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Artículo de publicación ISIThe generation of accurate terrain maps while navigating over off-road, irregular terrains is a complex challenge, due to the difficulties in the estimation of the pose of the laser rangefinders, which is required for the proper registration of the range measurements. This paper addresses this problem. The proposed methodology uses an Extended Kalman filter to estimate in real-time the instantaneous pose of the vehicle and the laser rangefinders by considering measurements acquired from an inertial measurement unit, internal sensorial data of the vehicle and the estimated heights of the four wheels, which are obtained from the terrain map and allow determination of the vehicle's inclination. The estimated 6D pose of the laser rangefinders is used to correctly project the laser measurements onto the terrain map. The terrain map is a 2.5D map that stores in each cell the mean value and variance of the terrain height. In each map's cell position, new laser observations are fused with existing height estimations using a Kalman filter. The proposed methodology is validated in the real world using an autonomous vehicle. Field trials show that the use of the proposed state estimation methodology produces maps with much higher accuracy than the standard approaches.FONDECYT 113015

The appropriate management of fall situations, i.e., fast instability detection, avoidance of unintentional falls, falling without damaging the body, fast recovery of the standing position after a fall-is an essential ability for biped humanoid robots. This issue is especially important for biped humanoid robots carrying out demanding movements such as walking on irregular surfaces, running, or playing a given sport. In this paper, we tackle the detection of instability, and the management of falls in biped humanoids using an integrated framework. In this framework, after instability is detected, a fall can be avoided, or at the very least, low-damage falling sequences can be triggered, depending on the degree of the detected instability. The proposed fall detection and fall avoidance methodologies have been validated in real-world experiments with biped humanoid robots (525 collision experiments were carried out). The obtained results show the robustness of the fall detection methodology. In addition, they show under which conditions falls can be avoided, and the reduction of the fall damage when a fall occurs. Besides, results also suggest that the proposed fall avoidance methodology still needs improvements. Moreover, the methodology is more suitable for small-sized and light-weight robots which are more mechanically robust to falling and can bear the required tests.

The development of research in robotics in a developing country is a challenging task. Factors such as low research funds, low trust from local companies and the government, and a small number of qualified researchers hinder the development of strong, local research groups. In this article, and as a case of study, we present our research group in robotics at the Advanced Mining Technology Center of the Universidad de Chile, and the way in which we have addressed these challenges. In 2008, we decided to focus our research efforts in mining, which is the main industry in Chile. We observed that this industry has needs in terms of safety, productivity, operational continuity, and environmental care. All these needs could be addressed with robotics and automation technology. In a first stage, we concentrate ourselves in building capabilities in field robotics, starting with the automation of a commercial vehicle. An important outcome of this project was the earn of the local mining industry confidence. Then, in a second stage started in 2012, we began working with the local mining industry in technological projects. In this article, we describe three of the technological projects that we have developed with industry support: (i) an autonomous vehicle for mining environments without global positioning system coverage; (ii) the inspection of the irrigation flow in heap leach piles using unmanned aerial vehicles and thermal cameras; and (iii) an enhanced vision system for vehicle teleoperation in adverse climatic conditions.