This paper proposes an olfaction based methodology to automatically cover an unknown area enabling the decoupled cooperation of a group of floor cleaning mobile robots. This method is based on the utilisation of low cost chemical sensors in cleaning mobile robots, in order to differentiate clean from dirty areas. The experimental results show that the use of olfactory capabilities allows to efficiently cover and clean a certain area, and demonstrate the possibility of coordinating several mobile robots without the need of expensive sensing capabilities, map building or complex algorithms for task scheduling.
Abslrocl-This paper presents a pre-processing method lo allow the implementation of landmine sensor fusion techniques in a pneumatic demining robot. The proposed method is based on a two-level strategy for the sensor fusion. This strategy allows separating two different classification tasks. ObjeeliBackground and MindAnother Object. This work is foeused in the first stage of the process, particularly in the Identification of Regions-Of-Interest (ROD. The pmposed ROI extraction algorithm decreases the number or parameters, is reliable in diflerent environmental conditions and can be Implemented online Results of experimental implementation and conclusions are presented.
To enable automated landmine detection a number of practical problems must be overcome. One of them is the mapping of data from landmine detection sensors using a mobile demining robot in an unstructured outdoor environment. The odometry of the robot used for mapping can be improved by using an additional vision system. It is proposed in this paper to utilize natural landmarks which can be found on the ground for this purpose. New simple algorithms for detection and association of natural landmarks are developed and described. Finally, the experimental results of applying these algorithms on a set of images obtained from a camera mounted on the robot are presented. The results show their robustness for different environmental conditions and robot motions.
Humanitarian demining is a dangerous and time consuming task which leads to more victims among deminers. Thus large efforts are being made worldwide in order to develop autonomous robots able to detect landmines without human participation. Any single sensor currently available for antipersonnel landmine detection can not provide the required detection/false alarm ratio, thus a combination of several sensors together with sensor fusion techniques is used. One of the main problems in the practical implementation of sensor fusion for a demining robot is the lack of experimental data for training the algorithms. Formally using a small training and evaluation set does not allow to take conclusions about the results of sensor fusion. This work presents a sensor fusion strategy which helps to overcome the problem of having few experimental data by creating an unified landmine signatures database. Using this database for sensor fusion in general and for features selection in particular is described.In addition to commonly used statistical features several new features are proposed which reflect the shape and the nature of the object. Moreover the designed ROIs extraction algorithm provides additional information allowing to improve features extraction. The results of features selection using a combination of Mutual information and Hausdorff distance are presented. Preliminary results of sensor fusion for pulsed and continuous metal detectors are also presented.
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