Navigation of micro-robot swarms for targeted delivery using reinforcement learning
This work addresses the challenge of precise swarm control for medical applications like targeted drug delivery, representing an incremental improvement in applying existing RL methods to a specific domain.
The paper tackled the problem of controlling micro-robot swarms for targeted drug delivery by using reinforcement learning algorithms (PPO and RPO) to navigate swarms of 4 to 25 microswimmers under hydrodynamic effects, achieving successful navigation to circular absorbing targets with demonstrated robustness to variations in target location and size.
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.