Control of a Nature-inspired Scorpion using Reinforcement Learning

TL;DR

This paper presents a scorpion-inspired terrestrial robot with a reinforcement learning controller, enabling it to navigate rough terrain efficiently for surveillance and exploration tasks.

Contribution

It introduces a novel scorpion-inspired robot model with RL-based control for navigation in complex environments.

Findings

Efficient navigation demonstrated in simulation

Successful modeling of leg, tail, and claw mechanisms

RL controller adapts to unknown terrains

Abstract

A terrestrial robot that can maneuver rough terrain and scout places is very useful in mapping out unknown areas. It can also be used explore dangerous areas in place of humans. A terrestrial robot modeled after a scorpion will be able to traverse undetected and can be used for surveillance purposes. Therefore, this paper proposes modelling of a scorpion inspired robot and a reinforcement learning (RL) based controller for navigation. The robot scorpion uses serial four bar mechanisms for the legs movements. It also has an active tail and a movable claw. The controller is trained to navigate the robot scorpion to the target waypoint. The simulation results demonstrate efficient navigation of the robot scorpion.