Autonomous Machinery Management for Supervisory Risk Control Using Particle Swarm Optimization

Abstract

Safe navigation for maritime autonomous surface ships (MASS) is a challenging task, and generally highly dependent on effective collaboration between multiple sub-systems in environments with various levels of uncertainty. This paper presents a novel methodology combining risk-based optimal control and path following with autonomous machinery management (AMM) for MASS navigation and supervisory risk control. Specifically, a risk-aware particle swarm optimization (PSO) scheme utilizes “time-to-grounding” predictions based on weather data and electronic navigational charts (ENC) to simultaneously control both the ship’s motion as well as the machinery system operation (MSO) mode during transit. The proposed autonomous navigation system (ANS) is comprised of an online receding horizon control that uses a PSO approach from previous works, which produces a dynamic risk-aware path with respect to grounding obstacles from a pre-planned MASS path, subsequently given as the input to a line-of-sight guidance controller for path following. Moreover, the MSO mode of the AMM system is simultaneously selected and assigned to explicit segments along the risk-aware path throughout the receding horizon, which effectively introduces into the optimization scheme an additional safety layer as well as another dimension for risk or resource minimization. The performance of the resulting ANS is demonstrated and verified through simulations of a challenging scenario and human assessment of the generated paths. The results show that the optimized paths are more efficient and in line with how human navigators would maneuver a ship close to nearby grounding obstacles, compared to the optimized paths of selected previous works.

Keywords

• autonomous navigation systems
• autonomous ships
• autonomous surface vessels
• decision-making
• dynamic programming
• electronic navigational charts