Abstract

Abstract
This paper studies the dynamics of foot-ground interaction and the performance of integer and fractional order controllers in multi-legged locomotion systems. For that objective the robot prescribed motion is characterized in terms of several locomotion variables. Moreover, to compare the system performance, we formulate six performance measures of the walking robot namely, the mean absolute power, the mean power dispersion, the mean power lost in the joint actuators, the mean force of the interface body-legs per walking distance and the mean square error of the hip and foot trajectories. A set of model-based experiments reveals the influence of the different controller implementations in the proposed indices.

Abstract
This paper compares the performance of classical position PD algorithm with a cascade controller involving position and force feedback loops, for multi-legged locomotion systems and variable ground characteristics. For that objective the robot prescribed motion is characterized in terms of several locomotion variables. Moreover, we formulate several performance measures of the walking robot based on the robot and terrain dynamical properties and on the robot hip and foot trajectory errors. Several experiments reveal the performance of the different control architectures in the proposed indices.

Abstract
This paper analyzes the performance of a hexapod robot under the action of a cascade controller involving position and force feedback loops. In order to evaluate the system properties we formulate several measures of the walking system based on the system dynamics and the trajectory errors. Simulation experiments reveal the influence of the different control algorithms upon the proposed indices.

Abstract
This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The chaos-like noise and the input/output signals are studied revealing a fractional-order dynamics, characteristic of a long-term system memory.

Abstract
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm population (GA) for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The input/output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.