基于脉冲神经网络的自适应趋药性轮廓跟踪算法
Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks
摘要：
在本文中，我们提出了一个尖峰神经网络（SNN）的自主导航，启发的蠕虫秀丽隐杆线虫的趋化网络。特别是，我们专注于轮廓跟踪的问题，其中机器人必须达到并随后遵循所需的浓度设定点。过去的计划，只使用klinokinesis可以有效地遵循轮廓，但需要过多的时间来达到设定点。我们解决这个缺点，提出了一种新的自适应klinotaxis机制，建立在以前提出的梯度攀升电路。我们展示了我们的klinotaxis电路可以自主配置执行梯度上升，梯度下降，随后被禁用，无缝集成与上述klinokinesis电路。我们还将速度调节（orthokinesis），以进一步提高轮廓跟踪性能。因此，第一次，我们提出了一个模型，成功地整合klinokinesis，klinotaxis和orthokinesis。我们证明，通过轮廓跟踪模拟，我们提出的方案达到设定点的时间减少了2.4倍，同时从设定点的平均偏差减少了8.7倍。
英文摘要：
In this paper we present a Spiking Neural Network (SNN) for autonomous navigation, inspired by the chemotaxis network of the worm Caenorhabditis elegans. In particular, we focus on the problem of contour tracking, wherein the bot must reach and subsequently follow a desired concentration setpoint. Past schemes that used only klinokinesis can follow the contour efficiently but take excessive time to reach the setpoint. We address this shortcoming by proposing a novel adaptive klinotaxis mechanism that builds upon a previously proposed gradient climbing circuit. We demonstrate how our klinotaxis circuit can autonomously be configured to perform gradient ascent, gradient descent and subsequently be disabled to seamlessly integrate with the aforementioned klinokinesis circuit. We also incorporate speed regulation (orthokinesis) to further improve contour tracking performance. Thus for the first time, we present a model that successfully integrates klinokinesis, klinotaxis and orthokinesis. We demonstrate via contour tracking simulations that our proposed scheme achieves an 2.4x reduction in the time to reach the setpoint, along with a simultaneous 8.7x reduction in average deviation from the setpoint.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
