Multi-Agent Dynamic Scheduling With a Posteriori Path Tracking and Collision Avoidance Using Model Predictive Control

Abstract

This research work investigates a coordinated multi-agent path planning and tracking method. The solution of a pre-processed dynamic scheduling problem performs target assignment and provides optimal starting times and paths for each agent. Afterwards, a linear model predictive controller ensures robust and fast path tracking while preventing agents from collisions. This task is formulated as a discretized quadratic programming (QP) problem and is solved using an in-house developed semi-smooth Newton method. Numerical experiments have demonstrated the efficiency of the approach.