Towards reuse and recycling of lithium-ion batteries: tele-robotics for disassembly of electric vehicle batteries

Disassembly of electric vehicle batteries is a critical stage in recovery, recycling and re-use of high-value battery materials, but is complicated by limited standardisation, design complexity, compounded by uncertainty and safety issues from varying end-of-life condition. Telerobotics presents an avenue for semi-autonomous robotic disassembly that addresses these challenges. However, it is suggested that quality and realism of the user's haptic interactions with the environment is important for precise, contact-rich and safety-critical tasks. To investigate this proposition, we demonstrate the disassembly of a Nissan Leaf 2011 module stack as a basis for a comparative study between a traditional asymmetric haptic-"cobot" master-slave framework and identical master and slave cobots based on task completion time and success rate metrics. We demonstrate across a range of disassembly tasks a time reduction of 22%-57% is achieved using identical cobots, yet this improvement arises chiefly from an expanded workspace and 1:1 positional mapping, and suffers a 10%-30% reduction in first attempt success rate. For unbolting and grasping, the realism of force feedback was comparatively less important than directional information encoded in the interaction, however, 1:1 force mapping strengthened environmental tactile cues for vacuum pick-and-place and contact cutting tasks.

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of ∼21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

Targeting effect on gait parameters in healthy individuals and post-stroke hemiparetic individuals

BACKGROUND

A targeting effect may occur in any gait analysis trial where the participant is instructed to step in a particular area or a clearly marked target is in their path. The targeting effect may affect the gait parameters and any variability being studied in regard to the participants. There are few studies examining this effect for healthy subjects and none for special populations.

METHODS

This study aimed to investigate if any targeting effects occurred in healthy and stroke-survivor populations. Eight male participants were recruited, four of whom exhibited right-hand side hemiparesis resulting from stroke. Each participant performed a series of gait trials at a comfortable walking pace after being made aware of the force plate in the centre of the walkway. The participants gait was then analysed and compared before and after the target force plate.

RESULTS

The results of the trials showed significant variations (p < 0.005) in the spatiotemporal gait parameters in both the healthy and stroke-survivor groups indicating a targeting effect.

CONCLUSIONS

The effects were similar in both groups with the step speed and length being slower and shorter for the targeting step compared to the step after the force plate.

An optimized design of a parallel robot for gait training

The guidelines for enhancing robot-assisted training for post-stroke survivors head towards increasing exercise realism and variability; in particular lower limb rehabilitation needs the patient to feel challenged to adapt his locomotion and dynamic balance capabilities to different virtual ground scenarios. This paper proposes a design for a robot whose end-effector acts as a footplate to be in permanent contact with the user's foot during practice: the structure is such that it enables the user's foot to rotate around three axis, differently from what is currently available in the research for gait training; the parallel kinematic structure and the dimensional synthesis allow a suitable range of motion and aim at limiting device mass, footprint and reaction forces on the actuators when rendering virtual ground. The employed methodology has been validated using ground reaction forces data relative to stroke survivors.