Online distributed motion planning for multi-vehicle systems

This work presents an online distributed motion planning strategy for cooperating vehicles. The motion planning is formulated as an optimization problem that returns smooth trajectories. These are parameterized as splines, which allows a representation with a limited number of variables and enables guaranteed constraint satisfaction with a finite set of constraints. The computations for solving the problem are distributed among the agents by using the Alternating Direction Method of Multipliers (ADMM). In order to cope with a dynamic environment and disturbances, the algorithm is formulated in a receding horizon fashion, such that the future part of a motion trajectory is reoptimized iteratively. The required update time and the amount of inter-agent communication are reduced by performing only one ADMM iteration per update. In this way the method converges over the subsequent path updates. Simulations with a formation of holonomic vehicles in a dynamic environment demonstrate the capability of the proposed approach to generate optimal trajectories at an update rate of 20 Hz.