Abstract
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.
Abstract (translated)
在社交环境中导航移动机器人仍然是一个具有挑战性的任务，因为人机交互的复杂性。为了解决这个问题，大多数为拥挤和动态环境设计的运动规划器都集中于选择最佳速度以达到目标，同时避免碰撞，但这些规划器没有明确考虑高级导航行为（避免穿过左侧或右侧，让别人通过或在其前面经过等）。在本文中，我们提出了一个新颖的运动规划器，它包含了代表人类行为多样性导航策略的拓扑学不同的路径。规划器通过基于真实世界人类运动数据训练的深度神经网络模型选择最优秀的拓扑学类，确保社会智能和上下文意识导航。我们的系统通过实时优化基于拓扑的运动规划器来优化所选路径，确保无缝适应期望的社会行为。 在这种程度上，我们解耦了感知和局部规划与决策过程。我们在真实世界中评估网络的预测准确性。此外，我们还评估了该规划器在模拟和真实世界平台上的导航能力，将其与最先进的规划器进行比较。我们证明了我们的规划器表现出社会可接受的行为，表现出平滑和令人印象深刻的表现。
URL
https://arxiv.org/abs/2404.16705
