Adding Uncertainty to an Object Detection System for Mobile Robots

Hernandez, Alejandra C.; Gómez, Clara; Crespo, Jonathan; Barber, Ramón

doi:10.1109/smc-it.2017.9

Cited by 4 publications

(1 citation statement)

References 156 publications

(263 reference statements)

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“…Another important challenge relates to safe ground movement of autonomous (space) rovers or robots. The authors of [18] address this issue by using support vector machines to recognize and avoid dangerous objects. In similar context, [19] addresses the task of image analysis for automated navigation systems.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Predicting Thermal Power Consumption of the Mars Express Spacecraft

Petković

Boumghar

Breskvar

et al. 2019

IEEE Aerosp. Electron. Syst. Mag.

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The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined 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 Accepted at IEEE Aerospace and Electronic Systems Magazine 2 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. Index Terms machine learning, Mars Express spacecraft, ensemble learning, predictive modeling, random forest, gradient boosting, feature engineering I. INTRODUCTION M ARS EXPRESS (MEX), a spacecraft operated by the European Space Agency (ESA), is Europe's first spacecraft that orbits Mars. During its science operations, since the beginning of 2004, it has provided evidence of the presence of water above and below the surface of the planet [1], an ample amount of three-dimensional renders of the surface as well as the most complete map of the chemical composition of Mars's atmosphere [2].MEX is powered by electricity generated by its solar arrays and stored in batteries to be used during the eclipse periods. The scientific payload of the MEX consists of seven instruments that provide global coverage of the planet's surface, subsurface and atmosphere. The instruments and on-board equipment have to be kept within their operating temperature ranges, spanning from room temperature for some instruments, to temperatures as low as -180°C for others. In order to maintain these predefined operating temperatures, the spacecraft is equipped with an autonomous thermal system composed of 33 heater lines as well as coolers. The thermal system, together with the platform units, consumes a significant amount of the total generated electric power, leaving a fraction to be used for science operations.Predicting the power consumption of the thermal system is a non-trivial task. However, due to the aging of the spacecraft and the decaying capacity of its batteries, it is a very crucial one for optimal planning and execution of science operations on MEX. The power consumption is a dynamic process that changes through time, depending on various external and internal factors, such as long-term exposure of the spacecraft to the Sun or heat generated by the onboard instruments. F...

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Section: Related Workmentioning

confidence: 99%