# Path Planning of Cleaning Robot with Reinforcement Learning

arXiv: https://arxiv.org/abs/2208.08211 · HTML: https://mlsanity.com/paper/2208.08211/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Reinforcement Learning for Navigation of Mobile Robot with LiDAR](https://mlsanity.com/paper/2112.02954/) (2021)

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning for Predicting Traffic Accidents](https://mlsanity.com/paper/2212.04677/) (2022)
- [Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources](https://mlsanity.com/paper/2209.07744/) (2022)
- [Kick-motion Training with DQN in AI Soccer Environment](https://mlsanity.com/paper/2212.00389/) (2022)
- [Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error](https://mlsanity.com/paper/2212.13175/) (2022)
- [Enhanced Transformer Architecture for Natural Language Processing](https://mlsanity.com/paper/2310.10930/) (2023)
- [Applying Reinforcement Learning to Optimize Traffic Light Cycles](https://mlsanity.com/paper/2402.14886/) (2024)
- [Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm](https://mlsanity.com/paper/2409.01046/) (2024)

## Abstract (arXiv)

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).