Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot

Abstract

Tiling robotics have been deployed in autonomous complete area coverage tasks such as floor cleaning, building inspection, and maintenance, surface painting. One class of tiling robotics, polyomino-based reconfigurable robots, overcome the limitation of fixed-form robots in achieving high-efficiency area coverage by adopting different morphologies to suit the needs of the current environment. Since the reconfigurable actions of these robots are produced by real-time intelligent decisions during operations, an optimal path planning algorithm is paramount to maximize the area coverage while minimizing the energy consumed by these robots. This paper proposes a complete coverage path planning (CCPP) model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost. To this end, a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) layers is trained using Actor Critic Experience Replay (ACER) reinforcement learning algorithm. The results are compared with existing approaches which are based on the traditional tiling theory model, including zigzag, spiral, and greedy search schemes. The model is also compared with the Travelling salesman problem (TSP) based Genetic Algorithm (GA) and Ant Colony Optimization (ACO) schemes. The proposed scheme generates a path with lower cost while also requiring lesser time to generate it. The model is also highly robust and can generate a path in any pretrained arbitrary environments.