Model-Free Visual Servo Swarming of Manned-Unmanned Surface Vehicles With Visibility Maintenance and Collision Avoidance

In this paper, aiming at a fleet of manned-unmanned surface vehicles (MUSVs), a novel visual servo swarming (VSS) mechanism is deliberately established by embodying visibility maintenance, swarm aggregation, collision avoidance and velocity matching. By making full use of line-of-sight ranges and angles between neighbors, a swarm of unmanned surface vehicles (USVs) with unknown inertia masses, internal dynamics and external disturbances can cooperate with a manned surface vehicle (MSV), thereby emerging flexibly collective behaviors in GPS-denied environments. To endow MUSVs with individually flexible behaviors, the VSS mechanism renders velocity matching of USVs with the MSV executing human-intelligence intention. Meanwhile, barrier Lyapunov functions are employed to reliably maintain visibility and avoid collisions among individuals, simultaneously. Distributed neural approximators using reduced-dimension inputs are devised to estimate unknown dynamics of USVs, while residual uncertainties are thoroughly suppressed by robust adaptations, thereby contributing to a model-free VSS (MVSS) scheme. Eventually, together with projection-based adaptive laws, the MVSS-based controller ensures uniform boundedness of estimation parameters and asymptotic convergence of regulation errors. Simulation results demonstrate remarkable efficacy in terms of collective performance.