Abstract
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.
Abstract (translated)
自动导航过程中机器人与行人之间的自然交互对移动机器人的智能发展至关重要。为了实现这一目标，机器人需要全面考虑社会规则并确保行人的心理舒适。在机器人路径规划领域的研究成果中，基于学习的社交适应算法在某些特定的人机交互环境中表现良好。然而，人机交互场景具有多样性，并且在日常生活中不断变化，因此对机器人社交适应路径规划的推广还需要进一步研究。为了解决这个问题，本文提出了一种将生成对抗网络（GAN）与最优快速探索随机树（RRT*）导航算法相结合的新社交适应路径规划算法。首先，提出了一个具有强大泛化性能的GAN模型，以适应更多的场景。其次，提出了一个基于最优快速探索随机树导航算法的GAN模型，用于生成人机交互环境中的路径。最后，我们提出了名为GAN-RTIRL的社会适应路径规划框架，将GAN模型与快速探索随机树逆强化学习（RTIRL）相结合，以提高计划路径和演示路径之间的同构性。在GAN-RTIRL框架中，GAN-RRT*路径规划器可以从演示路径更新GAN模型。这样，机器人可以在人机交互环境中生成更多具有人类特征的路径，在更复杂的环境中的泛化能力更强。实验结果表明，我们所提出的方法可以有效提高机器人运动规划的拟人程度和计划路径与演示路径之间的同构程度。
URL
https://arxiv.org/abs/2404.18687
