Data-Driven Displacement-Based Formation Control With Range-Only Measurements

This paper proposes an algorithm to coordinate a displacement-based formation of a multi-agent system when their local frames are not aligned. Agents can measure neither the relative positions nor orientations, but only the range (distance) to their neighbors. We demonstrate that persistently exciting data exchanged between two agents can be used to infer relative positions and orientations. In the proposed method, we address discrete-time distributed orientation estimation based on an averaging consensus protocol. An asymptotic stability analysis of the desired formation shape is presented and the proposed strategy is validated through simulation results.