Robust multi-agent collision avoidance through scheduling

We propose a class of computationally efficient algorithms for conflict resolution in the presence of modeling and measurement uncertainties. Specifically, we address a scenario where a number of agents, whose dynamics are possibly nonlinear, must cross an intersection avoiding collisions. We obtain an exact solution and an approximate one with quantified error bound whose complexity scales polynomially with the number of agents.