Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 256-262.DOI: 10.3778/j.issn.1002-8331.2106-0040

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Path Planning for Indoor Mobile Robot with Improved Deep Reinforcement Learning

CHENG Yi, HAO Mimi   

  1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
  • Online:2021-11-01 Published:2021-11-04



  1. 天津工业大学 控制科学与工程学院,天津 300387


An improved deep reinforcement learning algorithm based on deep image information is proposed in order to solve the problem of poor exploration ability and sparse environment state space of traditional deep reinforcement learning in path planning of the mobile robot in unknown indoor environment. The depth image information and target position information directly obtained by the Kinect visual sensor are used as the input of the network. The linear velocity and angular velocity of the robot are used as the output of the next action command. An improved reward and punishment function is designed to increase the reward value of the algorithm. The state space is optimized. To a certain extent, it alleviates the problem of reward sparsity. The simulation results show that the improved algorithm can improve the exploration ability of the robot and optimize the path trajectory. The robot can effectively avoid obstacles and plan a shorter path. Compared with DQN algorithm, the average path length in simple environment is shortened by 21.4%. The average path length in complex environment is reduced by 11.3%.

Key words: path planning, depth image information, Kinect visual sensor, deep reinforcement learning, reward and punishment function, exploration ability



关键词: 路径规划, 深度图像信息, Kinect 视觉传感器, 深度强化学习, 奖惩函数, 探索能力