Andrés Faiña

Flora Robotica - Mixed Societies of Symbiotic Robot-Plant Bio-Hybrids

Besides the life-as-it-could-be driver of artificial life research there is also the concept of extending natural life by creating hybrids or mixed societies that are built from natural and artificial components. In this paper we motivate and present the research program of the project flora robotica. Our objective is to develop and to investigate closely linked symbiotic relationships between robots and natural plants and to explore the potentials of a plant-robot society able to produce architectural artifacts and living spaces. These robot-plant bio-hybrids create synergies that allow for new functions of plants and robots. They also create novel design opportunities for an architecture that fuses the design and construction phase. The bio-hybrid is an example of mixed societies between hard artificial and wet natural life, which enables an interaction between natural and artificial ecologies. They form an embodied, self-organizing, and distributed cognitive system which is supposed to grow and develop over long periods of time resulting in the creation of meaningful architectural structures. A key idea is to assign equal roles to robots and plants in order to create a highly integrated, symbiotic system. Besides the gain of knowledge, this project has the objective to create a bio-hybrid system with a defined function and application -- growing architectural artifacts.

Development of a climbing robot for grit blasting operations in shipyards

This paper deals with the design and construction of a climbing robot for performing grit blasting operations in shipyards. The robot is based on a double sliding platform that uses permanent magnets for attachment. It is lightweight and compact and can move up and along the shipside with any inclination while grit blasting the surface to pre-specified surface quality levels. It can also rotate to compensate for hull curvature and to avoid obstacles while performing its task. The blasting operation is modulated by a vision based quality control system that is used by the mission control system to adapt the blasting parameters in order to attain the desired quality levels while maximizing the surface area the robot strips per unit time.

A Robot for the Unsupervised Grit-Blasting of Ship Hulls

This paper describes the design and the control architecture of an unsupervised robot developed for grit blasting ship hulls in shipyards. Grit blasting is a very common and environmentally unfriendly operation, required for preparing metallic surfaces for painting operations. It also implies very unhealthy and hazardous working conditions for the operators that must carry it out. The robot presented here has been designed to reduce the environmental impact of these operations and completely eliminate the health associated risks for the operators. It is based on a double frame main body with magnetic legs that are able to avoid the accumulation of ferromagnetic dust during its operation. The control system presents a layered structure with four layers that are physically distributed into two separate components in order to facilitate different operational modes as well as to increase the safety requirements of the system. A low-level control component has been implemented on the robotic unit itself, and a...

EDHMoR: Evolutionary designer of heterogeneous modular robots

This paper is devoted to the problem of automatically designing feasible and manufacturable robots made up of heterogeneous modules. Specifically, the coevolution of morphology and control in robots is analyzed and a particular strategy to address this problem is contemplated. To this end, the main issues of this approach such as encoding, evaluation or transfer to reality are studied through the use of heterogeneous modular structures with distributed control. We also propose a constructive evolutionary algorithm based on tree-like representations of the morphology that can intrinsically provide for a type of generative evolutionary approach. The algorithm introduces some new elements to smooth the search space and make finding solutions much easier. The evaluation of the individuals is carried out in simulations and then transferred to real robots assembled from the modules considered. To this end, the extension of the principles proposed by classical authors in traditional evolutionary robotics to brain-body evolution regarding how simulations should be set up so that robust behaviors that can be transferred to reality are obtained is considered here. All these issues are analyzed by means of an evolutionary design system called EDHMoR (Evolutionary Designer of Heterogeneous Modular Robots) that contains all the elements involved in this process. To show practical evidences of the conclusions that have been extracted with this work, two benchmark problems in modular robotics are considered and EDHMoR is tested over them. The first one is focused on solving a linear robot motion mission and the second one on a static task of the robot that does not require displacements.

Dynamic learning in cognitive robotics through a procedural long term memory

Brain-like robotic approaches aim to reproduce the complex processes occurring within the biological brains to achieve a higher level of autonomy. One of the key aspects of these approaches is dynamic learning, that is, how to provide the cognitive architectures that control de robot with adaptive learning capabilities. Several options have been considered in this line in the field of Cognitive Robotics, although the development of a proper memory system has provided the best practical results up to now. This work also follows this approach, seeking to show the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) cognitive architecture. The LTM management system that has been developed to control when a model must be stored orxa0replaced is presented here in detail. Moreover, a Short-Term Memory (STM) sub-system included in the MDB is also explained due to its strong relationship with the operation of the LTM. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.

