Navigation of micro-robot swarms for targeted delivery using reinforcement learning

TL;DR

This paper demonstrates how reinforcement learning algorithms, specifically PPO and RPO, can effectively control micro-robot swarms for targeted delivery, even with limited information and under various challenging conditions.

Contribution

It introduces the application of RL algorithms to micro-robot swarm navigation, incorporating hydrodynamic effects and curriculum learning for improved performance.

Findings

RL algorithms successfully navigate swarms of 4, 9, and 16 microswimmers.

Curriculum learning enhances navigation performance.

Robustness tests show effective target localization despite randomness.

Abstract

Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus important and artificial intelligence can help us perform this task successfully. In this work, we use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic effects, controlled by their orientation, towards a circular absorbing target. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size. We use curriculum learning to improve upon the performance and demonstrate the same in learning to navigate a swarm of 25 swimmers and steering the swarm to exemplify the manoeuvring capabilities of the RL model.