论文标题
部分可观测时空混沌系统的无模型预测
Whisker-Inspired Tactile Sensing for Contact Localization on Robot Manipulators
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Perceiving the environment through touch is important for robots to reach in cluttered environments, but devising a way to sense without disturbing objects is challenging. This work presents the design and modelling of whisker-inspired sensors that attach to the surface of a robot manipulator to sense its surrounding through light contacts. We obtain a sensor model using a calibration process that applies to straight and curved whiskers. We then propose a sensing algorithm using Bayesian filtering to localize contact points. The algorithm combines the accurate proprioceptive sensing of the robot and sensor readings from the deflections of the whiskers. Our results show that our algorithm is able to track contact points with sub-millimeter accuracy, outperforming a baseline method. Finally, we demonstrate our sensor and perception method in a real-world system where a robot moves in between free-standing objects and uses the whisker sensors to track contacts tracing object contours.
