Autonomous Collision Avoidance of Unmanned Surface Vehicles Based on Improved A Star And Minimum Course Alteration Algorithms

The ability to avoid shorelines, reefs and moving ships or obstacles is the premise of automatic navigation. For achieving the goal, conventional A star algorithm is improved to plan route globally. The safety distance is set between planning route and obstacles. Additionally, the planning route is further optimized to remove unnecessary waypoints, and only vital waypoints are retained for conforming to navigation practice and track keeping control. Besides, minimum course alteration algorithm (MCA) is proposed to avoid moving ships or obstacles constrained by COLREGs. The proposed algorithms are validated in simulation. The results show that the algorithms are credible in two and multi ships encounter situations, even the targets ships take unexpected course alteration and the own ship could well avoid collision with shorelines, reefs and moving ships or obstacles under external disturbance.

Automated summary

The study improved the A star algorithm for automatic navigation by setting a safety distance, optimizing the planned route, and proposing the MCA algorithm, successfully avoiding various obstacles.