  The natural interaction between robots and pedestrians in the process of
autonomous navigation is crucial for the intelligent development of mobile
robots, which requires robots to fully consider social rules and guarantee the
psychological comfort of pedestrians. Among the research results in the field
of robotic path planning, the learning-based socially adaptive algorithms have
performed well in some specific human-robot interaction environments. However,
human-robot interaction scenarios are diverse and constantly changing in daily
life, and the generalization of robot socially adaptive path planning remains
to be further investigated. In order to address this issue, this work proposes
a new socially adaptive path planning algorithm by combining the generative
adversarial network (GAN) with the Optimal Rapidly-exploring Random Tree (RRT*)
navigation algorithm. Firstly, a GAN model with strong generalization
performance is proposed to adapt the navigation algorithm to more scenarios.
Secondly, a GAN model based Optimal Rapidly-exploring Random Tree navigation
algorithm (GAN-RRT*) is proposed to generate paths in human-robot interaction
environments. Finally, we propose a socially adaptive path planning framework
named GAN-RTIRL, which combines the GAN model with Rapidly-exploring random
Trees Inverse Reinforcement Learning (RTIRL) to improve the homotopy rate
between planned and demonstration paths. In the GAN-RTIRL framework, the
GAN-RRT* path planner can update the GAN model from the demonstration path. In
this way, the robot can generate more anthropomorphic paths in human-robot
interaction environments and has stronger generalization in more complex
environments. Experimental results reveal that our proposed method can
effectively improve the anthropomorphic degree of robot motion planning and the
homotopy rate between planned and demonstration paths.
