论文标题
通过概率预测行人的未来运动，人群中的预期导航
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
论文作者
论文摘要
在动态环境中人类和机器人共存至关重要的是代理人了解彼此的行为并预测其运动的能力。本文介绍了随机过程预期导航（SPAN），该框架使非自称机器人能够在人群的环境中导航，同时预料并考虑行人的运动模式。为此，我们学习了一个预测模型，以预测连续的随机过程，以模拟行人的未来运动。预期的行人位置用于进行偶然的约束碰撞检查，并将其纳入时间碰撞的控制问题中。还集成了占用图，以允许使用静态障碍物检查概率碰撞。我们演示了在拥挤的仿真环境中跨度的能力，以及现实世界中的行人数据集。
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset.
