Gradient-based Taxis Algorithms for Network Robotics

TL;DR

This paper explores gradient-based taxis algorithms for locating network nodes in wireless environments, analyzing measurement errors, convergence, and practical implementation in network robotics.

Contribution

It introduces a gradient-based taxis algorithm tailored for network robotics and discusses its convergence and adaptability to complex objectives.

Findings

The algorithm converges under certain conditions.

Measurement errors impact gradient estimation accuracy.

Experimental results demonstrate practical applicability.

Abstract

Finding the physical location of a specific network node is a prototypical task for navigation inside a wireless network. In this paper, we consider in depth the implications of wireless communication as a measurement input of gradient-based taxis algorithms. We discuss how gradients can be measured and determine the errors of this estimation. We then introduce a gradient-based taxis algorithm as an example of a family of gradient-based, convergent algorithms and discuss its convergence in the context of network robotics. We also conduct an exemplary experiment to show how to overcome some of the specific problems related to network robotics. Finally, we show how to adapt this framework to more complex objectives.