Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.

One of the main needs for the humanitarian demining procedures in Colombia and in the whole world is to find complementary sensing methods for the metal detectors. This paper presents a study whose main goal is the development of a complementary sensor for a metal detector, based on the analysis of images taken by an on-board camera. The images are processed by developing texture and color analysis of different types of surfaces, composed mainly by grass or small rocks. Based on the color and texture analysis, this paper shows that it is possible to determine the presence of a non-metallic landmine within the image. This is achieved by finding the image regions whose texture features variations and color compositions are considered to be anomalous. It is thought that the texture variations over the image are an indicator that the surface has been intentionally modified. This image processing strategy is being integrated on the robot 'Ursula', designed at Universidad Javeriana in Colombia.

In this paper a complete strategy for scene modelling from sensory data acquired in a natural environment is defined. This strategy is applied to outdoor mobile robotics, from environment recognition to landmark extraction. In this work, an environment is understood as a specific kind of landscape, for instance prairie, forest, desert, etc. A landmark is defined as a conspicuous object in the environment. In the context of outdoor mobile robotics a landmark has to be useful to perform localization and navigation tasks.

We present an object tracking system based on an edge model for the target characterization. The target position is estimated by looking for the model in the current image using a Hausdorff partial distance. Target is searched only in a sub-window of current edge image. Its boundaries are determined by Kalman filter estimation that uses target dynamics to predict the current position. We use a spiral searching strategy to find the actual position. The target model is updated in each iteration by using unidirectional partial distance from the image to the model. This model is refined by an enclosure operator in order to perform the target/background discrimination. The parameters of our system can be modified in an active way along the tracking task. The system is shown to be robust to illumination changes and pose variations. The system has been also embedded in a mobile robot for personal robotics applications and integrated in a real-time OS (3 Hz).

Deals with perception functions required in an autonomous robot which must move in unknown natural environments. From a sequence of range images acquired during the motion, the robot must incrementally build a model and correct its situation estimate. This approach is suitable for environments in which (1) the terrain is mostly flat and (2) obstacles can be distinguished from the ground. The model gives ground and obstacle representations. In previous works, some obstacles emerging from the ground were selected as landmarks; in this paper, we present a new method: the boundary lines between the ground and the obstacles, can be approximated by B-spline curves so that stable curvature information can be extracted. The robot localization is performed from matchings between these curves perceived from different positions and from the numerical fusion between their models. Several experiments on actual images acquired in a lunar-like environment, are presented and analyzed.