Experimental Multi-Vehicle Path Coordination under Communication Connectivity Constraints

The main contribution of this paper is the experimental validation 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 problem 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 framework 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 reported. Results demonstrate the effect of communication connectivity requirements on robot velocity profiles and the effect of sensing and actuation 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. 1 Problem Statement, Motivation, and Related Work The problem of Multi-Vehicle Path Coordination (MVPC) is formally defined as follows: Given a group of vehicle robots that have fixed and known paths connecting an initial and a goal location, generate time-optimal velocity profiles that satisfy kinematic, dynamic, collision avoidance, and communication connectivity constraints. While a significant body of work has been devoted to path planning for mobile robots (e.g., [9], [18]), we focus our work on the relatively untouched area of communication-centric velocity planning along predetermined routes. More often than not, one does not get the liberty of planning an arbitrary path around sparse Pramod Abichandani, Kenneth Mallory, and M. Ani Hsieh Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104 e-mail: {pva23, km374, mhsieh1} @drexel.edu