Mobile Robotics

Evolutionary Active Materials (EAM) Project

is a joint effort of the Department of Computer Science and Engineering (CSE) and the Microelectronics and Materials Physics Laboratories of the Department of Electrical Engineering, and is funded by the Academy of Finland.

The aim of the EAM project is to develop novel evolutionary computation (EC) based design methods for active and versatile materials and structures, and realise the first components through a novel holistic design process utilising constantly increasing computation power, the development of multi-physics simulators, and EC techniques such as genetic algorithms.

One objective of the project is to accelerate the paradigm shift from the conventional design process for active materials towards a new goal driven holistic design process using the full potential of new materials, material combinations, and nonlinear dynamics of materials combined with complex geometries. The results of the project will be demonstrated first in the design of energy efficient piezoelectric actuators.

Contact: Janne Haverinen

Global pose estimation is of fundamental importance in various mobile robot applications where the accurate pose of the robot in a given environment is needed in order to successfully perform application specific tasks. Today, numerous techniques exist for indoor pose estimation. The most common techniques are based on range sensors and computer vision and these techniques have been successfully applied in various environments and mobile robot applications. Despite the fact that good solutions already exist for indoor pose estimation, there is room for new techniques which can make the pose estimation more accurate when combined with other techniques, more robust against possible changes in environmental conditions, and perhaps provide more cost efficient solutions for pose estimation. We proposed a global indoor pose estimation technique utilizing the ambient magnetic field. The technique is based on the well known observation that magnetic field fluctuations commonly exist inside buildings. These fluctuations 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. Assuming the anomalies of the magnetic field inside a building are nearly static and they have sufficient local variability, the anomalies provide a unique magnetic landscape that can be utilized in global pose estimation. Our experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time giving support for pose estimation techniques utilizing the local fluctuations of the magnetic field.

Our work on indoor positioning techniques making use of the ambient magnetic field continued in 2010. A new magnetic field mapping instrument has been developed. The new mapping instrument can be used to collect large amounts of data about the three-dimensional structure of the magnetic field.

A key issue in magnetic field localization is the building of a map of the surrounding magnetic fields. In contrast to the Tuli project, where the mapping is performed manually, in the robotics domain this is referred to as a simultaneous localization and mapping (SLAM) problem. During the year 2010, we published two papers focusing on experimental studies in the magnetic field SLAM and on theoretical issues relating to near-optimal SLAM exploration. Results from our experimental studies (see Figure 1) showed that indoor magnetic fields contain enough spatial variation for accurate mapping. In near-optimal SLAM exploration studies, we developed new methods to autonomously model continuous spatial processes while having uncertain localization information. These methods can be utilized in time-efficient magnetic field SLAM.

 

Figure 1. Magnetic field SLAM in the CSE lobby. The upper left image presents robot’s trajectory using only raw odometry information from the robot’s wheel encoders. The upper right image presents a corrected trajectory where we have used rao-blackwellized particle filters in magnetic field SLAM.

 

As part of our SLAM work, we have proposed a sub-modular sensing quality function which extends studies from discrete sensor placement into an autonomous sampling scheme where sensing sites must be visited frequently. This is beneficial in a SLAM context where the sensing sites themselves bear uncertainties. Also in time critical applications, the modelling accuracy has to be balanced with the sensing time. Our SLAM studies are inspired by our research on indoor mobile robot localization, utilizing ambient magnetic fields which can be modelled by three orthogonal GPs providing a flexible framework for localization and SLAM. We have proved that for applications where sensing sites must be visited frequently, mutual information provides near-optimal solutions. We have extended this into metric probability spaces where sensing sites are treated as random variables. We have shown that with particle filter discretization, this framework can attain near-optimality.

Last updated: 17.4.2012