A distributed control framework for motion coordination of teams of autonomous agricultural vehicles

Abstract

A distributed control framework intended to coordinate the motions of teams of autonomous agricultural vehicles operating in proximity is presented; master–slave and peer-to-peer operation modes are supported. Each vehicle has a nonlinear model predictive tracking controller, which keeps it as close as possible to the path demanded by the task, and coordinates and avoids collisions with nearby vehicles. To do this, it receives the motion trajectories of all other vehicles in its vicinity that may interfere with its own projected motion via a wireless network, and incorporates these trajectories in the computation of its own optimal control action. Each controller is supervised by a higher-level task controller that determines a limited set of the controller's parameters. Simulation experiments have shown that the minimisation of the tracking error along a finite horizon enabled the controller to track paths containing sharp turns, by applying appropriate steering well in advance of the turn. It has also been shown that the variation of specific predictive controller parameters results in a wide range of behaviours; i.e., vehicles can move in both operating modes, coordinate with nearby vehicles by altering their velocity profiles or the shapes of their paths, and avoid collisions.