论文标题
Frozone：人群中的无冻结，对行人友好的航行
Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds
论文作者
论文摘要
我们提出了Frozone，这是一种小说算法，用于处理机器人在密集的场景和人群中导航时出现的冻结机器人问题（FRP）。我们的方法感官并明确预测行人的轨迹，并构建一个潜在的冻结区（PFZ）；机器人可以冻结或对人类感到震惊的空间区域。我们的配方计算偏差速度以避免PFZ，这也解释了社会限制。此外，Frozone是为配备有限传感范围和视场的传感器的机器人而设计的。我们确保机器人的偏差是有限的，从而避免了突然的角运动，这可能导致周围障碍物的感知数据丢失。我们已经将冰冻与基于强化的学习（DRL）相撞方法相结合，并使用我们的混合方法来处理各种密度的人群。我们的整体方法在密集的环境中导致平稳且无碰撞的导航。我们在挑战室内方案中评估了方法在模拟和实际差速器机器人方面的性能。我们强调了方法在成功率（提高50％），行人友好性（100％提高）和冻结率（> 80％降低）方面的成功率（提高50％）的好处。
We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and explicitly predicts the trajectories of pedestrians and constructs a Potential Freezing Zone (PFZ); a spatial zone where the robot could freeze or be obtrusive to humans. Our formulation computes a deviation velocity to avoid the PFZ, which also accounts for social constraints. Furthermore, Frozone is designed for robots equipped with sensors with a limited sensing range and field of view. We ensure that the robot's deviation is bounded, thus avoiding sudden angular motion which could lead to the loss of perception data of the surrounding obstacles. We have combined Frozone with a Deep Reinforcement Learning-based (DRL) collision avoidance method and use our hybrid approach to handle crowds of varying densities. Our overall approach results in smooth and collision-free navigation in dense environments. We have evaluated our method's performance in simulation and on real differential drive robots in challenging indoor scenarios. We highlight the benefits of our approach over prior methods in terms of success rates (up to 50% increase), pedestrian-friendliness (100% increase) and the rate of freezing (> 80% decrease) in challenging scenarios.
