Magnetic field fluctuations in modern buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. If the anomalies of the magnetic field inside the building are nearly static and they have sufficient local variability, they provide a unique magnetic fingerprint that can be utilized in global self-localization. In this article, a Monte Carlo localization (MCL) technique based on this hypothesis is proposed. The feasibility of the technique is demonstrated by presenting a series of global localization experiments conducted in four arbitrarily selected buildings, including a hospital. The experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. In addition, successful human self-localization experiments were conducted by using a wireless wearable magnetometer. The reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time, giving support for self-localization techniques utilizing the local deviations of the field.

In swarm robotics research, instead of using large size robots, it is often desirable to have multiple small size robots for saving valuable work space and making the maintainance of the robots easier. Also, the implementation costs of a miniature robot is lower because of simpler mechanical design. In this paper, we present a novel modular miniature mobile robot designed for swarm robotics research. The sensor set of the robot includes a color stereo camera system with two CMOS cameras and DSP, allowing each robot to do sophisticated stereo image processing on-board. The modular design permits the addition of new modules into the system. The modules communicate using three serial buses (SPI, I2C, and UART), which enable flexible, adaptive, and fast inter-module data exchange. The robot is developed for swarm robotics research with the aim to provide a low-cost and low-power miniature mobile robot with capabilities typically found only in large size robots.

The idea of stochastic evolutionary neuron migration process (SENMP) is to use artificial evolution process to arrange spatially interacting computational entities, i.e. artificial neurons, into a pattern in 2-space so that desired behavior or dynamics emerges within the pattern. In this paper, we analyze the role of space in regard to SENMP performance using the well known double pole balancing problem as a test case. We also study the effect of environmental change to the adaptation process during a robot navigation experiment. This analysis suggests that synaptic scaling like dynamics, resembling inverted Hebbian rule, can emerge in the stochastic pattern formation process between the laterally interacting computational entities.

In this paper we show how simple laterally interacting computational entities, i.e. neurons, can be guided by a selectionist process into spatial patterns that show interesting and purposeful dynamics with regard to a particular utility measure. In other words, if a suitable population of laterally interacting mobile entities exist, it is possible to gradually arrange the entities into a spatial pattern that exhibits the desired dynamics. In this paper, the selectionist process is implemented with the stochastic evolutionary neuron migration process (SENMP) and approach is used to evolve dynamic recurrent neural networks (DRNNs) for controlling complex dynamic systems such as autonomous mobile robots, for example. The feasibility and advantages of the approach are demonstrated by evolving neural controllers for solving a non-Markovian double pole balancing problem. In addition, we have earlier used SENMP to evolve navigation behaviors for mobile robots in complex simulated and real environments.

Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task. In this paper, we propose a phenomenological developmental model based on a stochastic evolutionary neuron migration process (SENMP). Employing a spatial encoding scheme with lateral interaction of neurons for artificial neural networks representing candidate solutions within a neural ensemble, neurons of the ensemble form problem-specific geometrical structures as they migrate under selective pressure. The approach is applied to gain new insights into the development, adaptation and plasticity in artificial neural networks and to evolve purposeful behavior for autonomous robots. We demonstrate the feasibility and advantages of the approach by. using a simulator to evolve a robust navigation behavior for a mobile robot and by verifying the results in a real office environment. We also present some preliminary results regarding the behavior of the adapting neural ensemble and, particularly, a phenomenon exhibiting Hebbian dynamics.