175 research outputs found

    Humanoid gait optimization resorting to an improved simulation model

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    The simulation of a robot with a high number of joints can easily become unstable. Numerical errors on the first joint of the chain are propagated to the other joints. This is a very common problem in humanoid robots. A way to plan the gait for those robots is using simulation and optimization techniques. This paper addresses a new approach to optimize gait parameter sets using an Adaptive Simulated Annealing optimization algorithm combined with a new joint model that reduces its instability. The new model and the optimization are implemented in SimTwo (a developed physical robot simulator that is capable of simulating user defined robots in a three-dimensional space since it includes a physical model based on rigid body dynamics) and results are shown that validate the approach

    Generalization of Optimal Motion Trajectories for Bipedal Walking

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    Abstract— Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning algorithms for velocity and torque optimal trajectories with respect to optimality and feasibility. To demonstrate the usefulness of the trajectories, a control approach is presented that allows general walking including transitions between points in the task-space

    MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics

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    El libro de actas recoge las aportaciones de los autores a través de los correspondientes artículos a la Dinámica de Sistemas Multicuerpo y la Mecatrónica (Musme). Estas disciplinas se han convertido en una importante herramienta para diseñar máquinas, analizar prototipos virtuales y realizar análisis CAD sobre complejos sistemas mecánicos articulados multicuerpo. La dinámica de sistemas multicuerpo comprende un gran número de aspectos que incluyen la mecánica, dinámica estructural, matemáticas aplicadas, métodos de control, ciencia de los ordenadores y mecatrónica. Los artículos recogidos en el libro de actas están relacionados con alguno de los siguientes tópicos del congreso: Análisis y síntesis de mecanismos ; Diseño de algoritmos para sistemas mecatrónicos ; Procedimientos de simulación y resultados ; Prototipos y rendimiento ; Robots y micromáquinas ; Validaciones experimentales ; Teoría de simulación mecatrónica ; Sistemas mecatrónicos ; Control de sistemas mecatrónicosUniversitat Politècnica de València (2011). MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/13224Archivo delegad

    Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

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    There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Final results demonstrate that optimization is a valid gait planning technique.This work was been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project PEst-OE/EEI/UI0319/2014.info:eu-repo/semantics/publishedVersio

    A phase-indexed tracking controller for interactive physical simulation of animated characters

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-107).In this thesis, I describe a method of animating characters using physical simulation. The main advantage of this approach, verses traditional keyframing methods, is that the animated character can react to physical interactions. These reactions can be synthesized in real-time in interactive applications, such as video games, where traditional approaches can only playback pre-recorded sequences. Physically simulating a character requires a controller, but creating a controller is known to be a challenging task, especially when animation concerns about the style of the motion are taken into consideration. This thesis describes a method of generating a controller automatically and quickly from an input motion. The stylistic aspects of the controller are particularly easy to control, as they are a direct result of the input motion. In order to generate a controller from an input motion, I address two main challenges. First, the input motion must be rectified (minimally modified) to ensure that it is physically plausible. Second, a feedback strategy must be formulated to generate control forces during the simulation. The motion rectification problem is addressed by formulating a fast trajectory optimization that solves for a reference motion. The reference minimally deviates from the input motion to satisfy physical constraints. The second challenge is addressed by employing a novel phase-indexed controller that uses a combination of local and global feedback strategies to keep the character tracking the reference motion. Beyond tracking just a single reference motion, I also demonstrate how variation to a input motion can be automatically synthesized using the same trajectory optimization method used in the rectification process, and how these variations can be sequenced, using optimal control, to accomplish various goals.by Yeuhi Abe.Ph.D

    Enhanced robot learning using Fuzzy Q-Learning & context-aware middleware

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    Development of an Optimized Omnidirectional Walk Engine for Humanoid Robots

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