在拥挤的未知动态环境中基于预测信息的无人机自主轨迹规划研究
Jianing Tang1, Songyan Yang1, Shijie Chen1
1Yunnan Key Laboratory of Unmanned Autonomous Systems, School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650500, China.
这项研究引入了一种新方法,用于在人群中安全无人机 (UAV) 飞行. 它将行人预测与基于梯度的规划相结合,以提高在复杂环境中的无人机轨迹规划成功率.
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
- 计算机视觉 计算机视觉
背景情况:
- 无人驾驶飞行器 (UAV) 由于无法预测的人群动态而面临着自主超低空飞行方面的挑战.
- 密集的人群构成复杂,动态的障碍,威胁到无人机安全和运营可行性.
研究的目的:
- 开发一种集成的无人机轨迹规划方法,用于在拥挤的环境中安全的自动飞行.
- 通过将准确的行人轨迹预测与强大的基于梯度的规划相结合,增强无人机导航.
主要方法:
- 一个对比分布隐性代码生成器 (CDLCG) 已被开发用于行人轨迹预测,从历史数据推断未来的分布.
- 设计了一种基于自适应的梯度的无人机轨迹规划方法,将不同优化阶段和障碍类型的自适应性成本权重纳入其中.
- 验证是使用公共数据集,OptiTrack运动捕捉系统实验和不同人群密度的Gazebo模拟进行的.
主要成果:
- CDLCG模型准确地预测了行人轨迹,通过模拟和物理实验验验证.
- 基于自适应梯度的规划方法显著提高了在动态,拥挤的环境中无人机轨迹规划的成功率.
- 拟议的方法有效地平衡了轨迹的平滑性,安全性和可行性,确保了无人机的安全操作.
结论:
- 综合方法为复杂,动态的人群场景中的自主无人机导航提供了可靠的解决方案.
- 这项研究有助于在具有挑战性的现实环境中实现更安全,更有效的无人机操作.
- 开发的方法提高了无人机在人群中自主和安全操作的能力.
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