让机器人在人群中更安全地导航，通过预测行人动作并实时规划路径。
Enhancing Social Robot Navigation with Integrated Motion Prediction and Trajectory Planning in Dynamic Human Environments
- 将行人位置、朝向和运动信息融入路径规划目标函数。
- 在仿真中成功避开动态障碍物，导航安全性显著提升。
- 适合研究人机交互与智能机器人导航的开发者使用。
在动态人类环境中安全导航对移动服务机器人至关重要，社交导航是其中的关键。本文提出一种融合运动预测与轨迹规划的集成方法，充分利用社会可接受轨迹预测与定时弹性带（TEB）的优势，将行人的位置、朝向及运动信息纳入TEB算法的目标函数，并设计社交约束以保障导航安全。通过物理仿真，采用定量与定性指标评估系统性能，结果表明该方法在避免人类与动态障碍物方面表现优异，有效保障了机器人安全通行。代码已开源： https://github.com/thanhnguyencanh/SGan-TEB.git
原文摘要 · Abstract (English)
Navigating safely in dynamic human environments is crucial for mobile service robots, and social navigation is a key aspect of this process. In this paper, we proposed an integrative approach that combines motion prediction and trajectory planning to enable safe and socially-aware robot navigation. The main idea of the proposed method is to leverage the advantages of Socially Acceptable trajectory prediction and Timed Elastic Band (TEB) by incorporating human interactive information including position, orientation, and motion into the objective function of the TEB algorithms. In addition, we designed social constraints to ensure the safety of robot navigation. The proposed system is evaluated through physical simulation using both quantitative and qualitative metrics, demonstrating its superior performance in avoiding human and dynamic obstacles, thereby ensuring safe navigation. The implementations are open source at: \url{https://github.com/thanhnguyencanh/SGan-TEB.git}
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