人間と移動ロボットの共存

Autonomous mobile robots navigating through human crowds are required to foresee the future trajectories of surrounding pedestrians and accordingly plan safe paths to avoid any possible collision. This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder–decoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks to the special structure of BiRNN enhanced by the attention mechanism, a proximity-independent model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employs information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the resulting predicted trajectories and the learned attention scores to prove the advantages of BiRRNs on recognizing social interactions.

Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-the-art works have focused on pedestrian detection, tracking, and trajectory prediction. However, only a few studies have been conducted on recognizing some specific types of dangerous behaviors exhibited by pedestrians. Here, we propose a tracking enhanced detection method to recognize people using their smartphones while walking, referred to as smartphone zombie. Features used for pedestrian detection usually involve the rotation variance problem, and in this paper, the drawback is handled by employing motion information from multi-object tracking. The proposed solution has been validated through experiments performed on a newly collected dataset. Results showed that our detector can learn a distinct pattern of the appearance of smartphone zombies. Thus, it can successfully detect them outperforming the existed detection method.

In order for mobile robots to coexist with humans, both safety and efficiency should be satisfied. We propose a method to predict pedestrian movement for collision avoidance of mobile robots and pedestrians. In the proposed method, the pedestrian trajectories are measured and a database of human movement tendencies is generated. The database is applied to the prediction of future pedestrian movement. To decrease the initial time cost for database generation, environmental geometric configuration is considered in the form of virtual forces. To verify the usefulness of the proposed method, we generated the database based on five-hour observation and conducted three types of experiments based on the generated database. The first experiment showed the prediction performance of the proposed method and proved the method guaranteed the safety. The second experiment showed that the proposed method satisfied both the safety and efficiency through the comparative simulations. The third experiment showed the method could apply to the real mobile robot.