Optimal Motion Planning for Estimating Relative States of Mobile Agents With Range Measurements
Yü Tian, Zixuan Zhong, Zaiyue Yang · 2025
In this paper, we focus on the problem of optimal trajectory design for mobile agents to achieve precise relative localization in anchor-free environments. Given the challenges posed by range measurement noise and the lack of prior information about neighboring agents, we propose an approach that maximizes localization accuracy under a fixed number of movements. The method leverages the Fisher Information Matrix (FIM) to optimize the agent's trajectory and is extended to scenarios involving multiple agents. In particular, we quantitatively establish a link between localization variance and the number of movements, and the simulation demonstrate the superiority of our algorithm.