A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning

Abstract

This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot's adaptability and path optimization capabilities in dynamic environments. In particular, Re-DQN improves the stability of the training process through a dynamic incentive layer and achieves more comprehensive area coverage and shorter planning times in high-dimensional continuous state spaces. Simulation results show that Re-DQN outperforms the other algorithms in terms of performance, convergence speed, and stability, making it a robust solution for comprehensive coverage path planning. Future work will focus on testing and optimizing Re-DQN in more complex environments and exploring its application in multi-robot systems to enhance collaboration and communication.