Asynchronous Distributed Collision Avoidance With Intention Consensus for Inland Autonomous Ships

Abstract

This article focuses on the problem of collaborative collision avoidance (CCAS) for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision in a synchronous communication framework. Moreover, we introduce a new approach to shape the ship’s behavior to follow the waterway traffic regulations. The conditional convergence toward a stationary solution of this algorithm is guaranteed by the theory of the alternating direction method of multipliers (ADMM). To overcome the problem of asynchronous communication between ships, we adopt a new asynchronous nonlinear ADMM (Async-NADMM) and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can guarantee a safe distance between ships in complex scenarios while following the traffic regulations. Furthermore, the asynchronous algorithm has an efficient computational time and satisfies the real-time computing requirements of ships in field experiments.