A Comparison between Crisp and Fuzzy Logic in an Autonomous Driving System for Boats

The adoption of autonomous driving systems is increasingly widespread in land vehicles for private and public transportation and more standards have been defined (among which SAE and ADAS are the most complete). Although in maritime transportation automatic navigation was developed earlier with respect to similar systems developed for land vehicles, there are no standards equivalent to ADAS and SAE. Furthermore, the automation on boats is always partial and refers only to the route planning, obstacle avoiding and motion control functions. In this paper an autonomous driving system in a simulated environment for boats is presented with the attempt to help to define a standard equivalent to those mentioned above. The autonomous driving system presented takes into consideration many simulation aspects and has been developed as a single complete software solution. The design involved both classical approaches and computational intelligence techniques. We distinguish three main implementation phases relating to as many levels of control of the architecture. In this paper, we present only the simulation results related to the mid -level, giving particular attention to the tasks of boat avoidance and docks avoidance (i.e., entry and exit from ports), in which the differences between the crisp and fuzzy logic versions of the same mid-level controllers are highlighted. Results show how fuzzy controllers allow ADS to achieve a lower probability of collision and stall with the same performance as an ADS with crisp controllers. The performance indicator that has been formulated for this work is inspired by Fish Schooling Behavior, which has strong similarities with the self-driving boat problem.

Automated summary

This paper presents an autonomous driving system for boats in a simulated environment, aiming to help define a standard equivalent to those used in land vehicles. The system combines classical approaches and computational intelligence techniques, and has been tested in the mid-level control scenario. Results demonstrate that fuzzy controllers can achieve a lower probability of collision and stall, while maintaining the same performance as crisp controllers.