Communication in distributed model predictive collision avoidance

Abstract

This paper presents a model predictive approach for collision avoidance of car-like robots. An optimal problem is formulated in terms of cost minimization under constraints. Information on each robot can be incorporated online in the nonlinear model predictive framework and kinematic constraints are treated by Karush-Kuhn-Tucker(KKT) condition. For distributed collision avoidance of multiple robots with two levels of a communication network, performances are compared. In comparison with different types of communication, how much information the robots share can cause difference in the performance. More successful collision avoidance was possible when the robots share enough amount of information.