Robust Audio Localization for Mobile Robots in Industrial Environments

Abstract

For autonomous navigation in workspace, a mobile robot has to be able to know its position in this space in a precise way that means that the robot must be able to self-localize to move and perform successfully the different entrusted tasks. At present, one of the most used systems in open spaces is the GPS navigation system; however, in indoor spaces (factories, buildings, hospitals, warehouses...) GPS signals are not operative because their intensity is too weak. The absence of GPS navigation systems in these environments has stimulated the development of new local positioning systems with their particular problems. Such systems have required in many cases the installation of beacons that operate like satellites (similar to GPS), the use of landmarks or even the use of other auxiliary systems to determine the robot’s position. The problem of mobile robot localization is a part of a more global problem because in autonomous navigation when a robot is exploring an unknown environment, it usually needs to obtain some important information: a map of the environment and the robot’s location in the map. Since mapping and localization are related to each other, these two problems are usually considered as a single problem called simultaneous localization and mapping (SLAM). The problem of Simultaneous Localization and Map Building is a significant open problem in mobile robotics which is difficult because of the following paradox: to localize itself the robot needs the map of the environment, and, for building a map the robot location must be known precisely. Mobile robots use different kinds of sensors to determine their position: for instance it is very common the use of odometric or inertial sensors, however it is remarkable to consider that in wheel slippage, sensor drifts a noise causing error accumulation, thus leading to erroneous estimates. Another kind of external sensors used in robotics in order to solve localization are for instance CCD cameras, infrared sensor, ultra sonic sensor, mechanical wave and laser. Other sensors recently applied are the instruments sensible to the magnetic field known as the electronic compass (Navarro & Benet, 2009). Mobile robotics are interested on those able to measure the Earths magnetic field and express it through an electrical signal. One type of electronic compass is based on magneto-resistive transducers, whose electrical resistance varies with the changes on the applied magnetic field. This type of sensors presents sensitivities below 0.1 milligauss, with response times below 1 sec, allowing its reliable use in vehicles moving at high speeds (Caruso, 2000). In SLAM some applications with electronic compass have been developed working simultaneously with other sensors such as artificial vision (Kim et al., 2006) and ultrasonic sensors (Kim et al., 2007).