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用于USV导航的协同层次人工智能框架:关闭旋转变压器感知,T-ASTAR规划和能量感知TD3控制之间的循环
Haonan Ye1, Hongjun Tian1, Qingyun Wu1
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
本研究介绍了一种协同的人工智能框架,用于自主无人地面车辆 (USV) 导航. 人工智能增强了感知,规划和控制,在复杂的海洋环境中进行更安全,更有效的操作.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 海洋工程 海洋工程
- 自主系统 自主系统
背景情况:
- 自主无人地面车辆 (USV) 操作需要先进的导航,指导和控制.
- 挑战包括基于传感器的物体检测和在复杂的海洋条件下对能量有意识的路径规划.
研究的目的:
- 为强大的USV自主性提出一种新的协同人工智能框架.
- 为了提高复杂的海洋工程场景的导航,指导和控制.
主要方法:
- 集成的Swin转换器用于从视觉数据生成语义风险地图.
- 变压器增强的A星 (T-ASTAR) 用于能源意识的静态路径规划.
- 适应域的TD3代理,具有能量意识奖励,用于动态路径优化和避开障碍.
- 为了计算效率的CUDA加速.
主要成果:
- 与基准相比,实现了30%更短的路线和70%更少的转.
- 减少了64.7%的动态碰撞.
- 在使用CUDA加速生成地图时显示了215倍的速度改进.
结论:
- 协同人工智能框架显著提升了USV的自主性.
- 该方法解决了动态环境中的关键挑战,对象回避和能源受限制的操作.
- 人工智能组件的层次整合对于有效的无人驾驶海上系统至关重要.
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