# Enhancing Legged Robot Locomotion Through Smooth Transitions Using Spiking Central Pattern Generators

## TL;DR

This paper introduces a spiking neural network to help a six-legged robot smoothly switch between walking styles, improving balance and energy efficiency.

## Contribution

A novel method using spiking neural networks and SPIKE-synchronization to enable smooth and energy-efficient gait transitions in legged robots.

## Key findings

- The spiking CPG network successfully generates three distinct gaits (walk, jog, run) with coordinated movements.
- SPIKE-synchronization identifies optimal transition points, making gait changes imperceptible and energy-efficient.
- The system dynamically adapts to terrain rigidity, validated through real-time testing on a physical hexapod robot.

## Abstract

In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot’s balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot’s actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot’s legs to detect the rigidity of the terrain it navigates. Based on the terrain’s rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot.