让服务机器人兼顾物理安全与社交舒适，实现自然避让人类。
Socially Aware Motion Planning for Service Robots Using LiDAR and RGB-D Camera
- 融合激光雷达与深度相机数据，用卡尔曼滤波估计人位置和速度。
- 用非对称高斯函数建模个人空间，动态调整避障范围。
- 适合需与人共处的机器人导航场景，如医院、商场。
在共享环境中与人类协同工作的服务机器人需要兼顾物理安全与社交规范的导航系统。本文提出一种社会感知运动规划系统，通过激光雷达和RGB-D相机提取人体位置，并利用卡尔曼滤波融合信息进行人体状态估计。基于人体状态，采用非对称高斯函数建模个人空间，将该模型作为动态窗口法（Dynamic Window Approach）的输入，生成机器人轨迹。实验表明，机器人能在动态环境中与人类并行移动，同时尊重其物理与心理舒适度。
原文摘要 · Abstract (English)
Service robots that work alongside humans in a shared environment need a navigation system that takes into account not only physical safety but also social norms for mutual cooperation. In this paper, we introduce a motion planning system that includes human states such as positions and velocities and their personal space for social-aware navigation. The system first extracts human positions from the LiDAR and the RGB-D camera. It then uses the Kalman filter to fuse that information for human state estimation. An asymmetric Gaussian function is then employed to model human personal space based on their states. This model is used as the input to the dynamic window approach algorithm to generate trajectories for the robot. Experiments show that the robot is able to navigate alongside humans in a dynamic environment while respecting their physical and psychological comfort.
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