Experimental Multi-Vehicle Path Coordination under Communication Connectivity Constraints

Abstract

The main contribution of this paper is the experimental val- idation of a decentralized Receding Horizon Mixed Integer Nonlinear Programming (RH-MINLP) framework that can be used to solve the Multi-Vehicle Path Coordination (MVPC) problem. The MVPC prob- lem features path-constrained vehicles that begin their transit from a fixed starting point and move towards a goal point along fixed paths so as to avoid collisions with other robots and static obstacles. This frame- work allows to solve for time optimal velocity profiles for such robots in the presence of constraints on kinematics, dynamics, collision avoidance, and inter-robot communication connectivity. Experiments involving up to five (5) robots operating in a reasonably complex workspace are re- ported. Results demonstrate the effect of communication connectivity requirements on robot velocity profiles and the effect of sensing and ac- tuation noise on the path-following performance of the robots. Typically, the optimization improved connectivity at no appreciable cost in journey time, as measured by the time of arrival of the last-arriving robot.