Actuation Design Methodology for Haptic Interfaces and Rehabilitation Systems

This paper introduces a methodology and a software framework intended to optimize and speed up the design process of a haptic interface or a rehabilitation system. Starting from an initial mechanical design the procedure allows to export the kinematic and dynamic properties of the robotic system in a simulation environment. The software receives as additional input the Cartesian or joints trajectories and generates as output the required torques at the joints. From the recorded measurements the program extracts the torque ranges necessary to choose a suitable actuation system for the robot. The possibility to run the simulation in batch modality allows also to define different optimization techniques that may be used to reduce the overall system weight or increase its payloa

Design methodology for haptic interfaces and rehabilitation systems

Designing the actuation system for a robot is a crucial step during the machine development. This phase is even more critical if the robotic system is represented by a haptic interface or a rehabilitation system that are meant to operate in near contact with the human body. Generally, the design process takes into account a realistic model of the limb interacting with the interface [1,2] and includes as set of simulations intended to study the coupled interface-limb system. The result of these simulations allows fixing important requirements necessary to design the interface. Among them the number of Degrees of Freedom (DOF), the joints type and configuration, and the links dimensions. When the kinematic of the interface is ready a proper dynamic model [3] needs to be formalized and computed. This is demanded to find out other important design elements like the optimal actuators displacement and dimensioning

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

ADVANCED STEPS IN BIPED ROBOTICS: INNOVATIVE DESIGN AND INTUITIVE CONTROL THROUGH SPRING-DAMPER ACTUATOR

This paper focuses on the study and design of an anthropomorphical light biped robot. The robot presents a total of twelve degree of freedom that will permit it to act a walk in a three dimensional space, right now tested only in simulation. Each joint resemble the functionalities of the human articulation and is moved by tendon connected with actuator located in the robot’s pelvis. We implemented and tested an innovative actuator that permits to set the joint stiffness in real time maintaining a simple position control paradigm. The controller is able to estimate the external load measuring the spring deflection and demonstrated to be particularly robust respect to system uncertainties, such as inertia value changes. Comparing the resulting control law with existing models we found several similarities with the Equilibrium Point Theory

New Joint Design to Create a More Natural and Efficient Biped

This paper presents a human-oriented approach to design the mechanical architecture and the joint controller for a biped robot. Starting from the analysis of the human lower limbs, we figured out which features of the human legs are fundamental for a correct walking motion, and can be adopted in the mechanical design of a humanoid robot. We focus here on the knee, designed as a compliant human-like knee instead of a classical pin-joint, and on the foot, characterised by the mobility and lightness of the human foot. We implemented an elastic actuator, with a simple position control paradigm that sets the joint stiffness in real time, and developed the basic controller. Results in simulation are discussed. In our approach the robot gains in adaptability and energetic efficiency, which are the most challenging issues for a biped robot

A Neuromorphic Motion Controller for a Biped Robot

Here we propose a neuromorphic control system for a medium size humanoid robot under development in the Robotics and Mechatronics Department at Nazarbayev University and in cooperation with Politecnico di Milano