Open-Loop Position Control in Collaborative, Modular Variable-Stiffness-Link (VSL) Robots

Abstract

Collaborative robots (cobots) open up new avenues in the fields of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation and in collaboration with humans. The integration and control of variable stiffness elements allow inherently safe interaction. Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collisions. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions (e.g., pick and place). Hence, the study of open-loop position control for VSL robots is crucial to achieve high levels of safety, accuracy and hardware cost-efficiency in pHRI applications. In this paper, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during taskoriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links.

A critical review of modelling methods for flexible and rigid link manipulators

“…The LPM also does not develop sufficiently accurate dynamic model especially for less number of lumped masses and need to increase the number of lumped masses to represent the system well which imposes the difficulty of the modelling process. Even the Lagrangian [120,122,123,127,144,146,147] RLM Lagrange's equation [148][149][150][151][152][153][154][155][156] Newtonian method [150,151,[157][158][159][160] Kinematic method [149,157,158,[161][162][163][164][165][166][167] method is easy and appropriate for obtaining the equations of motion of FLMs, it is essential to use an approximation method to express the deflection of FLMs. Modelling RLMs is much easier than modelling FLMs as RLMs do not have any deflection or error on the manipulator's tip caused by the flexibility of the links.…”

Review of machine learning methods in soft robotics

“…They used a LSTM to localize the magnet at the tip of the robot compared to existing analytic and hybrid methods [ 124 ]. In [ 125 ], a hybrid model for controlling a modular collaborative Variable-Stiffness-Link (VSL) robots has been proposed. It consisted of forward kinematics and inverse kinematics whose models are 7-layer FNN.…”

Adaptive Neural Backstepping Control of Variable Stiffness Actuator

“…Variable stiffness actuators (VSAs) have received substantial attention in numerous robotic applications because they can transmit torque and change stiffness simultaneously. VSAs also have many advantages over traditional fixed stiffness actuators, including increases in the safety of human-robot interactions, the capacity for positioning precision and disturbance resistance, and the energy efficiency for locomotion [1][2][3][4][5][6]. According to the design principle, current VSAs can be divided into two categories such as antagonistic-type VSAs and serial-type VSAs [7].…”