Modeling and Control of Modular Actuator Arrays
Abstract
The objective of this research is to understand mechanisms for generating natural movements of skeletal mechanisms driven by biologically-inspired cellular actuators. The approach is to verify the hypothesis that the variability associated with high redundancy and the stochastic nature of the actuation is key to generating natural movements. This project seeks to: (i) develop a method to model and characterize actuator array topologies; (ii) develop a method to analyze the force variability of stochastic actuator arrays; (iii) develop an analytical method to generate movements for a robot with multiple degrees of freedom by minimizing the effect of variability; and (iv) demonstrate the validity of the approach through the development of a robotic arm driven by multiple stochastic array actuators.
The “fingerprint” method to characterize complex actuator topologies has been extended to dynamic actuator arrays with Hill-type visco-elastic muscle model. A three-layer rhomboidal PZT amplification structure has been developed for a camera positioner that mimics human ocular motion. The motor variability due to quantization has been analyzed and applied to generate smooth movements.
Award ID: 0932208
Submitted by Jun Ueda
on
Abstract
The objective of this research is to understand mechanisms for generating natural movements of skeletal mechanisms driven by biologically-inspired cellular actuators. The approach is to verify the hypothesis that the variability associated with high redundancy and the stochastic nature of the actuation is key to generating natural movements. This project seeks to: (i) develop a method to model and characterize actuator array topologies; (ii) develop a method to analyze the force variability of stochastic actuator arrays; (iii) develop an analytical method to generate movements for a robot with multiple degrees of freedom by minimizing the effect of variability; and (iv) demonstrate the validity of the approach through the development of a robotic arm driven by multiple stochastic array actuators.
The “fingerprint” method to characterize complex actuator topologies has been extended to dynamic actuator arrays with Hill-type visco-elastic muscle model. A three-layer rhomboidal PZT amplification structure has been developed for a camera positioner that mimics human ocular motion. The motor variability due to quantization has been analyzed and applied to generate smooth movements.
Award ID: 0932208