1,721,130 research outputs found
Multimodal Sensor Fusion for Attitude Estimation of Micromechanical Flying Insects: A Geometric Approach
No full textA bio-inspired approach for regulating and measuring visco-elastic properties of a robot arm
No full textA RLWPR network for learning the internal model of an anthropomorphic robot arm
No full textStudies of human motor control suggest that humans develop internal models of the arm during the execution of voluntary movements. In particular, the internal model consists of the inverse dynamic model of the muscolo-skeletal system and intervenes in the feedforward loop of the motor control system to improve reactivity and stability in rapid movements. In this paper, an interaction control scheme inspired by biological motor control is resumed, i.e. the coactivation-based compliance control in the joint space and a feedforward module capable of online learning the manipulator inverse dynamics is presented. A novel recurrent learning paradigm is proposed which derives from an interesting functional equivalence between locally weighted regression networks and lakagi-Sugeno-Kang fuzzy systems. The proposed learning paradigm has been named recurrent locally weighted regression networks and strengthens the computational power of feedforward locally weighted regression networks. Simulation results are reported to validate the control scheme
A bio-inspired approach for regulating visco-elastic properties of a robot arm
No full textNeurophysiological studies show that humans possess the capability of generating appropriate motor behaviors to different uncertain environmental conditions by combining a forward action, produced by the internal forward dynamic model, and a feedback control, realising the transformation from sensory information to motor commands. To this regard, a control system based on the combination of a feedforward and a feedback control loop has been developed in order to provide a robot arm with human-like adaptation capabilities. The work analyses the role of biological coactivation in the mechanism of adjustable visco-elastic arm properties and proposes a function for the evaluation of the robot arm coactivation based on the measure of the position error and the interaction force. The coactivation function is used to update the proportional and derivative parameters of the feedback controller and, consequently, the arm visco-elasticity in unpredictable environmental conditions. Finally, experimental results on the evolution of the coactivation in the adaptation and de-adaptation phases are provided in the last section of the paper
A bio-inspired grasp optimization algorithm for an anthropomorphic robotic hand
No full textA fundamental requirement for assistive robots is to guarantee a safe and human-like way to perform their tasks. In particular, the ability to realize smooth movements and obtain a stable grasp is of primary importance. In this perspective, this paper aims at studying human grasping and developing a bio-inspired method for power-grip posture prediction and finger trajectory planning for a robotic hand. The developed method is based on neuroscientific assumptions and experimental evidence coming from the observation of the human behavior during power grip. It is based on the minimization of a suitably defined function to identify the optimal grasp configuration and the choice of a logarithmic spiral trajectory for moving the fingers. The behavior of ten different subjects during the grasping action has been analyzed with the CyberGlove motion capture data glove. A common thumb posture has been observed and has been introduced in the grasping algorithm. The algorithm performance has been tested on an anthropomorphic robotic hand by means of simulation trials. The results demonstrate the effectiveness of the approach and pave the way for the implementation on a real robotic hand. © 2012 Springer-Verlag
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