To achieve robot navigation in crowded environments having high densities of moving people, it is insufficient to simply consider humans as moving obstacles and avoid collisions with them. That is, the impact of an approaching robot on human movements must be considered as well. Moreover, various navigation methods have been tested in their own environments in the literature, which made them difficult to compare with one another. Thus, we propose an autonomous robot navigation method in densely crowded environments for data-based predictions of robot-human interactions, together with a reproducible experimental test under controlled conditions. Based on localized positional relationships with humans, this method extracts multiple alternative paths, which can implement either following or avoidance, and selects an optimal path based on time efficiency. Each path is selected using neural networks, and the various paths are evaluated by predicting the position after a given amount of time has elapsed. These positions are then used to calculate the time required to reach a certain target position to ensure that the optimal path can be determined. We trained the predictor using simulated data and conducted experiments using an actual mobile robot in an environment where humans were walking around. Using our proposed method, collisions were avoided more effectively than when conventional navigation methods were used, and navigation was achieved with good time efficiency, resulting in an overall reduction in interference with humans. Thus, the proposed method enables an effective navigation in a densely crowded environment, while collecting human-interaction experience for further improvement of its performance in the future.

为了在人流密集的拥挤环境中实现机器人导航，仅将人视为运动障碍物并避免与它们发生碰撞是不够的。也就是说，还必须考虑接近机器人对人体运动的影响。此外，文献中已经在其自身环境中测试了各种导航方法，这使得它们很难相互比较。因此，我们提出了一种在拥挤环境中的自主机器人导航方法，用于基于数据的机器人与人的互动预测，以及在可控条件下的可再现实验测试。基于与人类的局部位置关系，此方法提取了多个替代路径，这些路径可以实现跟随或回避，并根据时间效率选择一条最佳路径。使用神经网络选择每条路径，并在经过给定的时间量后通过预测位置来评估各种路径。然后将这些位置用于计算达到某个目标位置所需的时间，以确保可以确定最佳路径。我们使用模拟数据训练了预测变量，并在人类四处走动的环境中使用了实际的移动机器人进行了实验。使用我们提出的方法，与使用传统的导航方法相比，可以更有效地避免碰撞，并且可以以良好的时间效率实现导航，从而总体上减少了对人的干扰。因此，所提出的方法能够在人群密集的环境中进行有效的导航，