Robot Crowd Environment Navigation Based on LSTM Optimization
WANG Qiang
FU Hao
LIU Jun
HE Haodong
Abstract：The existing robot social navigation methods have the problems of exploration capability needs more improvement and the risk assessment mechanism of pedestrian's behavior is incomplete.The both may cause occasional collision and robot naviga-tion policy being in local optimal value.To this end,this paper proposes a robot crowd environment navigation algorithm based on LSTM Optimization.Specifically,a danger factor is defined to optimize LSTM in deep reinforcement learning.Then,the risk assess-ment of pedestrian's behavior is more consistent with human social norms,which improves the safety of robot navigation.By combin-ing with SAC,a LSTM-SAC framework is constructed to improve the stochastic exploration capability and accelerate convergence of this algorithm.Finally,the algorithm is tested and compared with the latest algorithm in the simulation environment.
Keywords：robotsocial navigationdeep reinforcement learningSACLSTM
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3013-3019 )