Group-based Motion Prediction for Navigation in Crowded Environments
Group-based Motion Prediction for Navigation in Crowded Environments
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发表时间：
2021-07
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通讯作者：
Allan Wang;Christoforos Mavrogiannis;Aaron Steinfeld
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作者：
Allan Wang;Christoforos Mavrogiannis;Aaron Steinfeld
我们专注于规划的问题，在一个动态的多智能体环境，如行人场景的机器人的运动。使机器人能够安全地导航，并在社会上兼容的方式在这样的场景需要一个表示，占展开的多智能体动态。现有的解决这个问题的方法往往采用微观模型的运动预测的原因对其他代理的个人行为。虽然这些模型可以在轨迹预测基准中实现高跟踪精度，但它们通常缺乏对在拥挤场景中展开的组结构的理解。受心理学完形理论的启发，我们建立了一个模型预测控制框架（G-MPC），利用基于组的预测机器人运动规划。我们进行了广泛的模拟研究，涉及一系列具有挑战性的导航任务，从两个真实世界的行人数据集提取的场景。我们说明，G-MPC使机器人能够实现统计上显着更高的安全性和更低数量的组入侵比一系列的基线具有个人的行人运动预测模型。最后，我们表明，G-MPC可以处理噪声激光雷达扫描估计没有显着的性能损失。
We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. Inspired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion planning. We conduct an extensive simulation study involving a series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables a robot to achieve statistically significantly higher safety and lower number of group intrusions than a series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.