Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks

We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We notice that: 1) information self structuring through sensory-motor coordination does not deterministically occur in Rn vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; 2) it happens in a stochastic open ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework which aims to give new tools for the design of networks of new artificial self organizing, embodied and intelligent agents and the reverse engineering of natural ones. At this point, it represents much a theoretical conjecture and it has still to be experimentally verified whether this model will be useful in practice.

Path planning for active tensegrity structures

This paper presents a path planning method for actuated tensegrity structures with quasi-static motion. The valid configurations for such structures lay on an equilibrium manifold, which is implicitly defined by a set of kinematic and static constraints. The exploration of this manifold is difficult with standard methods due to the lack of a global parameterization. Thus, this paper proposes the use of techniques with roots in differential geometry to define an atlas, i.e., a set of coordinated local parameterizations of the equilibrium manifold. This atlas is exploited to define a rapidly-exploring random tree, which efficiently finds valid paths between configurations. However, these paths are typically long and jerky and, therefore, this paper also introduces a procedure to reduce their control effort. A variety of test cases are presented to empirically evaluate the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.Peer ReviewedPostprint (author's final draft

Learning morphological phenomena of Modern Greek an exploratory approach

This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive open‐ended multimedia environment. The proposed model introduces a new language metaphor, the ‘puzzle‐metaphor’ (similar to the existing ‘turtle‐metaphor’ for concepts from mathematics and physics), based on a visualized unification‐like mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching

On adaptive robot systems for manufacturing applications

System adaptability is very important to current manufacturing practices due to frequent changes in the customer needs. Two basic concepts that can be employed to achieve system adaptability are flexible systems and modular systems. Flexible systems are fixed integral systems with some adjustable components. Adjustable components have limited ranges of parameter changes that can be made, thus restricting the adaptability of systems. Modular systems are composed of a set of pre-existing modules. Usually, the parameters of modules in modular systems are fixed, and thus increased system adaptability is realized only by increasing the number of modules. Increasing the number of modules could result in higher costs, poor positioning accuracy, and low system stiffness in the context of manufacturing applications. In this thesis, a new idea was formulated: a combination of the flexible system and modular system concepts. Systems developed based on this new idea are called adaptive systems. This thesis is focused on adaptive robot systems. An adaptive robot system is such that adaptive components or adjustable parameters are introduced upon the modular architecture of a robot system. This implies that there are two levels to achieve system adaptability: the level where a set of modules is appropriately assembled and the level where adjustable components or parameters are specified. Four main contributions were developed in this thesis study. First, a General Architecture of Modular Robots (GAMR) was developed. The starting point was to define the architecture of adaptive robot systems to have as many configuration variations as possible. A novel application of the Axiomatic Design Theory (ADT) was applied to GAMR development. It was found that GAMR was the one with the most coverage, and with a judicious definition of adjustable parameters. Second, a system called Automatic Kinematic and Dynamic Analysis (AKDA) was developed. This system was a foundation for synthesis of adaptive robot configurations. In comparison with the existing approach, the proposed approach has achieved systemization, generality, flexibility, and completeness. Third, this thesis research has developed a finding that in modular system design, simultaneous consideration of both kinematic and dynamic behaviors is a necessary step, owing to a strong coupling between design variables and system behaviors. Based on this finding, a method for simultaneous consideration of type synthesis, number synthesis, and dimension synthesis was developed. Fourth, an adaptive modular Parallel Kinematic Machine (PKM) was developed to demonstrate the benefits of adaptive robot systems in parallel kinematic machines, which have found many applications in machine tool industries. In this architecture, actuators and limbs were modularized, while the platforms were adjustable in such a way that both the joint positions and orientations on the platforms can be changed

Dynamic Modeling of Branched Robots using Modular Composition

This letter proposes a systematic modular procedure for the dynamic modeling of branched robots comprising several subsystems, each of which being composed of multiple rigid bodies. Furthermore, the proposed strategy is applicable even if some subsystems are regarded as black boxes, requiring only the twists and wrenches at the connection points between different subsystems. To help in the model composition, we also propose a graph representation that encodes the propagation of twists and wrenches between the subsystems. Numerical results show that the proposed formalism is as accurate as a state-of-the-art library for robotic dynamic modeling.Comment: 7 pages, 5 figures, 2 tables. Under Review for the IEEE Robotics and Automation Letters (RA-L

Kinematic Modeling, Linearization and First-Order Error Analysis

This chapter deals with a modular method for the kinematic analysis of parallel kinematic machines (PKM) at discrete points within their workspace. Firstly, a modular approach is presented for calculating the forward kinematic transmission function of some widely used parallel kinematic machines. This includes the well-known Stewart-Gough-platforms of general geometry, the Delta-robots, and parallel machines with legs of constant length. The kinematic analysis is based on the kinetostatic method and permits to calculate the position, velocity, and acceleration transmission from the articulated joints towards the moveable platform of the machine. Furthermore, a force transmission is defined based on kinetostatic duality. By means of a simple numerical calculation schema, a comprehensive first-order sensitivity analysis is performed. Finally, it is shown how to set up the stiffness matrix for the aforementioned robots. Computational examples of the proposed algorithms are presented