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.

翻译：在社交环境中导航移动机器人仍是一项具有挑战性的任务，其难点在于人机交互的复杂性。现有针对拥挤动态环境设计的运动规划器大多侧重于选择最优速度以在避免碰撞的同时抵达目标，但并未明确考虑高层导航行为（如从左侧或右侧绕行、让他人先行或抢先通过等）。本文提出了一种新颖的运动规划器，通过融合表征不同人类绕行策略的拓扑学差异化路径来实现智能导航。该规划器利用基于真实人类运动数据训练的深度神经网络模型，选择最接近人类行为的拓扑类别，从而确保社交智能与情境感知的导航能力。系统通过基于优化的局部规划器实时优化所选路径，保障导航行为与期望社交模式的无缝契合。通过这种方式，我们将感知与局部规划从决策过程中解耦。我们使用真实数据评估了网络的预测精度，并在仿真环境与真实平台中对比其他前沿规划器验证了导航性能。实验表明，本规划器展现了社交合宜的行为特征，并呈现出平滑且显著优越的表现。