构建可量化行人受机器人影响轨迹偏移的仿真框架
Evaluating Robot Influence on Pedestrian Behavior Models for Crowd Simulation and Benchmarking
- 基于增强社交力模型引入机器人作用力，构建SRFM模型
- 在5种场景下量化不同导航算法导致的行人轨迹偏移
- 适用于评估机器人导航策略对人群行为的影响
机器人在行人间穿行会引发行人轨迹偏离。现有方法难以客观测量未见场景下的偏离程度。为此，本文提出一种仿真框架，通过重复模拟并基准测试不同导航算法驱动的机器人对行人轨迹的影响。采用改进的社交力模型（SFM），加入机器人作用力分量，形成社交机器人力模型（SRFM），其参数基于JRDB数据集的行人轨迹学习得到。在5种不同场景中，使用含与不含机器人作用力的SRFM进行行人模拟，客观测量机器人引起的轨迹偏离。本研究证明了客观量化行人对机器人反应的可行性，并利用该仿真训练两种强化学习策略，与传统导航模型进行对比评估。
原文摘要 · Abstract (English)
The presence of robots amongst pedestrians affects them causing deviation to their trajectories. Existing methods suffer from the limitation of not being able to objectively measure this deviation in unseen cases. In order to solve this issue, we introduce a simulation framework that repetitively measures and benchmarks the deviation in trajectory of pedestrians due to robots driven by different navigation algorithms. We simulate the deviation behavior of the pedestrians using an enhanced Social Force Model (SFM) with a robot force component that accounts for the influence of robots on pedestrian behavior, resulting in the Social Robot Force Model (SRFM). Parameters for this model are learned using the pedestrian trajectories from the JRDB dataset. Pedestrians are then simulated using the SRFM with and without the robot force component to objectively measure the deviation to their trajectory caused by the robot in 5 different scenarios. Our work in this paper is a proof of concept that shows objectively measuring the pedestrian reaction to robot is possible. We use our simulation to train two different RL policies and evaluate them against traditional navigation models.
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