Cooperative Navigation for Ground and Aerial Robots

Abstract

This study presents a novel approach to cooperative navigation for ground and aerial robots, utilizing sensor fusion techniques based on the Kalman filter. The primary objective is to enhance navigation accuracy by enabling real-time sharing and integration of positional information among robots. Through simulation results, we demonstrate that our method significantly improves the estimation of each robot's pose, effectively addressing the challenges posed by measurement noise and uncertainty. The results indicate that collaborative navigation not only optimizes individual robot performance but also enhances overall system resilience in complex environments.