Performance evaluation of a distributed integrative architecture for robotics

The eld of robotics employs a vast amount of coupled sub-systems. These need to interact cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms also require intensive cooperative interactions internally. The architecture proposed lends it- self amenable to problem domains that require rigorous calculations that are usually impeded by the capacity of a single machine, and incompatibility issues between software computing elements. Implementations are abstracted away from the physical hardware for ease of de- velopment and competition in simulation leagues. Monolithic developments are complex, and the desire for decoupled architectures arises. Decoupling also lowers the threshold for using distributed and parallel resources. The ability to re-use and re-combine components on de- mand, therefore is essential, while maintaining the necessary degree of interaction. For this reason we propose to build software components on top of a Service Oriented Architecture (SOA) using Web Services. An additional bene t is platform independence regarding both the operating system and the implementation language. The robot soccer platform as well as the associated simulation leagues are the target domain for the development. Furthermore are machine vision and remote process control related portions of the architecture currently in development and testing for industrial environments. We provide numerical data based on the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted, dynamic languages provides hard information showing the feasibility of Web Services based SOAs even in time critical robotic applications

Injecting Abstract Interpretations into Linear Cost Models

We present a semantics based framework for analysing the quantitative behaviour of programs with regard to resource usage. We start from an operational semantics equipped with costs. The dioid structure of the set of costs allows for defining the quantitative semantics as a linear operator. We then present an abstraction technique inspired from abstract interpretation in order to effectively compute global cost information from the program. Abstraction has to take two distinct notions of order into account: the order on costs and the order on states. We show that our abstraction technique provides a correct approximation of the concrete cost computations

Synthesizing and tuning chemical reaction networks with specified behaviours

We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theories and Markov chain Monte Carlo based optimisation. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimise the reaction rates of each CRN using a combination of stochastic search techniques applied to the chemical master equation, simultaneously improving the of correct behaviour and ruling out spurious solutions. In addition, we use techniques from continuous time Markov chain theory to study the expected termination time for each CRN. We illustrate our approach by identifying CRNs for majority decision-making and division computation, which includes the identification of both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference on DNA Computing and Molecular Programming, 201

Communicability across evolving networks

Many natural and technological applications generate time ordered sequences of networks, defined over a fixed set of nodes; for example time-stamped information about ‘who phoned who’ or ‘who came into contact with who’ arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time’s arrow is captured naturally through the non-mutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction