1,696 research outputs found
Adaptive, fast walking in a biped robot under neuronal control and learning
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks
Push Recovery of a Position-Controlled Humanoid Robot Based on Capture Point Feedback Control
In this paper, a combination of ankle and hip strategy is used for push
recovery of a position-controlled humanoid robot. Ankle strategy and hip
strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot
(CMP) regulation respectively. For controlling the CMP and CoP we need a
torque-controlled robot, however most of the conventional humanoid robots are
position controlled. In this regard, we present an efficient way for
implementation of the hip and ankle strategies on a position controlled
humanoid robot. We employ a feedback controller to compensate the capture point
error. Using our scheme, a simple and practical push recovery controller is
designed which can be implemented on the most of the conventional humanoid
robots without the need for torque sensors. The effectiveness of the proposed
approach is verified through push recovery experiments on SURENA-Mini humanoid
robot under severe pushes
Bayesian Gait Optimization for Bipedal Locomotion
One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization. © 2014 Springer International Publishing
Natural ZMP trajectories for biped robot reference generation
The control of a biped humanoid is a challenging
task due to the hard-to-stabilize dynamics. Walking reference
trajectory generation is a key problem. Linear Inverted
Pendulum Model (LIPM) and Zero Moment Point (ZMP)
Criterion based approaches in stable walking reference
generation are reported. In these methods, generally, the ZMP
reference during a stepping motion is kept fixed in the middle of
the supporting foot sole. This kind of reference generation lacks
naturalness, in that, the ZMP in the human walk does not stay
fixed, but it moves forward under the supporting foot. This paper
proposes a reference generation algorithm based on the LIPM
and moving support foot ZMP references. The application of
Fourier series approximation simplifies the solution and it
generates a smooth ZMP reference. A simple inverse kinematics
based joint space controller is used for the tests of the developed
reference trajectory through full-dynamics 3D simulation. A 12
DOF biped robot model is used in the simulations. Simulation
studies suggest that the moving ZMP references are more energy
efficient than the ones with fixed ZMP under the supporting foot.
The results are promising for implementations
Lower body design of the ‘iCub’ a human-baby like crawling robot
The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented
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