整合车辆-地形交互和模糊成本适应,以实现可靠的路径规划
Hongchao Zhang1,2, Qiancheng Zhao2, Yinghao Wu2
1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种适应性路径规划算法,用于在3D地形上导航的无人地面车辆 (UGV). 该方法提高了复杂环境中的驾驶稳定性和任务匹配.
科学领域:
- 机器人技术和自主系统
- 人工智能的人工智能
- 地理空间导航是指地理空间导航.
背景情况:
- 在复杂的3D地形中,无人地面车辆 (UGV) 的路径规划面临着由于地形波动和动态环境的挑战.
- 确保车辆的稳定性和适应各种多任务要求对于稳健的UGV运行至关重要.
- 现有的算法往往缺乏根据任务类型和地形相互作用动态调整路径规划成本的适应性.
研究的目的:
- 为在具有挑战性的三维 (3D) 地形中运行的UGV开发适应性路径规划算法.
- 通过考虑地形-UGV底盘干扰来提高车辆的驾驶稳定性.
- 通过学习模型,使多任务模式的动态成本调整成为可能.
主要方法:
- 构建了一个干扰模型来评估UGV底盘与3D地形的相互作用,并考虑稳定性.
- 设计了一个动态的成本调整机制,包括一个自动任务识别的学习模型.
- 在不同的任务模式中测试算法性能,模拟的环境具有稀疏和高密度的障碍.
主要成果:
- 拟议的算法产生了更顺,更安全,更适合任务的轨迹路径.
- 保持路径可行性,同时表现出更好的适应性和稳定性.
- 在不同的障碍密度和多任务驾驶条件下验证了有效性.
结论:
- 适应性路径规划算法为UGV在复杂,非结构化的3D环境中提供了卓越的性能.
- 动态成本调整机制提高了适应多任务要求的能力.
- 该方法为在各种条件下安全高效的UGV导航提供了强大的解决方案.
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