It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the environmental information around the robot, including its shape and observable appearances of obstacles. We also introduce the pedestrian map that specifies the movements of pedestrians around the robot. By applying both maps as inputs of the neural network, we show that a navigation policy can be trained to better interact with pedestrians following different collision avoidance strategies. We evaluate our approach under multiple scenarios both in the simulator and on an actual robot. The results show that our approach allows the robot to successfully interact with various pedestrians and outperforms compared methods in terms of the success rate.
翻译:移动机器人在人群中行走是一项挑战。 现有方法通常假定行人遵循预先确定的避免碰撞战略,如社会力模型(SFM)或最佳对冲避免碰撞战略(ORCA ) 。 然而,行人通常需要进一步改进其性能,以实际应用为目的,行人采用多种不同的避免碰撞战略。在本文中,我们提议了一种基于地图的深度强化学习方法,用于与各种行人一起进行人群觉醒机器人导航。我们使用传感器地图来代表机器人周围的环境信息,包括其形状和可观察到的障碍外观。我们还介绍了行人图,其中指明了机器人周围行人的流动情况。通过将这两张地图用作神经网络的投入,我们表明可以培训航行政策,以便根据不同的避免碰撞战略更好地与行人互动。我们在模拟器中和在实际机器人的多种情景下评估我们的方法。结果显示,我们的方法使得机器人能够成功地与各种行人互动,并在成功率方面与方法相比出差。