On the expected improvement of odometry estimation in integrated exploration

[EN] The problem of Integrated Exploration is the new trend in the construction of maps of unknown environments; in it, the old paradigm of Simultaneous Localization and Mapping (SLAM) is integrated with the planning of movements necessary for this task to be performed autonomously. However, although motion control is an essential part of this new paradigm, the existing literature has been limited to developing strategies that improve travel times and environmental coverage, leaving aside the impact that these can have on robot odometry and, consequently, on the requirements of localization algorithms. Accordingly, this document presents a new efficient way of exploring environments for the SLAM problem, which aims to improve exploration times and maximize coverage of the work area, as well as minimize the accumulated odometric error to simplify the localization process.[ES] El problema de Exploración integrada es la nueva tendencia en la construcción de mapas de ambientes desconocidos; en ella, se integra el viejo paradigma de la localización y mapeo simultáneos (SLAM) con la planificación de movimientos necesarios, para que esta tarea sea realizada de forma autónoma. Sin embargo, aunque el control de movimientos es una parte esencial de este paradigma, los trabajos encontrados en la literatura se han limitado a desarrollar estrategias que mejoren los tiempos de recorridos y la cobertura del ambiente, dejado de lado el impacto que estos puede tener sobre la odometría del robot y, en consecuencia, sobre los requerimientos de los algoritmos de localización. De lo anterior, en este documento se presenta una nueva forma eficiente de exploración de ambientes para el problema de SLAM, que tiene como objetivo mejorar los tiempos de exploración y maximizar la cobertura del área de trabajo, pero además el de minimizar el error odométrico acumulado para simplificar el proceso de localización.Toriz Palacios, A.; Sánchez López, A. (2020). Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada. Revista Iberoamericana de Automática e Informática industrial. 17(2):229-238. https://doi.org/10.4995/riai.2019.11828OJS229238172Abbas, T., Arif, M., Ahmed, W., 2006. Measurement and correction of systematic odometry errors caused by kinematics imperfections in mobile robots. SICE-ICASE International Joint Conference, 2073-2078. https://doi.org/10.1109/SICE.2006.315554Borenstein, J., 1998. Experimental results from internal odometry error correction with the OmniMate mobile robot. 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Learning Terrain Dynamics: A Gaussian Process Modeling and Optimal Control Adaptation Framework Applied to Robotic Jumping

The complex dynamics characterizing deformable terrain presents significant impediments toward the real-world viability of locomotive robotics, particularly for legged machines. We explore vertical, robotic jumping as a model task for legged locomotion on presumed-uncharacterized, nonrigid terrain. By integrating Gaussian process (GP)-based regression and evaluation to estimate ground reaction forces as a function of the state, a 1-D jumper acquires the capability to learn forcing profiles exerted by its environment in tandem with achieving its control objective. The GP-based dynamical model initially assumes a baseline rigid, noncompliant surface. As part of an iterative procedure, the optimizer employing this model generates an optimal control strategy to achieve a target jump height. Experiential data recovered from execution on the true surface model are applied to train the GP, in turn, providing the optimizer a more richly informed dynamical model of the environment. The iterative control-learning procedure was rigorously evaluated in experiment, over different surface types, whereby a robotic hopper was challenged to jump to several different target heights. Each task was achieved within ten attempts, over which the terrain's dynamics were learned. With each iteration, GP predictions of ground forcing became incrementally refined, rapidly matching experimental force measurements. The few-iteration convergence demonstrates a fundamental capacity to both estimate and adapt to unknown terrain dynamics in application-realistic time scales, all with control tools amenable to robotic legged locomotion