STP4:基于在密集的人群中预测行人轨迹的空间时间路径规划
Yuta Sato1,2, Yoko Sasaki1, Hiroshi Takemura1,2
1AIRC, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Koto-ku, Japan.
PeerJ. Computer science
|December 11, 2023
概括
这项研究引入了一种通过预测行人运动在人群中自主移动机器人导航的新方法. 这种方法使机器人导航更顺,更快,在拥挤的环境中提高效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 在拥挤的环境中自主移动机器人导航,由于不可预测的人类运动,存在重大挑战.
- 现有的方法往往在动态环境中难以实时适应和有效的路径规划.
研究的目的:
- 开发和评估一种用于密集人群的新型自主移动机器人导航系统.
- 通过预测行人轨迹来提高机器人导航安全性和效率.
主要方法:
- 拟议的方法将行人轨迹预测与时空路径规划相结合.
- 预测的行人路径被转换成一个时间序列的成本地图,用于机器人指导.
- 路径规划器在不需要长期轨迹预测的情况下运行.
主要成果:
- 在科学博物馆的现实世界机器人测试证实了轨迹预测模块的有效性.
- 模拟表明,相比于传统的二维路径规划,拟议的规划方法在50人群中实现了26.4%更快的到达时间.
- 导航策略导致机器人运动平稳,没有不规则的躲避机动.
结论:
- 开发的方法为在动态,拥挤的空间中自主移动机器人导航提供了强大的解决方案.
- 预测性轨迹分析和自适应性路径规划显著提高了导航效率和流性.
- 这种方法有望提高机器人在公共和高流量区域的性能.
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