  Recently, as the demand for cleaning robots has steadily increased, therefore
household electricity consumption is also increasing. To solve this electricity
consumption issue, the problem of efficient path planning for cleaning robot
has become important and many studies have been conducted. However, most of
them are about moving along a simple path segment, not about the whole path to
clean all places. As the emerging deep learning technique, reinforcement
learning (RL) has been adopted for cleaning robot. However, the models for RL
operate only in a specific cleaning environment, not the various cleaning
environment. The problem is that the models have to retrain whenever the
cleaning environment changes. To solve this problem, the proximal policy
optimization (PPO) algorithm is combined with an efficient path planning that
operates in various cleaning environments, using transfer learning (TL),
detection nearest cleaned tile, reward shaping, and making elite set methods.
The proposed method is validated with an ablation study and comparison with
conventional methods such as random and zigzag. The experimental results
demonstrate that the proposed method achieves improved training performance and
increased convergence speed over the original PPO. And it also demonstrates
that this proposed method is better performance than conventional methods
(random, zigzag).
