Communication packet loss concealment for pattern generation with robotic swarms

Abstract

In swarm robotics, a multitude of very simple robots often move to achieve pre-defined geometric patterns while communicating with each other. Robots are able to estimate their position and relocate themselves by obtaining other robots' position information. However, as is the often the case in wireless communication, robots cannot receive other robots' position information reliably. Thus, they need to deal with the packet loss problem and estimate the missing information in near-real time. Several nonlinear filters such as extended Kalman filters or particle filters have been widely used for many years. But these filters require enormous computational complexity, so it is difficult to be applied to low-cost mobile robots with limited computational and memory resources. To overcome this problem, we propose an extrapolating method with reduced computational cost yet high estimation accuracy. Specifically, we propose a novel exponential function concealment with linear blends, and validate its effectiveness and rate of convergence through extensive simulations.