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.

翻译：自主导航过程中机器人与行人的自然交互对于移动机器人的智能化发展至关重要，这要求机器人充分考虑社会规则并保障行人的心理舒适度。在机器人路径规划领域的研究成果中，基于学习的社交自适应算法在特定人机交互环境中表现良好。然而，日常生活中的人机交互场景多样且不断变化，机器人社交自适应路径规划的泛化能力仍有待进一步研究。为解决该问题，本文提出一种结合生成对抗网络（GAN）与最优快速探索随机树（RRT*）导航算法的新型社交自适应路径规划算法。首先，提出一种具有强泛化性能的GAN模型，使导航算法能够适应更多场景；其次，提出基于GAN模型的最优快速探索随机树导航算法（GAN-RRT*），用于在人机交互环境中生成路径；最后，提出名为GAN-RTIRL的社交自适应路径规划框架，该框架结合GAN模型与快速探索随机树逆强化学习（RTIRL）以提高规划路径与示范路径之间的同伦率。在GAN-RTIRL框架中，GAN-RRT*路径规划器能够根据示范路径更新GAN模型。通过这种方式，机器人能够在人机交互环境中生成更具拟人化特征的路径，并在更复杂的环境中具有更强的泛化能力。实验结果表明，本文提出的方法能够有效提升机器人运动规划的拟人化程度以及规划路径与示范路径之间的同伦率。