Distributed Filter-Based Integrated Cooperative Localization and Resilient Formation Control for Multi-Robot Systems Under DoS Attacks

This paper investigates resilient cooperative localization and formation control in multi-robot systems with sensor limitations under denial-of-service attacks. We propose a novel resilient framework that integrates an enhanced distributed Kalman consensus filter with formation control, explicitly ad dressing cyber threats and physical sensing constraints. Different from existing approaches, the proposed algorithm achieves more accurate position estimations by incorporating process noise in robot's dynamics and measurement noise from onboard sensors. These enhanced estimations are then used to design a robust formation controller. Additionally, a weighted fusion mechanism is introduced to improve state estimation by effectively combining observational data. Both simulation results and physical experiments confirm the effectiveness of the proposed algorithm, showcasing substantial improvements in localization accuracy and the ability to maintain desired formations.