发表机构
Toronto Metropolitan University; University of Freiburg（多伦多都会大学; 弗莱堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出用闭式连续时间神经网络估计空中连续体操纵器在无人机空气动力扰动下的末端执行器位置，相比MLP和GRU，RMSE分别降低39.52%和20.62%，验证了连续时间学习的有效性。
AI 中文摘要
本文研究了在无人机（UAV）产生的空气动力效应下，空中连续体操纵器（ACM）末端执行器位置估计的时间神经网络。在静止（旋翼关闭）和自由悬停条件下，跨连续体机器人（CR）构型和无人机高度收集了一个实验数据集，提供了有无空气动力残差的末端执行器位置测量。为建立标称框架，评估了具有逐渐丰富应变基的应变参数化运动学模型，以平衡模型复杂性和预测精度。所选标称模型随后作为基线，用于使用闭式连续时间（CfC）神经网络进行三维位置残差估计，并以多层感知器（MLP）和门控循环单元（GRU）作为比较。在未见测试实验中，CfC在五个随机种子上实现了22.00±1.70毫米的均方根误差（RMSE），而MLP为36.38±3.58毫米，GRU为27.72±2.92毫米，分别减少了39.52%和20.62%。这些结果证明了连续时间学习相对于静态和离散时间学习方法，在空气动力扰动下进行末端执行器位置估计的有效性。
英文摘要
This paper investigates temporal neural networks for \mbox{end-effector} position \mbox{estimation} of an aerial continuum manipulator (ACM) operating under aerodynamic effects induced by the unmanned aerial vehicle (UAV). An experimental dataset is collected under stationary (\mbox{rotor-off}) and \mbox{free-hovering} conditions across continuum robot (CR) configurations and UAV altitudes, providing \mbox{end-effector} position measurements with and without aerodynamic residuals. To establish a nominal framework, \mbox{strain-parameterized} kinematic models with progressively richer strain bases are evaluated to balance model complexity and prediction accuracy. The selected nominal model then serves as the baseline for 3D position residual estimation using a \mbox{closed-form} \mbox{continuous-time} (CfC) neural network, with a multilayer perceptron (MLP) and a gated recurrent unit (GRU) used for comparison. On unseen test experiments, the CfC achieves an RMSE of \(22.00\pm1.70~\mathrm{mm}\) over five random seeds, compared with \(36.38\pm3.58~\mathrm{mm}\) for the MLP and \(27.72\pm2.92~\mathrm{mm}\) for the GRU, corresponding to reductions of \(39.52\%\) and \(20.62\%\), respectively. These results demonstrate the effectiveness of \mbox{continuous-time} learning for \mbox{end-effector} position estimation under aerodynamic disturbances relative to static and \mbox{discrete-time} learning methods.
