# SpiKon-E: Hybrid Soft Artificial Muscle Control Using Hardware Spiking Neural Network

## TL;DR

This paper introduces a new hybrid artificial muscle system using a hardware spiking neural network for improved control in soft robotics.

## Contribution

A novel SMA-based linear actuator, hybrid soft actuation structure, and hardware SNN for endpoint force control in soft robotics.

## Key findings

- The new SMA-based linear actuator achieves higher displacements than traditional SMA wire systems.
- The hybrid system with a PneuNet and force sensor provides real-time pressure feedback and improved performance.
- The hardware SNN control system outperforms traditional control methods in actuator displacement.

## Abstract

Artificial muscles play a key role in the future of humanoid robotics and medical devices, with research on wire-driven joints leading the field. While electric servo motors were once at the forefront, the focus has shifted toward materials that react to changes in the environment (smart materials), including pneumatic silicone actuators and temperature-reactive metallic alloys, aiming to replicate human muscle actuation for improved performance. Initially designed for rigid actuators, control strategies were adapted to address the unique dynamics of artificial muscles. Although current controllers offer satisfactory performance, further optimization is necessary to mimic natural muscle control more rigorously. This study details the design and implementation of a novel system that mimics biological muscle. This system is designed to replicate the full range of motion and control functionalities, which can be utilized in various applications. This research has three significant contributions in the field of sustainable soft robotics. First, a novel shape memory alloy-based linear actuator is introduced, which achieves significantly higher displacements compared to traditional SMA wire-driven systems through a guiding mechanism. Second, this linear actuator is integrated into a hybrid soft actuation structure, which features a silicone PneuNet as the end effector and a force sensor for real-time pressure feedback. Lastly, a hardware Spiking Neural Network (HW-SNN) is utilized to control the exhibited force at the actuator’s endpoint. Experimental results showed that the displacement with the control system is significantly higher than that of the traditional control-based shape memory alloy systems. The system evaluation demonstrates good performance, thus advancing actuation and control in humanoid robotics.