Abstract
Current study results on robotics have been published. According to news reporting from Fujian, People's Republic of C hina, by NewsRx journalists, research stated, "The path planning of mobile robot s helps robots to perceive environment using the information obtained from senso rs and plan a route to reach the target." Funders for this research include Young And Middle-aged Teachers in Fujian Provi nce; Department of Education of Fujian Province; National Natural Science Founda tion Cultivation Program of Jimei University. The news journalists obtained a quote from the research from Jimei University: " With the increasing difficulty of task, the environment the mobile robots face b ecomes more and more complex. Traditional path planning methods can no longer me et the requirements of mobile robot navigation in complex environment. Deep rein forcement learning (DRL) is introduced into robot navigation However, it may be time-consuming to train DRL model when the environment is very complex and the e xisting environment may differ from the unknown environment. In order to handle the robot navigation in heterogeneous environment, this paper utilizes deep tran sfer reinforcement learning (DTRL) for mobile robot path planning. Compared with DRL, DTRL does not require the distribution of the existing environment is the same as that of the unknown environment. Additionally, DTRL can transfer the kno wledge of existing model to new scenario to reduce the training time."
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NSTL