Asynchronous distributed collision avoidance with intention consensus for inland autonomous ships

Abstract

This paper focuses on the problem of collaborative collision avoidance 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 and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can prevent ship collisions even in complex scenarios.

References in corpus (5)

• On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
• Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
• Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results
• Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
• Hybrid Collision Avoidance for ASVs Compliant with COLREGs Rules 8 and 13-17