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基于LSTM-注意网络的自动地面车辆面临突然和移动障碍物的在线轨迹规划方法
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了一种新的LSTM-Attention网络方法,用于自动地面车辆 (AGV) 轨迹规划. 它可以实时避免突然和移动障碍的紧急障碍.
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
- 控制系统 控制系统
背景情况:
- 自动地面车辆 (AGV) 需要强大的避难障碍,以确保安全运行.
- 现有的方法经常与实时性能和动态环境作斗争.
研究的目的:
- 为 AGV 开发一种新的在线轨迹规划方法,以处理突然和移动的障碍物.
- 为了确保高实时性能和障碍回避机动的最佳性.
- 提高 AGV 避障系统的适用性.
主要方法:
- 使用长期短期记忆-注意力 (LSTM-Attention) 网络进行轨迹规划.
- 在LSTM-Attention网络的线下培训中,使用生成的AGV避障轨迹数据集进行线下培训.
- 实施旋转坐标系统,以管理不同方向的障碍物和移动的障碍物.
主要成果:
- LSTM-Attention网络有效地学习了车辆障碍信息和最佳控制行动之间的映射.
- 拟议的方法实现了实时性能和AGV的最佳轨迹规划.
- 旋转坐标系统显著扩大了该方法对各种场景的适用性.
- 通过广泛的模拟和物理实验进行验证,证实了有效性.
结论:
- 基于新的LSTM-Attention方法为AGV避难障碍提供了有效和高效的解决方案.
- 该方法展示了强大的实时能力和适应动态环境的适应性.
- 这项研究有助于推进安全可靠的自主导航系统.
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