Abstract:

Formation control of multi-agent systems has pro- found applications in today’s technological scene, ranging from satellite constellations, collaborative load transportation, cooperative surveillance, and distributed aperture imaging systems. Often, these applications are needed in environments where localization is challenging or inexistent, such as indoor and underground environments or extra-planetary scenarios (such as Mars or the Moon). In this work, we propose a novel formation control scheme using image feature correspondences from widespread onboard cameras and only one range measurement between the formation leader and one of its neighbors. Then, optimal control inputs generated by a Nonlinear Model Predictive Control-based control law drive the agents toward the desired formation setting. The framework is tested both in simulation and on mobile platforms in a laboratory environment, with multiple camera types.