Concurrent Learning With Triangle-Based Cooperative Correction for Multi-Robot Relative Localization and Formation Control

Abstract

This letter presents a framework for cooperative relative localization and formation control of multi-robot systems in GPS-denied environment. First, a concurrent learning-based scheme with sliding-window sampling strategy is developed to exploit historical information, relaxing the strict persistent excitation requirement to a finite excitation condition. Second, a novel triangle-based cooperative correction mechanism is introduced to mitigate the accumulation of local biases, thereby significantly enhancing estimation accuracy. Building upon the precise localization, a composite controller is designed to achieve formation maintenance and inter-robot collision avoidance simultaneously. Extensive simulations and physical experiments demonstrate the effectiveness of the proposed approach, demonstrating high localization accuracy, robust formation control, and strong resilience under realistic conditions.