This paper presents the use of Rowat and Selverston-type of central pattern generator (CPG) to control locomotion. It focuses on the role of afferent exteroceptive and proprioceptive signals in the dynamic phase synchronization in CPG legged robots. The sensori-motor neural network architecture is evaluated to control a two-joint planar robot leg that slips on a rail. Then, the closed loop between the CPG and the mechanical system allows to study the modulation of rhythmic patterns and the effect of the sensing loop via sensory neurons during the locomotion task. Firstly simulations show that the proposed architecture easily allows to modulate rhythmic patterns of the leg, and therefore the velocity of the robot. Secondly, simulations show that sensori-feedbacks from foot/ground contact of the leg make the hip velocity smoother and larger. The results show that the Rowat–Selverston-type CPG with sensory feedbacks is an effective choice for building adaptive neural CPGs for legged robots.
The work presented in this paper is an important step toward a better understanding of a compact hydraulic robotic actuator, based on the Integrated Electro-Hydraulic Actuator (IEHA) developed by Alfayad and Ouezdou [1]. The novel advantage of this actuator is being highly compact and autonomous (no need for central hydraulic source), while keeping a good power to weight ratio. In order to present and develop the working dynamics of this actuator, a highly detailed mathematical model for the system is presented. The proposed model is simulated using MATLAB-Simulink software to identify the effect of the internal system parameters on system dynamics and prepare an input-output test-bed model. Such test-bed model is used to obtain the transfer function of the system and its order. Analysis of the effects of the main parameters was carried out and a lower order of the system was identified. A linear model of the system is derived and validated using system identification technique. Finally, a robust motion controller is applied on the proposed linear model and the simulation results are presented. The distance between the shaft center + Piston contact point on the surface of the Housing [mm]; m: The end effectors mass [kg]; me: Mass of the carriage of the IEHA [kg]; N: Number of micropistons [−]; Prp: The pressure of the oil at the intake channel of the radial pump [bar]; Pc: The pressure difference between the two Cylinder chambers A and B [bar]; Ps: High pressure line [bar]; PA: Pressure in chamber A of the micro-pistons [bar]; PB: Pressure in chamber B of the micro-pistons [bar]; Pi: The micropistons of the IEHA [-]; Qmac: The average macroscopic flow of the N Micro-pistons [m3/s]; Qmic: The average microscopic flow of the N Micro-pistons [m3/s]; Qe: The flow from microvalve into the carriage Chambers [m3/s]; Qeleak: Leakage flow from the carriage micro-Pistons to the body of the actuator [m3/s]; Q: The flow from the micro-pump [m3/s]; QleakA: The leakage between the carriage and output Cylinder + the one between the hydraulic Chamber to the micro pumps body [m3/s]; Qpileak: The leakage flow of the micro-pistons in the Radial pump [m3/s]; Qleak: The internal leakage between the two Chambers of the cylinder [m3/s]; Rb: The radius of the carriage [mm]; rtig: The micro-valve radius [mm]; rrp: The radius of the in-out opening section of the micro-pump [mm]; Sc: The surface area of the linear hydraulic Cylinder's piston [cm2]; Se: The surface area of the carriage chambers [cm2]; Spi: The active area of a single piston [mm2]; V: Fluid volume [m3]; ve: The volume of the chamber of the carriage [m3]; vpi: The volume of the IEHA micro-pistons [m3]; vc: The volume of the chamber of the output Cylinder [m3]; X: Micro-valve input displacement [mm]; Y: The end effector (output load cylinder) Position [mm]; β: Bulk modulus of elasticity [MPa]; Ɵ: The angle between the piston and the Reference axe [degrees]; ω: Rotational speed of the shaft [rad/sec]; ρ: The density of the oil [kg/m3]; ζ: The actual pressure in the piston chamb...
For robotic applications, hydraulic actuation is still an open research issue. A hydraulic actuation solution based on integrated hydraulics is presented in the form of an integrated electro-hydraulic Actuator (IEHA). This actuator tackles some issues related to compactness and self-contained autonomous robotic systems. One of its primary uses is the actuation of the hydraulic humanoid robot HYDROïD. Primary experimental validation of the IEHA actuator has suffered from several low-performance issues. The main reason for such performance is due to that inner friction forces and leakage losses were not addressed or modeled yet. In this paper, a detailed dynamic model of the IEHA actuator is needed to identify the mechanical components that affect the volumetric and mechanical efficiencies of the actuator. The dynamic model focuses on the dynamics of essential parameters and components like the micro-pump pistons, their internal leakage, and friction during actuation. The design parameters of these mechanical parts are optimally calculated to produce a new enhanced dynamic model. The new IEHA model is verified through simulation to actuate the ankle pitch rotation of the robot. A genetic algorithm is used to optimize the geometrical and design parameters while enhancing the overall mechanical and volumetric efficiencies of the IEHA. The IEHA model was able to generate a locomotion gait cycle successfully for a walking speed of 1.17 m/s. Simulation results have shown that the design enhancements led to 95% and 97% volumetric and mechanical efficiencies respectively. Finally, the new enhanced IEHA prototype is tested experimentally. The output flow of the new prototype has improved by approximately 58%.
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