Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152896
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dc.contributor.authorWang, Xiaoyeen_US
dc.date.accessioned2021-10-14T04:46:50Z-
dc.date.available2021-10-14T04:46:50Z-
dc.date.issued2021-
dc.identifier.citationWang, X. (2021). UR robot manipulator path planning via reinforcement learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152896en_US
dc.identifier.urihttps://hdl.handle.net/10356/152896-
dc.description.abstractWith the increasing demand of people's intelligent life, there have been new developments in robotic field. Combined with neural network, traditional reinforcement learning algorithms is also improved to adapt to the situation of high-dimensional continuous space. As the most basic step of manipulator motion control, path planning has also become more intelligent with the development of reinforcement learning. First of all, the path planned in the Cartesian space needs to be converted into a path of joint angles, which will play an important role in subsequent motion control. We use the D-H parameter method to model the kinematics of the manipulator, find out its forward and inverse kinematics and verify its correctness. Secondly, we discuss the obstacle avoidance effect and its limitations of the traditional obstacle avoidance method artificial potential field method in a known environment, and use the RRT algorithm to improve it when it falls into a local optimum. Thirdly, we use the DDPG algorithm in reinforcement learning to plan the trajectory, which is suitable for continuous state and action situation. Through the training of the model, the simple path planning between 2 given points of the robot arm is completed by training the RL model. We analyze the problems of sparse rewards and propose some possible solutions.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationISM-DISS-02242en_US
dc.subjectEngineering::Electrical and electronic engineering::Control and instrumentation::Roboticsen_US
dc.titleUR robot manipulator path planning via reinforcement learningen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorHu Guoqiangen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailGQHu@ntu.edu.sgen_US
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