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artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Intelligent Automation & Soft Computing 2023年第1期35卷 1135-1150页

作者： A.Sivaranjani B.Vinod Department of Robotics and Automation Engineering PSG College of TechnologyCoimbatore641004India Department of Electrical and Electronics Engineering PSG College of TechnologyCoimbatore641004India

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an *** Learning methods enable a state action function in mobile robots suited to their *** trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its *** Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the *** it requires a large number of training *** research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the artificial Potential Field(APF)***,a TB3 Waffle Pi DQN is built and trained to reach the *** the APF shortest path algorithm is incorporated into the DQN *** proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN *** performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 *** performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.

关键词： artificial potentialfield deep reinforcement learning mobile robot turtle bot deep Q network path prediction

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