Lappa: A new type of robot for underwater non-magnetic and complex hull cleaning

This paper is concerned with the design and implementation of a new concept of robot to clean the underwater sections of ship hulls without using any magnetic attachment. The use of this type of robots on a regular basis to preserve a clean hull, usually when ships are in port or anchored, will improve the efficiency of the ships and will permit a reduction in the use of chemicals that are harmful to the environment to prevent the growth of marine life on the hull. The main contribution of the robot described in this paper is that it is a completely novel design that through an appropriate morphology solves the problems that arise when moving along hulls, including changing planes, negotiating appendices, portholes, corners, and other elements. It thus provides a basis for completely autonomous operation. The design and implementation of the robot is described and some simulations and tests in real environments are presented.

An evolution friendly modular architecture to produce feasible robots

This paper proposes the use of a modular robotic architecture in order to produce feasible robots through evolution. To this end, the main requirements the architecture must fulfill are analyzed and a top-down methodology is employed to obtain the different types of modules that make it up. Specifically, the problem of how to increase the evolvability or evolution friendliness of the system is addressed by considering a heterogeneous modular architecture with a large number of connection faces per module. Afterwards, a prototypical implementation of these modules with the required features is described and different experiments provide an indication of how versatile the architecture is for evolving robot morphologies and control for specific tasks and how easy it is to build them. We present a modular architecture to produce feasible robots through evolution.The architecture is based on a set of a heterogeneous modules.The modules contain a large number of connection faces per module.The design and the implementation of prototype modules is described in detail.Different experiments show its potential for evolving robot morphologies and control.

An adaptive detection/attention mechanism for real time robot operation

During the lifetime of a mobile robot, the number and complexity of the stimuli it receives may be quite high. Therefore, the construction of a detection system considering the whole sensorial space is usually not a viable proposition when aiming for real time operation. It becomes necessary to build some kind of sensorial hierarchy map to put some order into how detectors are applied. This is what is usually called an attentional system, and it provides a framework for applying detectors in a more efficient manner. In this paper, an architecture for developing attentional functions for robots that must operate in real time in dynamic environments is presented. This architecture is based on the concept of attentor and it allows for the real time adaptation to the environment and tasks to be performed in a natural manner. One of the main requirements imposed on the design of the architecture was the capability of handling different sensorial modalities and attentional streams in a transparent manner while, at the same time, being able to progressively create more complex attentional structures. The architecture is particularized for its implementation in a real robot.

Towards an evolutionary design of modular robots for industry

We are interested in the next generation of industrial robots, those that are able to operate in dynamic and unstructured environments and, consequently, that are able to adapt to changing circumstances or to work on different tasks in an autonomous way. In this sense, multirobot systems and, in particular, modular systems present several features like scalability, fault tolerance, low maintenance or reconfiguration capabilities that make them highly suitable for this kind of environments. The work presented here is concerned with the problem of automatically obtaining the morphology and control structure for this type of modular systems. In this line, we present the first results produced using a newly designed constructive evolutionary approach that takes into account the extreme difficulty of the tremendously deceptive and uninformative search space this type of applications are faced with. As an example, the algorithm is used to design the morphology and the distributed control parameters for a typical benchmark problem, that of moving as far as possible in a straight line, for a heterogeneous modular robotic system developed by our group.

Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a difficult and time consuming task requiring significant computational power. While artificial evolution in virtual creatures has made use of powerful generative encodings, here we investigate how a generative encoding and direct encoding compare for the evolution of locomotion in modular robots when the number of robotic modules changes. Simulating less modules would decrease the size of the genome of a direct encoding while the size of the genome of the implemented generative encoding stays the same. We found that the generative encoding is significantly more efficient in creating robot phenotypes in the initial stages of evolution when simulating a maximum of 5, 10, and 20 modules. This not only confirms that generative encodings lead to decent performance more quickly, but also that when simulating just a few modules a generative encoding is more powerful than a direct encoding for creating robotic structures. Over longer evolutionary time, the difference between the encodings no longer becomes statistically significant. This leads us to speculate that a combined approach – starting with a generative encoding and later implementing a direct encoding – can lead to more efficient evolved designs.