%0 Journal Article %A JIN Dongyin %A LI Yue %A SHAO Zhenzhou %A SHI Zhiping %A GUAN Yong %T Design of Reinforcement Learning Reward Function for Trajectory Planning of Robot Manipulator %D 2022 %R 10.3778/j.issn.1002-8331.2102-0307 %J Computer Engineering and Applications %P 302-308 %V 58 %N 19 %X Aiming at the problems of low learning efficiency of robotic manipulator trajectory planning methods based on deep reinforcement learning and poor robustness of planning strategies, this paper proposes a robotic manipulator trajectory planning method based on voice reward function. The voice instructions are defined as the different states of planning task, and modeled using the Markov chain. It provides the global guidance for the trajectory planning, reduces the blindness of deep reinforcement learning. Meanwhile, the proposed method combines the global information based on the voice and local information of relative distance to design the reward function, considering the degree of fitness between the relative distance and voice guidance. Experimental results demonstrate that the proposed reward function improves the robustness and convergence rate of manipulator trajectory planningeff ectively. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2102-0307