no es tarea fácil. La habilidad de los robots modulares de tener morfología arbitraria provee una ventaja sobre robots monolíticos normales al moverse en diferentes ambientes. Sin embargo, ser capaz de auto reconfigurarse tiene sus propios problemas. El control de movimiento para robots modulares es difícil de diseñar e implementar. La morfología de los robots también influencia la capacidad de percibir de los robots modulares. Solo contados estudios incluyen información sensorial al ajustar u optimizar controladores para este tipo de robots. La mayor contribución de este trabajo es el desarrollo de una arquitectura de robot modular que hace que este pueda moverse en diferentes ambientes teniendo en cuenta información sensorial. Esta arquitectura está compuesta por cuatro partes principales: una estrategia de control, un modelo de ambiente configurable, un mecanismo de adaptación y una plataforma de robot modular nueva: el robot EMERGE. El robot modular EMERGE, es diseñado para ser fácil de construir y de reconfigurar gracias a sus conectores magnéticos. Esto también posibilita a un agente externo, como un manipulador robótico, a reconfigurar el robot. Los resultados de los experimentos muestran que la buena coordinación del robot es muy importante para que los controles que usan sensores puedan mejorar. Los mecanismos internos del controlador, por ejemplo, las estructuras de decisión también tienen un rol importante al adaptar el robot a diferentes ambientes. Evaluar robots en la realidad es una tarea muy costosa y las diferencias entre la simulación y la realidad hacen que los robots se comporten muy diferente. Los conectores magnéticos hacen que armar las morfologías de módulos de EMERGE sean fáciles de armar, mas no de desarmar.Doctorad

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life

Self-sufficiency of an autonomous self-reconfigurable modular robotic organism

In recent years, getting inspiration from simple but complex biological organisms, several advances have been seen in autonomous systems to mimic different behaviors that emerge from the interactions of a large group of simple individuals with each other and with the environment. Among several open issues a significantly important issue, not addressed so far, is the self-sufficiency, or in other words, the energetic autonomy of a modular robotic organism. This feature plays a pivotal role in maintaining a robotic organism\u27s autonomy for a longer period of time. To address the challenges of self-sufficiency, a novel dynamic power management system (PMS) with fault tolerant energy sharing is proposed, realized in the form of hardware and software, and tested. The innate fault tolerant feature of the proposed PMS ensures power sharing in an organism despite docked faulty robotic modules. Due to the unavailability of sufficient number of real robotic modules a simulation framework called Replicator Power Flow Simulator is devised for the implementation of application software layer power management components. The simulation framework was especially devised because at the time of writing this work no simulation tool was available that could be used to perform power sharing and fault tolerance experiments at an organism level. The simulation experiments showed that the proposed application software layer dynamic power sharing policies in combination with the distributed fault tolerance feature in addition to self-sufficiency are expected to enhance the robustness and stability of a real modular robotic organism under varying conditions.Inspiriert von einfachen aber komplexen biologischen Organismen wurden in den letzten Jahren verschiedenste autonome Systeme entwickelt, welche die Verhaltensweisen einer großen Gruppe einfacher Individuen nachahmen. Das zentrale und bis heute ungelöste Problem dieser Organismen ist deren autonome Energieversorgung. Zur Sicherstellung der Energieversorgung eines aus mehreren Robotern zusammengesetzten Organismus wurde in dieser Arbeit ein neuartiges Power-Management-System (PMS) konzipiert, aufgebaut und an einzelnen Robotermodulen und einem Roboterorganismus getestet. Die Hardware eines bestehenden Roboters wurde um ein neues Konzept erweitert, das auch bei fehlerhaften Robotermodulen einen Energieaustausch sicherstellt und so zu einer erhöhten Robustheit des PMS führen soll. Das entwickelte PMS wurde in modulare Roboter integriert und beispielhaft anhand eines Roboterorganismus getestet. In Ermangelung einer ausreichenden Anzahl von Robotermodulen wurde eine Simulationsumgebung entwickelt und die Software des PMS im Simulationsprogramm, anstatt im Roboter, implementiert. Dieses Simulationswerkzeug ist momentan das Einzige, das unter Berücksichtigung des Bewegungsmodells des Organismus den Energietransport im Roboterorganismus visuell darstellt und das Verhalten in verschiedenen Fehlerfällen simulieren kann. Die Simulationen und Messungen zeigen, dass das entwickelte PMS geeignet ist, die Energieversorgung von Roboterorganismen auch in Fehlerfällen sicherzustellen und so die Stabilität und Robustheit zu erhöhen

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions