Vadim Gerasimov

In use parameter estimation of inertial sensors by detecting multilevel quasi-static states

We present an autoadaptive algorithm for in-use parameter estimation of MEMS inertial accelerometers and gyros 1 using multi-level quasi-static states for greater accuracy and reliability. Multi-level quasi-static states are detected robustly using data from both gyros and accelerometers. Proper estimation of time-varying sensor parameters allows us to develop a mixed-reality real-time hand-held orientation tracker with dynamic accuracy of less than 2°. Existing methods like Kalman filters do not take time-varying nature of parameters into account, instead modelling the time-variation as higher values in noise covariance matrices; thus underestimating the sensor capabilities.

Sensor/actuator networks supporting agents for distributed energy management

We have demonstrated the use of low-cost sensing devices for intelligent control of typical commercial energy loads: cool rooms for keeping produce fresh and heating, ventilation, and air-conditioning (HVAC) systems for buildings. We provide the sensing devices - Berkeley Motes and X10 sensing and switching equipment - with a presence on the Internet to permit sophisticated applications to access their data and control their electrical loads. The ad hoc sensor network is connected to the mobile phone network through a light-weight gateway for long-haul communications. Local control provides failsafe default behaviour in the absence of remote control or when communication is lost. We propose to use this network to deploy agent-based software to manage distributed energy resources, including local generation plants as well as loads. Our aim is to provide new ways for small-to-medium enterprises, energy retailers, and energy network businesses to obtain commercial benefits in a deregulated energy market.

Directed self-assembly of 2-dimensional mesoblocks using top-down/bottom-up design

In this paper we present a general design methodology suitable for a class of complex multi-agent systems which are capable of self-assembly. Our methodology is based on a top-down, bottom-up approach, which has the potential to achieve a range of global design goals whilst retaining emergent behaviour somewhere in the system, and thereby allowing access to a richer solution space. Our experimental environment is a software system to model 2-dimensional self-assembly of groups of autonomous agents, where agents are defined as square smart blocks. The general design goal for such systems is to direct the self-assembly process to produce a specified structure. The potential of this design methodology has been realised by demonstrating its application to a toy problem - the self-assembly of rectangles of different sizes and shapes in a two-dimensional mesoblock environment. The design procedure shows different choices available for decomposing a system goal into subsidiary goals, as well as the steps needed to ensure a match to what is achievable from the bottom-up process. Encouraging results have been obtained, which allows mesoblock rectangles of specified size to be assembled in a directed fashion. Two different approaches to the same problem were presented, showing the flexibility of the method.

Vision-Based Drivable Surface Detection in Autonomous Ground Vehicles

One of the primary tasks for most autonomous ground vehicles is road following. For safe maneuvering the vehicle needs to correctly identify the drivable surface. Our work is focused on the use of simple video cameras as the sensor devices. We describe a new machine learning approach to drivable surface detection that automatically combines a set of rectangular features and histogram backprojection based image segmentation algorithms to produce superior results. The machine learning algorithm is based on the AdaBoost method, one of a class of boosting techniques which are applicable to many image processing tasks such as object and face recognition or image segmentation. The algorithm is trained and tested on video data obtained from video cameras mounted on an autonomous tractor at our Queensland site. The algorithm approach, together with the simple feature-based weak classifiers used, produces significantly improved drivable surface detection results

Multiagent Self-Assembly Simulation Environment

We describe a software system1 to model and visualize 3D or 2D self-assembly of groups of autonomous agents. The system makes a physically accurate estimate of the interaction of agents represented as rigid cubic or tetrahedral structures with variable electrostatic charges on the faces and vertices. Local events cause the agentsý charges to change according to user-defined rules or rules generated by genetic algorithms. The system is used as an experimental environment for theoretical and practical study of autonomous agent self-assembly. In particular, the system is used to further develop and test self-assembly properties of meso-blocks. The software system will be applied to the analysis, prediction and design of self-assembly behavior of agents from atomic- to macro-scales. In particular, it will be a platform for developing design techniques that can be implemented in real nano-scale systems to achieve useful structures.

Symbiotic sensor networks in complex underwater terrains: a simulation framework

This paper presents a new multi-agent physics-based simulation framework (DISCOVERY), supporting experiments with self-organizing underwater sensor and actuator networks. DISCOVERY models mobile autonomous underwater vehicles, distributed sensor and actuator nodes, as well as multi-agent data-to-decision integration. The simulator is a real-time system using a discrete action model, fractal-based terrain modelling, with 3D visualization and an evaluation mode, allowing to compute various objective functions and metrics. The quantitative measures of multi-agent dynamics can be used as a feedback for evolving the agent behaviors. An evaluation of a simple simulated scenario with a heterogeneous team is also described.

Self-assembly Simulation System

This paper describes a software system to model and visualize 3D or 2D selfassembly of groups of autonomous agents. The system makes a physically accurate estimate of the interaction of agents represented as rigid cubic or tetrahedral structures with variable electrostatic charges on the faces and vertices. Local events cause the agents’ charges to change according to user-defined rules or rules generated by genetic algorithms. The system is used as an experimental environment for theoretical and practical study of self-assembly. In particular, the system is used to further develop and test self-assembly properties of meso-blocks.