一个基于语义细分和翅膀运动的规划框架,用于在越过障碍物的地形中使用关节追踪机器人
Pu Zhang1, Junhang Liu1, Yongling Fu1
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
这项研究引入了一个新的计划框架,用于关节轨道机器人 (ATR) 通过整合跨越障碍的机动来自主导航复杂的地形. 该系统减少了对人类控制的依赖,提高了挑战性环境中的效率和适应能力.
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
- 机械工程 机械工程
背景情况:
- 带有双活跃的关节轨道机器人 (ATR) 擅长穿越复杂的地形.
- 目前的ATR操作通常严重依赖于远程的人类控制来解决障碍.
- 提高自主导航能力对于在具有挑战性的环境中提高运营效率至关重要.
研究的目的:
- 为ATR开发一个新的规划框架,使自主全球路径生成能够与集成的跨越障碍机动进行整合.
- 减少在复杂地形上对ATR操作的远程人工控制的依赖.
- 提高ATR在苛刻环境中的适应性和运营效率.
主要方法:
- 使用轻量级的DeepLab V3+架构与边缘感知模块进行海拔地图的实时语义细分.
- 一个简化的机器人-地形接触模型,通过接触点穿越来快速计算姿势.
- 快速探索随机树 (RRTs) 结合飞运动平滑性成本,用于协作路径和机动规划.
主要成果:
- 该框架成功使爬虫机器人能够区分平面,穿越障碍物和难以穿越的地形区域.
- 自主路线规划导致相比手动遥控器更短的旅行时间.
- 计划中的路径显示出更好的稳定性和更低的飞能量消耗.
结论:
- 拟议的框架大大提高了ATR在复杂地形上的自主导航.
- 它为减少人类干预和提高机器人的性能提供了实际解决方案.
- 这一进步对于在苛刻和非结构化环境中部署ATR具有实质价值.
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