Enhancing Micro/Nanorobot Navigation for Medical Applications Using Reinforcement Learning

In this study, we explore the application of reinforcement learning (RL) to enhance the navigation of micro/nanorobots within medical environments. Micro and nanorobots, operating at scales comparable to cells, hold significant promise for tasks such as targeted drug delivery, minimally invasive surgery, and precision manufacturing. We propose a control strategy utilizing swarm intelligence and potential fields to direct the movements of these robots. The environment is initialized with hexagonal units representing maze rooms, where the initial and goal positions of the robots are randomly determined. Each robot's state includes distances from walls, other robots, and the endpoint, as well as their position within the maze. Actions involve adjusting the magnitude and direction of potential fields that influence robot movement. The RL framework employs the proximal policy optimization (PPO) algorithm, optimizing a reward function designed to encourage goal achievement and obstacle avoidance. Results from training indicate the RL agent's effectiveness in guiding the robots through complex environments. Comparative analysis with traditional pathfinding algorithms, such as A* and Ant Colony Optimization (ACO), demonstrates the superior adaptability and efficiency of the RL-based approach, particularly in scenarios with unknown maps. This research underscores the potential of RL in advancing the precision and reliability of micro/nanorobot swarms for advanced medical applications.