Energy regenerative damping in variable impedance actuators for long-term robotic deployment

Energy efficiency is a crucial issue towards longterm deployment of compliant robots in the real world. In the context of variable impedance actuators (VIAs), the main focus has been on improving energy efficiency through reduction of energy consumption. However, the harvesting of dissipated energy in such systems remains under-explored. This study proposes a novel variable damping module design enabling energy regeneration in VIAs by exploiting the regenerative braking effect of DC motors. The proposed damping module uses four switches to combine regenerative and dynamic braking, in a hybrid approach that enables energy regeneration without reduction in the range of damping achievable. A physical implementation on a simple VIA mechanism is presented in which the regenerative properties of the proposed module are characterised and compared against theoretical predictions. To investigate the role of variable regenerative damping in terms of energy efficiency of longterm operation, experiments are reported in which the VIA equipped with the proposed damping module performs sequential reaching to a series of stochastic targets. The results indicate that the combination of variable stiffness and variable regenerative damping is preferable to achieve the optimal trade-off between task performance and energy efficiency. Use of the latter results in a 25% performance improvement on overall performance metrics (incorporating reaching accuracy, settling time, energy consumption and regeneration), over comparable schemes where either stiffness or damping are fixed.

A Hybrid Dynamic-regenerative Damping Scheme for Energy Regeneration in Variable Impedance Actuators

Increasing research efforts have been made to improve the energy efficiency of variable impedance actuators (VIAs) through reduction of energy consumption. However, the harvesting of dissipated energy in such systems remains underexplored. This study proposes a novel variable damping module design enabling energy regeneration in VIAs by exploiting the regenerative braking effect of DC motors. The proposed damping module uses four switches to combine regenerative and dynamic braking, in a hybrid approach that enables energy regeneration without reduction in the range of damping achievable. Numerical simulations and a physical experiment are presented in which the proposed module shows an optimal trade-off between task performance and energy efficiency.

A Library for Constraint Consistent Learning

This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task and null-space parts, and (iii) uncovering the underlying null space control policy. It is a powerful tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use in a systematic way. This paper documents the implementation of the library, tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain indepth experience in learning constraints and underlying control policies in the context of redundant robotic systems.

Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that a constraint in a task is not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of demonstrations, such that they can be used to optimise an autonomous controller to avoid singularity, and thereby unpredictable behaviour when carrying out the task, without having to explicitly know the tasks constraint. The proposed approach avoids singularity by maximising task manipulability throughout the motion of the constrained system, and is not limited to kinematic systems. Its benefits are demonstrated through comparisons with other control policies.

Learning Null Space Projections in Operational Space Formulation

In recent years, a number of tools have become available that recover the underlying control policy from constrained movements. However, few have explicitly considered learning the constraints of the motion and ways to cope with unknown environment. In this paper, we consider learning the null space projection matrix of a kinematically constrained system in the absence of any prior knowledge either on the underlying policy, the geometry, or dimensionality of the constraints. Our evaluations have demonstrated the effectiveness of the proposed approach on problems of differing dimensionality, and with different degrees of non-linearity.