Path planning for autonomous surface vessels based on improved artificial fish swarm algorithm: a further study

ABSTRACT

Featuring agile controllability, high-level autonomy, and powerful field operational advantages, autonomous surface vessels (ASVs) are gaining increasing attention worldwide. Central to the control of ASVs, path planning is one of the key technologies that ensure the navigation safety. However, many of the previous works to solve the path planning problem are to find a collision-free and shortest path, but the solutions are not satisfied by the safety requirements and the non-holonomic constraints of ASVs. This paper expands on our previous work on the improved artificial fish algorithm (IAFSA) to address these challenges. The expanding technique is introduced to modify the grid cost, and a B-spline-based path smoother is applied to the algorithm to enhance its feasibility in cooperating with the ASV controller. Simulation experiments have been conducted to illustrate the effectiveness of the proposed method. Moreover, the new algorithm is tested in the USV control system model in a practical environment. The simulation results have demonstrated that the proposed method is more efficient than the other state-of-the-art algorithms, and the simulation with the ASV model illustrated its excellent performance cooperating with the control system. Therefore, the proposed method can be considered as a reliable algorithm to solve the path planning problem of ASVs.

KEYWORDS:

• Path planning
• artificial fish swarm algorithm
• unmanned surface vehicle
• Optimization
• Collision avoidance
• USV

Acknowledgements

The authors would like to thank the Editor-in-Chief, the Associate Editor, and the anonymous referees for their comments and suggestions. This work is supported by the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology [grant number JCKYS2022SXJQR-01] and the College of Civil Engineering and Architecture, Zhejiang University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology [grant number JCKYS2022SXJQR-01].