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Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot
برنامه ریزی کامل مسیر پوشش با استفاده از یادگیری تقویتی برای تمیز کاری و نگهداری ربات مبتنی بر Tetromino-2020 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. Keywords: Tiling robotics | Cleaning and maintenance | Inspection | Path planing | Reinforcement learning |
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