Sensing and mapping for interactive performance

This paper describes a trans-domain mapping (TDM) framework for translating meaningful activities from one creative domain onto another. The multi-disciplinary framework is designed to facilitate an intuitive and non-intrusive interactive multimedia performance interface that offers the users or performers real-time control of multimedia events using their physical movements. It is intended to be a highly dynamic real-time performance tool, sensing and tracking activities and changes, in order to provide interactive multimedia performances. From a straightforward definition of the TDM framework, this paper reports several implementations and multi-disciplinary collaborative projects using the proposed framework, including a motion and colour-sensitive system, a sensor-based system for triggering musical events, and a distributed multimedia server for audio mapping of a real-time face tracker, and discusses different aspects of mapping strategies in their context. Plausible future directions, developments and exploration with the proposed framework, including stage augmenta tion, virtual and augmented reality, which involve sensing and mapping of physical and non-physical changes onto multimedia control events, are discussed

Self-Adapting Modular Robotics: A Generalized Distributed Consensus Framework

Biological systems achieve amazing adaptive behavior with local agents performing simple sensing and actions. Modular robots with similar properties can potentially achieve self-adaptation tasks robustly. Inspired by this principle, we present a generalized distributed consensus framework for selfadaptation tasks in modular robotics. We demonstrate that a variety of modular robotic systems and tasks can be formulated within such a framework, including (1) an adaptive column that can adapt to external force, (2) a modular gripper that can manipulate fragile objects, and (3) a modular tetrahedral robot that can locomote towards a light source. We also show that control algorithms derived from this framework are provably correct. In real robot experiments, we demonstrate that such a control scheme is robust towards real world sensing and actuation noise. This framework can potentially be applied to a wide range of distributed robotics applications.Engineering and Applied Science

Distributed Cooperative Deployment of Heterogeneous Autonomous Agents: A Pareto Suboptimal Approach

The paper presents a distributed cooperative control law for autonomous deployment of a team of heterogeneous agents. Deployment problems deal with the coordination of groups of agents in order to cover one or more assigned areas of the operational space. In particular, we consider a team composed by agents with different dynamics, sensing capabilities, and resources available for the deployment. Sensing heterogeneity is addressed by means of the descriptor function framework, an abstraction that provides a set of mathematical tools for describing both agent sensing capabilities and the desired deployment. A distributed cooperative control law is then formally derived nding a suboptimal solution of a cooperative dierential game, where the agents are interested in achieving the requested deployment, while optimizing the resources usage according to their dynamics. The control law eectiveness is proven by theoretical arguments, and supported by numerical simulations

Feedback control of data aggregation in sensor networks

Sensor networks have recently emerged as a new paradigm for distributed sensing and actuation. This paper describes fundamental performance trade-offs in sensor networks and the utility of simple feedback control mechanisms for distributed performance optimization. A data communication and aggregation framework is presented that manipulates the degree of data aggregation to maintain specified acceptable latency bounds on data delivery while attempting to minimize energy consumption. An analytic model is constructed to describe the relationships between timeliness, energy, and the degree of aggregation, as well as to quantify constraints that stem from real-time requirements. Feedback control is used to adapt the degree of data aggregation dynamically in response to network load conditions while meeting application deadlines. The results illustrate the usefulness of feedback control in the sensor network domain. 1