Robot configuration design is hampered by the lack of established, well-known design rules, and designers cannot easily grasp the space of possible designs and the impact of all design variables on a robot’s performance. Realistically, a human can only design and evaluate several candidate configurations, though there may be thousands of competitive designs that should be investigated. In contrast, an automated approach to configuration synthesis can create tens of thousands of designs and measure the performance of each one without relying on previous experience or design rules. This thesis creates Darwin2K, an extensible, automated system for robot configuration synthesis. This research focuses on the development of synthesis capabilities required for many robot design problems: a flexible and effective synthesis algorithm, useful simulation capabilities, appropriate representation of robots and their properties, and the ability to accomodate application-specific synthesis needs. Darwin2K can synthesize and optimize kinematics, dynamics, structural geometry, actuator selection, and task and control parameters for a wide range of robots.

This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarmbased problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. Applications in the areas of combinatorial optimization, distributed manufacturing, collective robotics, and routing in networks (including mobile ad hoc networks) are then reviewed. Military and security applications are then described, specifically highlighting the groups that have been or continue to be active in swarm research. The final section of the document identifies areas of future research of potential military interest. A substantial bibliography is provided in

We present a real-time localization system based on monocular vision and natural landmarks. In a learning step, we record a reference video sequence and we use a structure from motion algorithm to build a model of the environment. Then in the localization step, we use this model to establish correspondences between the 3D model and 2D points detected in the current image. These correspondences allow us to compute the current camera localization in real-time. The main topic of this paper is the performance evaluation of the whole system. Four aspects of performance are considered: versatility, accuracy, robustness and speed. 1

Computational functionalism [5] fails to understand the embodied and sit- uated nature of behaviour by taking steady state functions as theoretical primitives, and by interpreting cognitive behaviour from a language-like, observer dependant framework without a naturalized normativity. Evolu- tionary functionalism [28, 27], on the other hand, by grounding functional normativity on historical processes fails to give an account of normative functionality based on the present causal mechanism producing behaviour. We propose an alternative autonomous dynamical framework where func- tionality is defined as contribution to self-maintenance [15, 10, 35] and nor- mativity as satisfaction of closure criteria. We develop this framework by a set of formal definitions in the framework of dynamical system theory and propose the hypothesis of an homeostatic-plasticity [31, 40] based general purpose value system as an internalized normative mechanism that selects between internal state trajectories to produce adaptive functionality under different environmental conditions. To test the hypothesis we develop a simulation model where lower level specifications of a control arquitecture (an homeostatic plastic DRNN) give rise (through a simulated evolutionary process) to adaptive behaviour in a foraging task where food sources can be poisonous or profitable. Analysis of the evolved agent show that plastic changes occur when the agent produces salient adaptive interactions, those plastic changes determining the adaptive strategy. The embodied and interactive adaptive functionality is dynamically analysed, illustrating the autonomous dynamical framework.