The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its predefined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes. We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation.
We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle. Autonomous mobile robots use vision sensors for navigation due to its ability to provide detailed information of the environment. Visual perception of an environment, is an important capability for mobile robots, so many efforts are leading the research community for such a fundamental and challenging task. An aspect to emphasize is that vision systems have to cope with uncertainty because sensors have noise and the previous knowledge is unclear or inaccurate. In this paper, we propose an uncertainty estimation model applied to an object detection system. The vision system is designed to recognize objects in usual human environments, working on a mobile robot. To calculate the uncertainty, we considerthe model accuracy of the system, the probability of detection after the prediction process and the empirical probability of detecting each object according to the distance. The experimental results demonstrate the feasibility and usefulness of incorporating uncertainty information into an object detection system. Finally, the results motivate us to continue improving the uncertaintymodel in order to use the information generated to strengthenmobile robot navigation systems, as well as for the developmentof a place categorization system.