Socially aware robot navigation framework in crowded and dynamic environments: A comparison of motion planning techniques

Hong Thai Le, Duy Thao Nguyen, Xuan-Tung Truong
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引用次数: 2

Abstract

We present a comparison of navigation capability for mobile robots in crowded environments between the hybrid reciprocal velocity obstacle (HRVO) model and the social force model (SFM). The SFM determines the velocities to drive a mobile robot to its goal destination by using information about the position of surrounding humans and obstacles; meanwhile, the HRVO model considers the current position and velocity to calculate the new velocity for the mobile robot. The comparison is evaluated by conducting experiments in simulation environment. The experimental results have demonstrated that using additional information help the mobile robot achieve better performance when avoiding obstacles in crowded environments.
拥挤和动态环境中的社会感知机器人导航框架:运动规划技术的比较
研究了混合互易速度障碍(HRVO)模型和社会力模型(SFM)在拥挤环境下移动机器人导航能力的比较。SFM利用周围人类和障碍物的位置信息,确定驱动移动机器人到达目标目的地的速度;同时,HRVO模型考虑当前位置和速度,计算移动机器人的新速度。并在仿真环境下进行了实验验证。实验结果表明,使用附加信息可以帮助移动机器人在拥挤环境中获得更好的避障性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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