SHINE：用于拥挤环境中导航的社会同源性识别
SHINE: Social Homology Identification for Navigation in Crowded Environments
AI总结：
针对移动机器人社交环境导航缺乏高层行为考量的问题，提出SHINE规划器，通过拓扑路径与人类行为模仿实现社交智能导航，经仿真和真实实验验证性能优异。
摘要：
移动机器人在社交环境中的导航仍是一项具有挑战性的任务，原因在于人机交互的复杂性。大多数为拥挤动态环境设计的运动规划器，都专注于选择最佳速度以抵达目标同时避免碰撞，但并未明确考虑高层导航行为（例如从左侧或右侧绕行、让他人先行或抢先通过等）。在本研究中，我们提出了一种新型运动规划器，该规划器纳入了代表人类周围多样化导航策略的拓扑相异路径。规划器利用在真实世界人类运动数据上训练的深度神经网络模型，选择最能模仿人类行为的拓扑类别，确保具备社交智能且感知上下文的导航能力。我们的系统通过基于优化的局部规划器实时优化所选路径，确保无缝契合预期的社交行为。通过这种方式，我们将感知和局部规划与决策过程解耦。我们使用真实世界数据评估了网络的预测准确率。此外，我们还在仿真环境和真实世界平台中评估了导航能力，并将其与其他最先进的规划器进行了对比。实验表明，我们的规划器展现出符合社交期望的行为，且性能流畅出色。
英文摘要：
Navigating mobile robots in social environments remains a challenging task due to the intricacies of human-robot interactions. Most of the motion planners designed for crowded and dynamic environments focus on choosing the best velocity to reach the goal while avoiding collisions, but do not explicitly consider the high-level navigation behavior (avoiding through the left or right side, letting others pass or passing before others, etc.). In this work, we present a novel motion planner that incorporates topology distinct paths representing diverse navigation strategies around humans. The planner selects the topology class that imitates human behavior the best using a deep neural network model trained on real-world human motion data, ensuring socially intelligent and contextually aware navigation. Our system refines the chosen path through an optimization-based local planner in real time, ensuring seamless adherence to desired social behaviors. In this way, we decouple perception and local planning from the decision-making process. We evaluate the prediction accuracy of the network with real-world data. In addition, we assess the navigation capabilities in both simulation and a real-world platform, comparing it with other state-of-the-art planners. We demonstrate that our planner exhibits socially desirable behaviors and shows a smooth and remarkable performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
