占用意识的神经距离感知用于操纵器在托卡马克真空容器中避开障碍物
1School of Mechanical Engineering & Automation, Beihang University, Xueyuan Road 37, Beijing 100191, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一个占用意识的神经距离感知 (ONDP) 框架,用于在狭窄的空间中精确的机器人导航. ONDP提供高速,准确的距离传感,克服了复杂环境中传统方法的局限性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
背景情况:
- 精确的距离感知和碰撞推理对于机器人操纵在狭窄空间 (如托卡马克真空容器) 中至关重要.
- 传统的基于网格或voxel的方法存在局限性,包括离散化工件,不连续性和高内存使用量,阻碍了连续的几何推理和基于优化的规划.
研究的目的:
- 提出一种新的占用感知神经距离感知 (ONDP) 框架,作为一个紧的,可差异化的几何传感器,用于在类似反应堆的环境中避免操纵器障碍.
- 引入物理分层采样策略,通过基于工程约束的数据分布来解决受限环境中的采样不足问题.
主要方法:
- 开发了一个占用感知神经距离感知 (ONDP) 框架,利用以物理单位 (毫米) 训练的轻量级神经网络.
- 实施了物理分层采样策略,使用加权配额和对称边界约束来实现安全关键地区的强有力的梯度学习.
- 采用平均绝对误差损失函数,以确保严格遵守工程公差.
主要成果:
- 通过高频距离和正常查询 (大批次超过15kHz,比基于网格的查询加快5911倍) 实现了大约2-3毫米的近地精度.
- 在对托卡马克船模型的实验中证明了连续的,亚厘米的几何准确性.
- 验证了ONDP框架对实时感知,监控和运动规划的能力.
结论:
- 该ONDP框架为有限空间机器人操纵提供了几何传感的突破.
- 它的高精度,速度和连续的几何真实性使其能够与轨迹优化和模型预测控制无集成.
- 这一进步显著提高了机器人在具有挑战性的环境中 (如托卡马克室内) 的机器人能力.
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